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Energy Storage
Systems
V. K. Mathew
Tapano Kumar Hotta
Hafiz Muhammad Ali
Senthilarasu Sundaram Editors
Optimization and Applications
Engineering Optimization: Methods and Applications

Engineering Optimization: Methods and
Applications
Series Editors
Anand J. Kulkarni, Department of Mechanical Engineering, Symbiosis Institute of
Technology, Pune, Maharashtra, India
Amir H. Gandomi, Engineering & Information Technology, University of
Technology Sydney, Sydney, NSW, Australia
Seyedali Mirjalili, Brisbane, QLD, Australia
Nikos D. Lagaros, National Technical University of Athens, Athens, Greece
Warren Liao, LSU, Construction Management Department, Baton Rogue,
LA, USA

Optimization carries great significance in both human affairs and the laws of nature.
It refers to a positive and intrinsically human concept of minimization or maxi-
mization to achieve the best or most favorable outcome from a given situation.
Besides, as the resources are becoming scarce there is a need to develop methods
and techniques which will make the systems extract maximum from minimum use
of these resources, i.e. maximum utilization of available resources with minimum
investment or cost of any kind. The resources could be any, such as land, mate-
rials, machines, personnel, skills, time, etc. The disciplines such as mechanical,
civil, electrical, chemical, computer engineering as well as the interdisciplinary
streams such as automobile, structural, biomedical, industrial, environmental engi-
neering, etc. involve in applying scientific approaches and techniques in designing
and developing efficient systems to get the optimum and desired output. The multi-
faceted processes involved are designing, manufacturing, operations, inspection and
testing, forecasting, scheduling, costing, networking, reliability enhancement, etc.
There are several deterministic and approximation-based optimization methods that
have been developed by the researchers, such as branch-and-bound techniques,
simplex methods, approximation and Artificial Intelligence-based methods such
as evolutionary methods, Swarm-based methods, physics-based methods, socio-
inspired methods, etc. The associated examples are Genetic Algorithms, Differen-
tial Evolution, Ant Colony Optimization, Particle Swarm Optimization, Artificial
Bee Colony, Grey Wolf Optimizer, Political Optimizer, Cohort Intelligence, League
Championship Algorithm, etc. These techniques have certain advantages and limi-
tations and their performance significantly varies when dealing with a certain class
of problems including continuous, discrete, and combinatorial domains, hard and
soft constrained problems, problems with static and dynamic in nature, optimal
control, and different types of linear and nonlinear problems, etc. There are several
problem-specific heuristic methods are also existing in the literature.
This series aims to provide a platform for a broad discussion on the devel-
opment of novel optimization methods, modifications over the existing methods
including hybridization of the existing methods as well as applying existing opti-
mization methods for solving a variety of problems from engineering streams.
This series publishes authored and edited books, monographs, and textbooks. The
series will serve as an authoritative source for a broad audience of individuals
involved in research and product development and will be of value to researchers and
advanced undergraduate and graduate students in engineering optimization methods
and associated applications.

V. K. Mathew · Tapano Kumar Hotta ·
Hafiz Muhammad Ali · Senthilarasu Sundaram
Editors
Energy Storage Systems
Optimization and Applications

Editors
V. K. Mathew
Department of Mechanical Engineering,
MIT School of Engineering
MIT-ADT University
Pune, Maharashtra, India
Hafiz Muhammad Ali
Department of Mechanical Engineering
Interdisciplinary Research Center for
Renewable Energy and Power Systems
(IRC-REPS)
King Fahd University of Petroleum
and Minerals
Dhahran, Saudi Arabia
Tapano Kumar Hotta
School of Mechanical Engineering
Vellore Institute of Technology
Vellore, India
Senthilarasu Sundaram
School of Engineering and the Built
Environment
Edinburgh Napier University
Edinburgh, UK
ISSN 2731-4049 ISSN 2731-4057 (electronic)
Engineering Optimization: Methods and Applications
ISBN 978-981-19-4501-4 ISBN 978-981-19-4502-1 (eBook)
https://doi.org/10.1007/978-981-19-4502-1
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Singapore Pte Ltd. 2023
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse
of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and
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claims in published maps and institutional affiliations.
This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.
The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,
Singapore

Contents
1 Artificial Intelligence Based Integrated Renewable Energy
Management in Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Avinash Kaldate, Amarsingh Kanase-Patil,
and Shashikant Lokhande
2 The Role of Lower Thermal Conductive Refractory Material
in Energy Management Application of Heat Treatment
Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Akshay Deshmukh, Virendra Talele, and Archana Chandak
3 Thermal Energy Storage Methods and Materials . . . . . . . . . . . . . . . . . 39
Santosh Chavan
4 Heat Flow Management in Portable Electronic Devices . . . . . . . . . . . 63
Sagar Mane Deshmukh and Virendra Bhojwani
5 A Review on Phase Change Material–metal Foam
Combinations for Li-Ion Battery Thermal Management
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
S. Babu Sanker and Rajesh Baby
6 Performance Enhancement of Thermal Energy Storage
Systems Using Nanofluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Vednath P. Kalbande, Pramod V. Walke, Kishor Rambhad,
Man Mohan, and Abhishek Sharma
7 Inoculum Ratio Optimization in Anaerobic Digestion of Food
Waste for Methane Gas Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Parag K. Talukdar, Varsha Karnani, and Palash Saikia
8 Nano-Mixed Phase Change Material for Solar Cooker
Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
C. V. Papade and A. B. Kanase-Patil
v

vi Contents
9 Technical Review on Battery Thermal Management System
for Electric Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Virendra Talele, Pranav Thorat, Yashodhan Pramod Gokhale,
and Hemalatha Desai
10 Battery Thermal Management System for EVs: A Review . . . . . . . . . 227
Amit Jomde, Prashant Patane, Anand Nadgire, Chetan Patil,
Kshitij Kolas, and Virendra Bhojwani
11 Design and Development of a Water-Cooled Proton Exchange
Membrane Fuel Cell Stack for Domestic Applications . . . . . . . . . . . . 249
Justin Jose, Rincemon Reji, and Rajesh Baby
12 Analysis of Combustion and Performance Characteristics
of a Producer Gas-Biodiesel Operated Dual Fuel Engine . . . . . . . . . . 267
Pradipta Kumar Dash, Shakti Prakash Jena,
and Harish Chandra Das
13 Influence of Biogas Up-Gradation on Exhaust Emissions
of a Dual-Fuel Engine with Thermal Barrier Coating . . . . . . . . . . . . . 279
Sanjaya Kumar Mishra, Pradipta Kumar Dash,
Shakti Prakash Jena, and Premananda Pradhan
14 Predicting the Performance Enhancement of Proton Exchange
Membrane Fuel Cell at Various Operating Conditions
by Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Tino Joe Tenson and Rajesh Baby
15 Role of Phase Change Material Thermal Conductivity
on Predicting Battery Thermal Effectiveness for Electric
Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
Virendra Talele, Pranav Thorat, Yashodhan Pramod Gokhale,
Archana Chandak, and V. K. Mathew
16 Thermal Design and Numerical Investigation of Cold
Plate for Active Water Cooling for High-Energy Density
Lithium-Ion Battery Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
Virendra Talele, Rushikesh Kore, Hemalatha Desai,
Archana Chandak, Hemant Sangwan, Gaurav Bhale,
Amit Bhirud, Saurabh Pathrikar, Anurag Nema, and Naveen G. Patil
17 AnEffectiveReductionofExhaustEmissionsfromCombustive
Gases by Providing a Magnetic Field Through the Fuel
Supply Line: SI Engine, CI Engine, and LPG Gas Stove . . . . . . . . . . 365
Rakesh Kumar Sidheshware, S. Ganesan, and Virendra Bhojwani

Contents vii
18 Thermo-Hydraulic Performance of High Heat Flux Electronic
Chip Cooling Through Microchannel Heat Sinks with Fins
on Base Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
Vasujeet Singh, Pruthiviraj Nemalipuri, Harish Chandra Das,
Vivek Vitankar, Malay Kumar Pradhan, Asita Kumar Rath,
and Swaroop Jena
19 Review on Characteristic Features of Jet Impingement
that Favours Its Application in Solar Air Heaters . . . . . . . . . . . . . . . . 415
M. Harikrishnan, R. Ajith Kumar, and Rajesh Baby
20 Thermal Management of Electronics Systems—Current
Trends and Future Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
Ganesan Dhanushkodi
21 Carbon Dioxide Storage and Its Energy Transformation
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
Manoj S. Choudhari, Vinod Kumar Sharma, Mukesh Thakur,
Sanjay Gupta, and Shajiullah Naveed Syed

Editors and Contributors
About the Editors
V. K. Mathew completed his Ph.D. in Mechanical Engineering at Vellore Insti-
tute of Technology, Vellore, India. He has published several articles in Scopus and
SCI-listed Journals. His areas of research interest are thermal management systems,
computational fluid dynamics, heat transfer and numerical methods, battery cooling
systems, hybrid electric, vehicle, nonconventional energy, artificial intelligence, and
machine learning.
Tapano Kumar Hotta is currently working as Associate Professor in the School
of Mechanical Engineering, VIT Vellore. He has pursued his Ph.D. in Mechanical
Engineering from IIT Madras in the area of Electronic Cooling. He has around 15
years of academic and research experience in different institutes of repute. His areas
of research are in a broad sense include active and passive cooling of electronic
devices, heat transfer enhancement, optimization of thermal systems, etc. He has
around 40 publications to his credit in journals and conferences of international
repute. He has guided more than 30 UG students and a dozen PG students in their
project work. Two students have obtained their degrees leading to a Ph.D. under
his guidance in the field of heat transfer enhancement. He has filed a patent on
“Innovative Design of PCM Based Cascade Heat Sinks Integrated with Heat Pipes
for the Thermal Management of Electronics”. He is a member of the editorial board
and a reviewer for various international journals and conferences related to the field
of heat transfer.
Hafiz Muhammad Ali is currently working as an Associate Professor of Mechan-
ical Engineering at King Fahd University of Petroleum and Minerals, Saudi Arabia.
He received his doctoral degree in mechanical engineering from the School of Engi-
neering and Materials Science, Queen Mary, University of London, United Kingdom,
in 2011. He was a postdoc at the Water and Energy Laboratory of the University of
California at Merced, the USA in 2015–16. He is a noted faculty member having
ix

x Editors and Contributors
thermal sciences, heat transfer, and solar energy as his major areas of interest. Over
several years, he supervised numerous undergraduate and postgraduate students,
and his work produced more than 260 papers featured in various reputed interna-
tional journals with citations over 11,000 and H-Index of 56. He is the recipient of
highly cited research (HCR) award 2021 by Clarivate Analytics. He also represented
his institution and country at several international and national conferences as an
invited and keynote speaker. His other research interests include electronics cooling,
condensation, nanofluids, heat transfer devices, and thermal management.
Senthilarasu Sundaram is currently working as Senior Lecturer in the Department
of Renewable Energy at the Environmental and Sustainability Institute (ESI), Univer-
sity of Exeter, United Kingdom. He has a total of 18 years’ research experience in
solar energy, material, and system. His research focus is on third-generation photo-
voltaics involving different technologies, as well as on the applications of nanostruc-
tured oxide materials and developing flexible solar cells on metal and polymer foils.
In addition, he is concentrating on fundamental scientific studies of new materials,
thin films, and low-cost device concepts. He is also a member of the renewable energy
department in the College of Engineering, Mathematics, and Physical Sciences. He
has over 127 journal article publications.
Contributors
Ajith Kumar R. Department of Mechanical Engineering, Amrita Vishwa
Vidyapeetham, Amritapuri, India
Babu Sanker S. Department of Mechanical Engineering, St. Joseph’s College of
Engineering and Technology, Palai, Kerala, India
Baby Rajesh Department of Mechanical Engineering, St. Joseph’s College of
Engineering and Technology, Palai, Kottayam, Kerala, India
Bhale Gaurav Department of Mechanical Engineering, MIT School of Engi-
neering, MIT ADT University, Pune, Maharashtra, India
Bhirud Amit Department of Mechanical Engineering, MIT School of Engineering,
MIT ADT University, Pune, Maharashtra, India
Bhojwani Virendra Department of Mechanical Engineering, MIT ADT Univer-
sity, Loni, Pune, India;
Department of Mechanical Engineering, MIT School of Engineering, MIT-ADT
University, Pune, Maharashtra, India
Chandak Archana Department of Mechanical Engineering, MIT School of Engi-

Editors and Contributors xi
Chavan Santosh Department of Mechanical Engineering, Bule Hora University,
Bule Hora, Ethiopia
Choudhari Manoj S. Department of Mechanical Engineering, RCET, Bhilai,
Chhattisgarh, India
Das Harish Chandra Department of Mechanical Engineering, NIT Meghalaya,
Shillong, India
Dash Pradipta Kumar Department of Mechanical Engineering, SOA Deemed to
be University, Bhubaneswar, India
Desai Hemalatha Mechanical and Aerospace Engineering Department, University
of California, Los Angeles, CA, USA
Deshmukh Akshay School of Physics, Engineering, and Computer Science,
University of Hertfordshire, Hatfield, UK
Deshmukh Sagar Mane Department of Mechanical Engineering, Tolani Maritime
Institute, Induri, Pune, India
Dhanushkodi Ganesan Centre for Electromagnetics, SAMEER, Chennai, India
Ganesan S. Mechanical Engineering Department, Sathyabama Institute of Science
and Technology, Chennai, Tamil Nadu, India
Gokhale Yashodhan Pramod Institute for Mechanical Process Engineering, Otto-
Von-Guericke University Magdeburg, Magdeburg, Germany
Gupta Sanjay School of Mechanical Engineering, Vellore Institute of Technology,
Vellore, Tamil Nadu, India
Harikrishnan M. Department of Mechanical Engineering, Amrita Vishwa
Vidyapeetham, Amritapuri, India
Jena Shakti Prakash Department of Mechanical Engineering, SOA Deemed to be
University, Bhubaneswar, India
Jena Swaroop Directorate of Factories and Boilers, Government of Odisha,
Bhubaneswar, India
Jomde Amit Dr. Vishwanath Karad, MIT World Peace University, Pune, India
Jose Justin Department of Mechanical Engineering, St. Joseph’s College of Engi-
neering and Technology, Palai, Kottayam, Kerala, India
Kalbande Vednath P. Department of Mechanical Engineering, G H Raisoni
College of Engineering, Nagpur, India
Kaldate Avinash Department of Mechanical Engineering, Sinhgad College of
Engineering, Savitribai Phule Pune University, Pune, India
Kanase-Patil A. B. Department of Mechanical Engineering, Sinhgad College of
Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India

xii Editors and Contributors
Kanase-Patil Amarsingh Department of Mechanical Engineering, Sinhgad
College of Engineering, Savitribai Phule Pune University, Pune, India
Karnani Varsha Department of Mechanical Engineering, Jorhat Engineering
College, Jorhat, Assam, India
Kolas Kshitij Fraunhofer ENAS, Chemnitz, Germany
Kore Rushikesh Department of Mechanical Engineering, MIT School of Engi-
Lokhande Shashikant Department of Electronics and Telecommunication Engi-
neering, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune,
India
Mathew V. K. Department of Mechanical Engineering, MIT School of Engi-
Mishra Sanjaya Kumar Department of Mechanical Engineering, SOA Deemed to
Mohan Man Department of Mechanical Engineering, Rungta College of Engi-
neering and Technology, Bhilai, India
Nadgire Anand Dr. Vishwanath Karad, MIT World Peace University, Pune, India
Nema Anurag Department of Mechanical Engineering, MIT School of Engi-
Nemalipuri Pruthiviraj Department of Mechanical Engineering, NIT Meghalaya,
Shillong, India
Papade C. V. Department of Mechanical Engineering, N. K. Orchid. College of
Engineering and Technology, Dr. DBATU University, Solapur, Maharashtra, India;
Department of Mechanical Engineering, Sinhgad College of Engineering, Savitribai
Phule Pune University, Pune, Maharashtra, India
Patane Prashant Dr. Vishwanath Karad, MIT World Peace University, Pune, India
Pathrikar Saurabh Department of Mechanical Engineering, MIT School of Engi-
Patil Chetan Dr. Vishwanath Karad, MIT World Peace University, Pune, India
Patil Naveen G. School of Engineering, Ajeenkya DY Patil University, Lohegaon,
Pune, India
Pradhan Malay Kumar Government of Odisha, OSDMA, Bhubaneswar, India
Pradhan Premananda Department of Mechanical Engineering, SOA Deemed to
Rambhad Kishor Department of Mechanical Engineering, St. John College of
Engineering and Management, Palghar, India

Editors and Contributors xiii
Rath Asita Kumar Department of Mechanical Engineering, Raajdhani Engi-
neering College, Bhubaneswar, Odisha, India
Reji Rincemon Department of Mechanical Engineering, St. Joseph’s College of
Engineering and Technology, Palai, Kottayam, Kerala, India
Saikia Palash Department of Mechanical Engineering, Jorhat Engineering College,
Jorhat, Assam, India
Sangwan Hemant Department of Mechanical Engineering, MIT School of Engi-
Sharma Abhishek Department of Mechanical Engineering, Manipal University
Jaipur, Jaipur, India
Sharma Vinod Kumar Mechanical EngineeringDepartment, National Institute of
Technology Calicut, Kerala, India
Sidheshware Rakesh Kumar Sathyabama Institute of Science and Technology,
Chennai, Tamil Nadu, India
Singh Vasujeet Department of Mechanical Engineering, NIT Meghalaya, Shillong,
India
Syed Shajiullah Naveed School of Mechanical Engineering, Vellore Institute of
Technology, Vellore, Tamil Nadu, India
Talele Virendra Department of Mechanical Engineering, MIT School of Engi-
Talukdar Parag K. Department of Mechanical Engineering, Jorhat Engineering
College, Jorhat, Assam, India
Tenson Tino Joe Department. of Mechanical Engineering, Providence College of
Engineering, Chengannur, Kerala, India
Thakur Mukesh NMDC DAV Polytechnic Dantewada, Shri Atal Bihari Vajpayee
Education City Jawanga, Dantewada, Chhattisgarh, India
Thorat Pranav Department of Mechanical Engineering, MIT School of Engi-
Vitankar Vivek NIT Meghalaya, Shillong, India;
FluiDimensions, Pune, India
Walke Pramod V. Department of Mechanical Engineering, G H Raisoni College
of Engineering, Nagpur, India

Chapter 1
Artificial Intelligence Based Integrated
Renewable Energy Management
in Smart City
Avinash Kaldate, Amarsingh Kanase-Patil, and Shashikant Lokhande
1.1 Introduction
Problems related to the integration of AI technology into smart energy systems need
toprovideamultifacetedunderstandingofeconomicandsocialissuesusingsoftware.
This type of socio-technological integration requires a clear definition of the domain
of energy management in which the problem exists. (Kanase-Patil et al. 2011a). As
the energy sector becomes more complex in various sectors, effective mechanisms
are needed to successfully manage the available systems and make the right decisions
at the right time. Artificial Neural Networks (ANN), Genetic Algorithms (GA), Ant
Colony Algorithm, Hill Climbing Algorithm, and Particle Swarm Algorithm have
been used in AI technology to solve problems of classification, optimization, fore-
casting, and control strategy (Javed et al. 2012). Many Integrated Renewable Energy
Sources (IRES) system operations are executed at a fundamental level of automa-
tion due to lack of information on automated control resources (Kanase-Patil et al.
2011b). It would be beneficial to use AI in the system to give a new direction to
the IRES design and power grid control. Optimization of controllable loads through
AI techniques reduces the effect in the form of cost. The AI algorithm should be
systematically used for the management of IRES to optimize to satisfy controllable
loads. AI approaches give numerous effective and strong solutions to address the
constraints of traditional optimization and control methods by utilizing existing data
(Vinay and Mathews 2014).
A. Kaldate · A. Kanase-Patil (B)
Department of Mechanical Engineering, Sinhgad College of Engineering, Savitribai Phule Pune
University, Pune 411041, India
S. Lokhande
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering,
Savitribai Phule Pune University, Pune 411041, India
e-mail: sdlokhande.scoe@sinhgad.edu
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
V. K. Mathew et al. (eds.), Energy Storage Systems, Engineering Optimization: Methods
and Applications, https://doi.org/10.1007/978-981-19-4502-1_1
1

2 A. Kaldate et al.
This chapter reviews current advances and challenges for the use of IRES in smart
cities, explaining the role of the use of AI in future energy generation in smart city
energy management. The use of AI-managed solutions will transform the energy
sector in the future. Electrical grids in the smart city are also controlled by the use of
AI, which allows them to use energy and interact bilaterally between the customer
and the energy supplier. The use of smart grids with AI responds well to rapid energy
demand changes in emergencies (Qamar and Khosravi 2015). The management of
smart meters and sensors is important in this; AI collects, analyzes, and optimizes the
information obtained from them. In order to manage energy through AI in cities, it is
necessary to first get acquainted with the essential concepts and principles of energy
management, as well as study how to integrate them in the context of smart cities.
Energy management depends on various levels such as directing, systematic supply
of energy, controlling energy consumption, increasing productivity, and reducing
energy costs through efficient use of energy (Ibrahim et al. 2011).
Energy management in the city is about energy saving, monitoring, control-
ling, and conserving energy. These include optimized energy consumption, proper
management of energy resources, and increased active energy efficiency. As urban
areas expand rapidly, the challenge is to make the most of the energy available in
the city. The rapid development of existing cities and their transformation into smart
cities has made energy management an integral part of urban transformation. Proper
adaptation of the resources available in the smart city will give a high quality of life to
the residents. Smart use of IRES will make cities more autonomous but it is expected
to be managed more effectively using AI algorithms. Smart energy management
is an integral part of smart city development that will be used to optimize energy
conservation (Rozali et al. 2015). Smart City Energy Management integrates multiple
domains through a combination of technology with information and communication
technology and ensures the sustainability of its solution (Shum and Watanabe 2009).
This chapter examines the basic structure of the AI algorithms. Moreover, each algo-
rithm case study is studied which will be useful for understanding the use of AI in
IRES field. Figure 1.1 shows dimensions of energy management. The mainstream of
the city’s energy management mainly includes proper planning of energy systems,
energy awareness, proper training in energy use and measures to be taken for energy
conservation.
1.2 Smart City
Creating a smart city means integrating existing buildings and infrastructure with
smart technology. In certain cities, for example, if the Internet of Things (IoT)-based
infrastructure and public services are created and managed, it is able to become a
smart city (Zekic-Susac et al. 2020). It will have the same facilities as before and
will have only smart technology. Another example is that the city is made smart by
using a variety of electronic sensors to collect urban area data and by connecting
it to the Internet or software, then this collected data is used for energy fulfillment

1 Artificial Intelligence Based Integrated Renewable Energy Management … 3
Maximum and
optimum ener-
gy utilization
Energy Manage-
ment
Planning of
Energy Re-
sources
Energy
Efficiency
Technology
Use for Energy
Management
Fig. 1.1 Dimensions of energy management
of a city. Smart technology is used to solve various challenges in the city. Data
collected in smart cities is used to control a variety of things, including traffic flow,
waste collection, smart energy use, and distribution automation. Many authors and
organizations have contributed to the development of the smart city concept (Menon
2017).
The problems of energy management and waste management in the smart city are
rapidly increasing due to the growing population. These problems are mitigated by
systematic planning and optimization, which requires the use of smart technology.
Energy planning is usually based on smart grids and other relevant energy factors
such as the design of the IRES system need to be considered in order to use energy.
The energy needs of modern cities are abundant, so modern cities need to improve
existing energy systems and better implement new solutions by harmonizing all these
energy solutions. The growing internal demands for renewable resources as well as
the growing need for energy in the electric transport system need to be consid-
ered in energy planning without being seen separately in the city’s energy planning.
These examples represent the challenges facing the energy sector. To better compre-
hend urban dynamics and assess the impact of various energy-policy alternatives,
simulation tools are utilized (Brenna et al. 2012).
This includes using AI to develop a complete smart city model that encompasses
all energy-related activities in order to satisfy the expanding energy demands of
present and future cities while also addressing their complexity. When looking at the
entire world, it is clear that more than half of the population now lives in cities, and
urbanization does not seem to be decreasing; By 2030, 60% of the population will
live in cities (Riffat et al. 2016). Therefore, as cities grow, it is imperative to find

4 A. Kaldate et al.
Fig. 1.2 Smart city energy
management using smart
grid
Smart
Grid
Offices
Industry
Electric
Vehical
Charging
Station
Home
IRES
Power
Substation
better ways to manage this population and meet their energy needs and the services
they need. Due to this increasing urbanization, urban people consume two-thirds
of the world’s total energy and therefore global carbon emissions are increasing
at an extremely high rate. Therefore, it is necessary to find more renewable energy
sources than before and use existing renewable energy more efficiently. Proper energy
management requires smart city data collection and digital connectivity of the city
and the use of AI technology to properly analyze the information received. For this, it
is necessary to consider the concept of smart energy in the city (Qamar and Khosravi
2015). Figure 1.2 shows how it is possible to connect various establishments in the
city using smart grids to solve energy-related problems.
1.3 Energy Management
Energy management in a smart city is the process of saving in building energy
usage and optimizing the energy system with the information of energy consumption
obtainedandknowingtheenergycostfromit.Oneofthefewstepsforenergymanage-
ment is to continuously collect information and analyze the information obtained. AI
algorithms are often used to calculate the return on investment in IRES. Energy opti-
mizationsolutionsbasedonAIhavebeenimplementedinmanyplaces.Properenergy
management regulates the energy consumption of a building and seeks to reduce the
cost of resources involved in energy generation. Using IRES helps reduce carbon
emissions in the city. Excessive energy consumption increases energy consumption
which leads to energy scarcity. Therefore, this risk is reduced by managing energy
and controlling it through proper energy planning. The AI system is used for energy
management to reduce energy generation costs and make optimal use of energy.
IRES is used in the size of AI to make energy-efficient, economical, and efficient
(Nge et al. 2019). Energy management involves the following things.

Fig. 1.3 Smart energy
management
Smart Energy
Management
Smart Energy
Smart
Trasportation
System
Smart
Buildings
Smart Water
Treatment
Smart Street
Light
Smart
Farming
Smart Air
quality
Monitoring
• Strategy and commitment is energy management.
• Proper planning of energy use
• Proper monitoring of energy use
• Planning for energy conservation
• Monitoring energy use
• Establishing the effectiveness of energy conservation measures.
In energy management, short-term, medium, and long-term energy supply plans
need to be implemented to ensure minimum costs and minimum pollution. Using
AI, it is possible to select and optimize the optimal energy for each type of energy
consumption in order to reduce energy costs and improve productivity, quality of
life, and the environment. It balances energy supply and demand for personal and
national interests. Energy management is the key to saving energy in the city (Wang
et al. 2015). This will reduce the damage to the entire earth. The use of IRES reduces
our dependence on fossil fuels, which needs to happen because its supply is limited
to a growing population. Figure 1.3 reviews the systems required in smart energy
management.
1.4 Integrated Renewable Energy System
Integrated renewable energy systems provide a number of advantages over traditional
energy systems, including decentralized energy generation and improved energy
security. In many regions of the world, renewable energy sources are extensively

6 A. Kaldate et al.
available. Other forms of renewable energy sources are not as widely available
as solar radiation (Kanase-Patil et al. 2010). Certain types of renewable energy,
such as geothermal and marine thermal energy, are only available in certain places
(Kanase-Patil et al. 2010). Solar, wind, hydropower, biomass, geothermal, and ocean
energy are all examples of renewable energy systems. These renewable resources
are converted into usable products using a variety of energy conversion technolo-
gies. For example, using PV cells, solar energy is converted into thermal or electrical
energy. Solar thermal systems are used to run many industrial processes that require
moderate to high temperatures. An integrated photovoltaic system is achieving great
solar-to-electrical efficiency. Wind energy is also widely available. The available
kinetic energy is converted into other useful forms, for which turbines are rotated
using wind speed. Hydropower is available in many different forms, including energy
from dams, kinetic energy from rivers, and ocean waves (Bansal et al. 2012).
Because all renewable energy sources have their own unique features, integrated
systems are utilized to combine all (Kanase-Patil et al. 2020). To integrate available
renewable energy sources, various alternative configurations are made, including
DC-connected configurations, AC-connected configurations, and hybrid-connected
configurations (Kaldate et al. 2020). In the DC configuration, there is only one DC
bus to which renewable energy sources are connected via an appropriate electrical
interfacing circuit (Ahmed et al. 2011). The DC bus is directly connected to DC power
sources. It entails loading DC from the DC bus via a DC/DC converter in order to
maintain the DC voltage level. It also uses a configuration inverter to supply power to
the AC load. It appears that when the inverter fails, the entire system will be unable
to supply energy for AC loading. The DC-connected configuration of the hydro-
wind-solar-based integrated system is shown in Fig. 1.4. The power frequency AC
and high-frequency AC connections are separated in the AC coupled configuration
integration configuration. The scheme diagram of power frequency AC (PFAC) bus
shown in Fig. 1.5 considers wind-solar-based integrated system. Electrical circuits
also connect power sources to the energy-frequency AC bus. At the same time, a
converter connects the storage system to the bus. The DC-AC paired configuration
hybrid scheme in the hybrid system has both DC and PFAC buses. PFAC power
sources are connected directly without any interfacing circuits shown in Fig. 1.5.
This eliminates the use of converters. The usage of converters is no longer necessary.
As a result, when compared to DC coupled and AC linked schemes, the hybrid DC-
AC coupled design has a lesser price and higher energy efficiency (Chauhan and
Saini 2014). Because the hybrid approach requires complicated control and energy
management, AI techniques appear to be required for optimization.
Distributed production, energy storage, thermal active technology integration, and
demand response in transmission systems are all areas where renewable energy inte-
gration is focused. AI algorithms are being utilized to overcome technological, finan-
cial, regulatory, and organizational constraints to renewable and distributed energy
systems. Planning, grid operations, and demand-side management are all integrated
with the AI algorithm. IRES assist in the reduction of carbon emissions through
the use of renewable energy and other environmentally friendly distributed energy
sources. It utilizes the available energy to meet peak loads by combining distributed

Fig. 1.4 AC/DC
hydro-wind-solar based
integrated system
PV Cell
DC Energy
Wind Turbine
Small hydro
AC Load
DC
Bus
AC
Bus
AC/
DC
Fig. 1.5 Scheme diagram of
PFAC’s planned
wind-solar-based integrated
system
Wind Tur-
bine AC
Load
PV cell DC
to AC con-
verter
AC load
DC load
Energy
Storage
PFAC Bus
systems and customer loads. Reliability, security, and flexibility are enhanced by
microgrid applications. Smart integrated renewable energy systems have the poten-
tial to overcome challenging obstacles. This helps to improve durability as well as
improve efficiency and adequacy in their energy and consumption sectors. Proper
analysis of the market value chain is done by appropriate technology and appro-
priate decisions are made regarding the structure of the market and the processing
of flexible provisions.
Financial viability is also improved by providing “demand response” information
(Bhoyar and Bharatkar 2013). Long-term planning decisions that include demand-
side flexibility resources serve as the foundation for developing new design standards
for off-grid renewable energy systems. Advanced information and communication
technology presents opportunities in addition to promoting smart integrated renew-
able energy systems as an active community resource for active customers to support
grid services. Smart integrated renewable energy systems are a viable solution to
energy issues (Kaygusuz et al. 2013).

8 A. Kaldate et al.
1.5 Artificial Intelligence (AI)
New advances are being made in the areas of computer vision, machine learning, and
deep learning, now the AI utility has added a new dimension to it. The functionality
of AI is huge. AI is equally efficient in retrieving and analyzing data from data
sources. AI analyzes the information obtained and identifies various sets of samples
and makes appropriate recommendations and estimates based on the analyzed data
(Saraiva et al. 2015). AI provides insight into the machine and therefore helps to fix
it accurately, independently, and well for applications without human intervention.
More strategies are being developed for the use of AI, as well as the importance of
IRES in global energy use. In this, AI provides a good opportunity for proper energy
management and meeting demand and supply in the design of IRES. In this global
utility sector, the system based on efficient power generation AI is able to meet the
high demand for electricity from the customers (Qamar and Khosravi 2015).
AI capabilities are used by energy companies and grid operators to increase renew-
able energy use and increase energy efficiency. IoT Connected AI technology helps
to improve the management of the grid for renewable energy generation to balance
demand and supply. It helps manage energy in AI malls, hotels, and many other
sector services to improve the production and supply of renewable energy (Varshney
et al. 2008). AI is opening up a new opportunity to connect different decentralized
energy sources and make them the right size. AI capabilities are being used to opti-
mize IRES usage. When using energy systems, AI capabilities with a combination of
machinelearninganddeeplearningalgorithmseasilybringinsightsintotheoperation
of energy operations. The AI algorithm analyzes the data and suggests a proactive
approach to IRES energy management while helping to save on unnecessary energy
consumption costs.
The AI algorithm offers a customized solution that works with the synchronization
of the IRES system. AI is also applied in energy production or storage. It is possible
to analyze the data obtained from there together and help IRES to run efficiently.
This includes helping to manage energy purchases at a lower cost by optimizing
AI. Using AI supports managing power demand and balancing the grid. For hybrid
energy system optimization, AI has algorithms such as Genetic Algorithm (GA),
Particle Swarm Optimization (PSO), Simulated Annealing (SA), Artificial Neural
Network (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), and Particle
Swarm Optimization (PSO) related to various optimization techniques (Qamar and
Khosravi 2015). In addition, algorithms are used to help researchers for cost-effective
solutions of IRES. AI algorithms have been studied in IRES considering various case
studies. Figure 1.6 shows that classification of AI algorithms.

Fig. 1.6 Classification of AI
algorithm
Artificial
Intelligence
Machine
Learning
K-means Regression
SVM
Natural
Inspired
PSO
Artificial Bee
Colony
Ant Colony
Optimization
Genetic
Algorithm
Simulated
Annealing
ANN
Feed Forword
ANN
Convolutional
ANN
1.5.1 Genetic Algorithm (GA)
GA is driven by evolution’s ability to adapt to the challenges of living in difficult
conditions. The method helps population evolution by identifying the most suit-
able individuals for reproduction. GA relies on the natural selection process and
the concept of the existence of fitness. It operates with a fixed size population of
possible solutions to the problem. There are three stages in GA: selection, crossover,
and mutation (Strasser et al. 2015). The idea of a genetic algorithm is based on
Darwin’s theory. In which strong individuals in the population are more likely to
produce offspring. Genetic Algorithms are used to perform optimization processes
that contain the principles of natural heredity and natural selection. This is done
to change the chosen solution and help the next generation choose the most suit-
able offspring to carry on. The GA algorithm considers multiple problems at once
and provides fast circulation for the best possible solution to the problem. Genetic
Algorithms are also used in conjunction with other technologies, including neural
networks, expert systems, and case-based reasoning. The solution is reached through
a multipurpose optimization method to achieve optimal solution in IRES. Figure 1.7
explains flow chart of Genetic Algorithm.
Case Study Based on Genetic Algorithm for Charge Regulation in IRES
IRES combines renewable energy sources and lead batteries using genetic algorithms
to reduce Net Present Cost (NPC). Mathematical equations should be considered to
combine PV, wind, and hydro systems (Homer software mathematical model 2021).

10 A. Kaldate et al.
Fig. 1.7 Genetic algorithm
flow chart Start
Initial Population
Evaluate Individuals
Selecting Reproduction
Crossover Mutation
Mutation
Best Individuals
Results
PV power
Ppv = ηpv × Npvp × Npvs × Vpv × Ipv (1.1)
where,Ppv solar energy output on an hourly basis of PV array ηpv PV module conver-
sion efficiency, Npvp and Npvs s the number of solar cells coupled in parallel and
series, Vpv operating voltage, Ipv operating current.
Wind Power
Pw = 0.5 × ηw × ηg × ρa × Cp × A × V 3
r (1.2)
where ρa air density, A the area of the windmill which is perpendicular to the wind, V
wind speed, Cp Power Coefficient, ηw and ηg transmission efficiency and generator
efficiency.
Hydro Power System
Ph = ηh × ρwater × g × Hnet × Q (1.3)
where ηh, ρwater , g, Hnet , Q represents efficiency, density of water, gravity, flow rate,
and head, respectively.
IRES power output
P(t) =
nh
Σ
h=1
Ph +
ne
Σ
w=1
Pw +
ns
Σ
s=1
Ps (1.4)

Battery Charging
Pb(t) = Pb(t − 1) × (1 − σ) − [Pbh(t)/ηbi − Pbl(t)]
(1.5)
where Pb(t − 1), Pb(t) the energy stored in the battery at the start and end of the
interval t,Pbl(t) at time t, the load demand, Pbh(t) PV array total energy generated,
σ the self-discharge factor, ηbi the battery efficiency.
Net present cost (NPC) for each component is derived using
CNPC =
Cann,tot
CRF
(1.6)
where Cann,tot total annualized cost, CNPC net present cost, CRF capital recovery
factor
CRF =
i × (1 + i)N
(1 + i)N − 1
(1.7)
where N is the number of years and i is the annual real discount rate.
Step to be used by genetic algorithm to calculate NPC using genetic algorithm.
• Adjusting the number of individuals in the population, number of generations,
crossing rate, and mutation rate for genetic algorithm.
• The genetic algorithm of reproduction, crossing, and mutation is used to make
the right choice for the next generation. In this the roulette-wheel method is used,
the crossing is done using a crossing point method, and the elements of some
individuals are mutated by randomly changing.
• The genetic algorithm generates randomly component size vectors for PV, wind,
hydro turbines, and batteries. A genetic algorithm is used for this selection.
• Calculate for each selected component meet load demand is found. Fund the
random generation of operation strategy vectors. It calculates operating cost for
each strategy.
• Battery charging or discharging will be decided on the additional energy available.
It calculates the lowest operating cost and NPC for the size of the IRES. Finally,
the solution with the lowest NPC is considered.
1.5.2 Particle Swarm Optimization (PSO)
There have been several studies related to the social behavior of animal groups
in the PSO. This shows that some of the animals, birds, and fish in the group share
information in their group and thus have a great benefit in the survival of the animals.
This is used in PSO nonlinear optimization and the solution to the problem is found.
To solve the complex problem, the behavior of the herd of animals has been studied

to create a PSO optimization algorithm. Finding the point at which the whole flock
should land is a complex issue, as it depends on many factors. The goal of birds is
to maximize food availability and minimize the risk of predation.
In the PSO algorithm, the same mechanism is applied. PSO is a frequently used
swarm intelligence optimization technique in which the answer to a question is deter-
mined by the speed of the particles. PSO does not require any overlap and mutation
calculations, simple calculations, and fast search speeds. PSO is a population-based
search process that uses particles to change the position of particles in a problem
area. In PSO, the search location is multidimensional, with each particle’s position
being changed based on the experience of nearby particles (Jadhav et al. 2011).
Algorithm of particle swarm optimization.
• Step 1: Entering system parameters.
• Step 2: Initialize the PSO settings.
• Step 3: The iteration is set at the beginning and then the particle population is
started rapidly at random positions and dimensions.
• Step 4: For each particle, the objective function is calculated and compared with
the individual best value. Based on this, the first best value is modified with a
higher value and the current state of the particles is reported.
• Step 5: The particles corresponding to the individual best particles in all particles
are selected and the values are set as the global best.
• Step 6: The speed and position of each particle is updated.
• Step 7: If the number of iterations reaches the maximum limit. Go to Step 8;
otherwise, set the next iteration and go to Step 4.
• Step 8: The best particle denoted by global best provides the optimal solution/or
the problem.
Energy Management System using Particle Swarm Optimization.
The power management system requires production to control the flow of elec-
tricity during grid-connected operation and to match the load in the microgrid. The
PSO algorithm has been used to reduce the cost of energy extracted from the grid,
generated in the grid, and used by loads. The mathematical models of generator
functions, solar generation function, and construct functions are given below (Gaing
2003).
Functions of Generators
Fj
(
Pj
)
= αj + βj Pj + γj P2
j (1.8)
where j = generating source; P = a source’s power output j; F = source’s operating
costs j, α, β, γ are the cost coefficients.
Function of solar generation
F(Ps) = aPs + Ge
Ps (1.9)
where, Ps solar generation, a annuitization coefficient, Ge
Operation and Mainte-
nance (O & M) costs per unit generated energy,

Functions of Constraints
Pgenerated /= PLoad (1.10)
Pgrid = Pgenerated − PLoad (1.11)
Pmin
j ≤ Pj ≤ Pmax
j (1.12)
Step to be used by Particle Swarm Optimization for energy management.
• In this case it is necessary to first provide the necessary data for the required
algorithm. In this case, the forecasted load, solar power generation and wind
power generation should be provided.
• The algorithm selects the initial parameters, including the population size.
• The algorithm will start to find the fitness evaluation of each parameter.
• The diesel generator is turned on using a microgrid. But this involves applying
the PSO algorithm to find the optimal way to send all available diesel generators
to meet the load demand while reducing operating costs.
• The termination condition is checked and if it is satisfied, the system will output
a power reference signal for each diesel generator at intervals each time. If the
termination condition is not satisfied, the system will go back again.
1.5.3 Ant Colony Optimization (ACO)
ACO is an algorithm in the class of biologically induced heuristics. The basic idea
of ACO in this algorithm is that it works in the same way that it is collaborated in
ant colonies. Dorigo first used ACO in 1992 to solve the problems of oxidation. The
ants go out to find food and return to their nests. During this journey, ants release a
chemical pathway called pheromones to the ground. Pheromones guide other ants to
food. When facing an obstacle, the ant has an equal chance to choose the left or right
path. So this pheromone is used to choose the right path. Each ant creates a complete
solution to the food search problem according to the potential state transition rules.
The whole purpose of the scheduling problem using ACO in IRES is to reduce the
electricity bill by making optimal use of electricity from the grid. Figure 1.8 shows
working flow chart of Ant Colony Algorithm (Qamar and Khosravi 2015).
Energy Management System Using Ant Colony Optimization
It uses ACO to reduce electricity bills as well as grid and waiting time by making
optimal use of schedule issues. The cost of electricity must be reduced in each time
slot, while the waiting time for shiftable equipment must be reduced. The main
concern in this work is to increase the level of convenience of end-users by reducing
the cost of electricity. The cost of electricity must be reduced in each time slot, while
the waiting time for shiftable equipment must be reduced. This work has increased

Fig. 1.8 Ant colony
algorithm flow chart Start
Path Construction by Ant
Ants meeting are calculated
Path combined
Pheromone increased
Correct Path Found
End
the level of convenience of end-users by reducing the cost of electricity (Rahim et al.
2015).
Model of energy consumption
Ea
(t) =
{
Ea
t1
+ Ea
t2
+ Ea
t3
+ · · · + Ea
t24
}
(1.13)
where, Ea
t1
, Ea
t2
, Ea
t3
. . . Ea
t24
each appliance’s energy consumption needs at the
appropriate time slot
ET =
24
Σ
t=1
( A
Σ
a=1
E(i,t)
)
(1.14)
where, ET the overall energy consumption requirement for all appliances on a daily
basis.
Model for calculating energy prices
E(t) =
24
Σ
t=1
(ν(t) + Δ(t) + κ(t)) (1.15)
where, E(t) the total amount of energy used by all appliances
C(t) =
⎧
⎪
⎨
⎪
⎩
C1(t) 0 ≤ E(t) ≤ E1
th(t)
C2(t) E1
th(t) ≤ E(t) ≤ E2
th(t)
C3(t) E2
th(t) < E(t)
(1.16)

where E1
th and E2
th thresholds for power consumption, C1C2 and C3, costs in these
specific circumstances.
Objective function and its solution via ACO
min
24
Σ
t=1
(
a1 ·
A
Σ
a=1
(Ea(t) × Ca(t))
)
+ a2(ϕa(t))
)
(1.17)
where,Ca thecostofelectricityineachtimeslotmustbekepttoaminimum,a1 and a2
weights of two parts of objective.
Step to be used by Ant Colony Optimization for energy management.
1. The algorithm initializes all parameters as well as includes data related to
equipment and time slots.
2. The algorithm randomly generates a population of ants.
3. Each individual ant update evaluates pheromones and the objective function of
each individual ant.
4. Calculates electricity bill using algorithm.
5. Each ant local pheromone is updated and then the best solution is selected.
1.5.4 Hill Climbing Optimization
Hill climbing is an approximate algorithm used for optimization problems in the field
of AI. This algorithm performs the right input and a good genetic function, giving
the algorithm the best possible solution to the problem in a short period of time. This
given satisfaction may not be the absolute best given every time but it is good enough
considering the time it takes to get the satisfaction. This algorithm lists all possible
options in the search algorithm based on the information available (Bhandari et al.
2015).Ithelpsthealgorithmtochoosetheshortestpathpossible.Theaverageincrease
in energy gain using MPPT using the Hill Climbing Algorithm has been found to
be 16–43%. To calculate the power in this algorithm, one immediately measures the
voltage (V) and the current (I) and then compares it with the last calculated power.
If the operating point difference is positive, the algorithm continuously overlaps the
system, otherwise, if the operating point difference is positive, the direction of the
object is changed. Hill Climbing (HC) is a mathematical method for optimizing a
problem that belongs to the domain of local search methods (Mhusa and Nyakoe
2015). The HC technique begins with the creation of the initial state, i.e., the initial
solution. The following steps depict the optimization process using the hill climbing
algorithm. This algorithm is used to size IRES by minimizing the Levelized cost of
energy in IRES.
The algorithm is as follows:
Step 1: Find a possible solution.
Step 2: Verify that each solution is correct.

Step 3: If each solution is correct then move on to the next step.
Step 4: Choose the most suitable solution for each of these solutions.
Hill Climbing Algorithm for MPPT
When MPPT is performed, it uses Boost Converter as a duty cycle feedback param-
eter. The main disadvantage of this technique is that the system shuts down during
the period of continuous radiation. A very small value of the difference in the duty
cycle is required for the period of stable radiation; ΔD reduces the energy gained
by the PV thus reducing the strong oscillation of the force about the peak power
point. At the same time, rapidly changing radiation requires a higher charge cycle
value to increase the pursuit of peak power. This is done by measuring the values
of PV voltage and current. Also, the generated power is calculated and the result of
the comparison is seen to be complementary or unchanged compared to its value in
the previous iteration and the PWM output duty cycle is changed accordingly (Sher
et al. 2015).
The PV module’s current output is
Impp = Ki Io
[
exp
(
Voc
nNscVT
)
− 1
]
(1.18)
where Io current in an open circuit, Ki the current proportionality constant, Voc
open-circuit voltage, Nsc series cells,
The PV module’s voltage output is
Vmpp =
VT
[
exp((Voc/VT) − 1)
]
(1 − 1/Ki)exp(Voc/VT) − 1
(1.19)
where VT is the maximum power point voltage.
Step to Be Used by Hill Climbing Optimization Algorithm MPPT of PV
• It collects data of voltage and current from PV.
• Calculate power from the from voltage and current
• The algorithm compares its value to the previously calculated power value.
• The previous value is determined more or less and the power is added or decreased
accordingly.
1.5.5 Neural Network Algorithm
Advances in biological research have made it possible to understand the process of
natural decision-making. The brain is a sophisticated parallel computer that has the
power to make decisions faster than any advanced computer. It has the ability to
learn, remember and generalize new things. The ANN algorithm was developed in

Sum-
ming
Junction
Input
Output
X1
X2
X3
Y
w
w2
w3
weight
Perceptron
Fig. 1.9 Basic neuron diagram for ANN
1943 to study this ability of the brain. Mathematical models of biological neurons
are presented in the ANN algorithm (Ranganayaki et al. 2016). The model has the
ability to calculate any logical expression. The standard weight also performs a
similar function, as do the different synaptic forces of biological neurons in ANN.
For the synaptic weight of the synaptic strength of biological neurons, some
inputs are more important than others. Therefore ensures that more significant factors
have a greater impact on the process function principle when they produce nerve
responses. ANNs have adjustable coefficients of weight in the network and determine
the strength of the input signal as indicated by the artificial neuron. Weight determines
the connection strength of the input and it is possible to train on the basis of different
training sets with respect to the specific architecture of the network or its learning
rules. The individual inputs in the perceptron are multiplied by its corresponding
connection weight. Figure 1.9 shows basic neuron diagram for ANN (Ata 2015).
1.5.6 Artificial Neural Network Approach in IRES
Power management is done on a distributor hybrid grid using ANN. The problem is
that the voltage drop and power quality can be reduced, thus the power stability is
managed using ANN. In real-time monitoring, ANN algorithms are used to perform
power management based on the quality and stability of the power system. The IRES
have solar, wind or hydro time series nonlinear and static data, using ANN algorithms
to learn from data patterns and predict future behavior of weather events is possible
(Rahman et al. 2021).
Step to be used by ANN to predict renewable resource output.
• Data is collected from energy sources and the environment
• The original data is normalized and pre-processed

• The ANN model is trained, the accuracy of the training samples is evaluated, and
the pre-trained model is certified by the verification samples.
• The designed ANN model is used to estimate the power output by the test dataset.
• The weather information available in it is matched with the information in the
history and it determines how much energy will be obtained from IRES.
1.6 Concluding Remarks
This chapter explores the use of AI algorithms in the architecture of public sector
energy management systems to increase energy efficiency, estimate energy consump-
tion, and be used as part of a smart city. It explains how the AI algorithm is used
to optimum sizing of parts of the IRES system. This chapter provides information
on Genetic Algorithms, Particle Swarm Optimization, Anti-Colony Algorithms, Hill
Climbing Algorithms, and Artificial Neural Networks. It turns out that many things
can be simplified in IRES using AI algorithms. This chapter explains the basic design
of AI algorithms and the various IRES problems solved by AI algorithms.
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Chapter 2
The Role of Lower Thermal Conductive
Refractory Material in Energy
Management Application of Heat
Treatment Furnace
Akshay Deshmukh, Virendra Talele, and Archana Chandak
2.1 Introduction
In a modern scenario of translation of technology causes more production of versa-
tile products which needs to go from the heat treatment process to increase its
working effectiveness. In this translation of the production sector where demand
had sustainably increased, the use of energy to run this heat treatment furnace is
also increased. The extensive use of power to fulfil the functional requirement of
the furnace is a typical result of an increasing number of reactive chemicals such
as CO and HC, which cause growing greenhouse gases in the atmosphere (Lisienko
et al. 2016). The cause of global warming by industrial applications is an intensive
problem, on which several national-level government bodies are working to curb
the level of emissions under control despite the strict policy no significant growth
for emission reduction is observed if companies on primary ground start to work
on energy management solution this problem can be effectively solved in upcoming
years (Källén 2012). The heat treatment of any product is an intensive process that
consumes a large amount of fuel to fulfil temperature requirement in the furnace,
the typical working temperature in heat treatment range from 900 C to 1200 °C
depending on the application and intensity of work the requirements of temperature
corresponding to the fuel is burned which causes an emission of harmful gases (Stål
och värmebehandling – En handbok 2010). Most of these greenhouse gases expelled
from the furnace contribute towards pollution and lowers efficiency. Methane, the
other GHG, is secondary resource energy (SER) and is used in metallurgical units
A. Deshmukh (B)
School of Physics, Engineering, and Computer Science, University of Hertfordshire,
Hatfield AL109AB, UK
e-mail: ad18abx@herts.ac.uk
V. Talele · A. Chandak
Department of Mechanical Engineering, MIT School of Engineering, MIT ADT University,
Pune 412201, India
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
V. K. Mathew et al. (eds.), Energy Storage Systems, Engineering Optimization: Methods
and Applications, https://doi.org/10.1007/978-981-19-4502-1_2
21

22 A. Deshmukh et al.
for burning carbon dioxide. Hyl-3 processes are used in iron and steel manufac-
turing processes, such as Corex, Romelt, and Midrex. Iron or sponge iron is loaded
into steel arc furnaces (EAF) with added scrap iron (Cvinolobov and Brovkin 2004;
Yusfin and Pashkov 2007; Romenets, et al. 2005; Voskoboynikov et al. 1998). In
metallurgical furnaces, the main source of heat is natural gas combustion. Energy
costs are an integral part of manufacturing, covering costs, and energy savings in
high-temperature processes are extremely important. The chamber furnaces are part
of a group of regular furnaces commonly used to forge and warm heavy components.
For chamber furnaces, the time change in the chamber temperature should be held
in line with technical requirements. Therefore, it is very important to save energy by
reducing the heat transfer rate by isolating the furnace walls (Rusinowski and Szega
2001). The energy loss in the furnace can be calculated by correlation with energy
balance for the burning in which the furnace’s thermal condition varies over time,
and the performance of energy consumption depends heavily on the length of the
specific process step. The energy input must equate with energy production to ensure
the oven’s continuous function and a relatively straightforward calculation of heat
loss from the walls (Chen et al. 2005). In periodic chamber furnaces, the measure
of heat loss is further complicated by the deposition of energy in the furnace walls.
The loss of energy is primarily dependent upon the temperature of the insulation,
thickness of isolation, the temperature of the furnace chamber, and the mode of oper-
ation of a furnace (Han et al. 2011). In the event of transient heat piping, calculating
the heat loss from the furnace wall entails the time change in furnace walls. Heat
loss occurs during discharge or refreshment in the furnace as thermal treatment is
carried out from the interior wall surface. Obtaining exact heat loss value in fined
tuned accurate CFD simulation which was proposed by Yang et al. (2007) where they
performed time variation transient CFD simulation of product to predict the thermal
performance, in which the model consists of turbulent flow with intensive calibration
of the system. From the various literatures (Dubey and Srinivasan 2014; Kim et al.
2000; Kim and Huh 2000; Mayr et al. 2017, 2015; Jaklič et al. 2007; Quested et al.
2009), it is observed that there is a scant amount of research performed on energy
conservation system of furnace influence by lining material, so the current study
is an attempt to perform practical validation of furnace influence by lower thermal
conductive in lining refractories material. The achieved data are correlated by using
advanced neural network technique to find a correlation between input data with
conventional refractories and the target set of pyro block refractories.
2.1.1 Heat Treatment in Furnaces
The heating is carried out in the different furnaces using different heating mediums. A
large amount of energy is required to heat the components. In the total manufacturing
cost of forgings, a major share is consumed by the energy cost. Typically, the heating
furnace is used to carry out a thermophysical operation such as.

2 The Role of Lower Thermal Conductive Refractory Material … 23
Fig. 2.1 Heat treatment operations
1. Hardening tempering
2. Normalizing
3. Iso annealing
4. Stress revealing.
Maximum consumption of heat is required in the operation of hardening and
tempering, normalizing, and iso annealing. The typical heat contribution is achieved
by burning fuel. The below chart shows the temperature range for the operation
(Fig. 2.1).
The furnace efficiency is determined by the ratio of thermal input from the furnace
to the material. All the heat applied to the furnace can be used for heating the material
or components of industrial heating furnaces. The part is heated by continuously
incorporating specific quantities of thermal energy into the product placed in the
chamber. Figure 2.2 shows a schematic of various energy losses in furnaces.
2.1.2 Refractory Material
Refractory isolation is used to decrease the heat loss rate across furnace walls. This is
duetoahighlevelofporosityandthedesiredporeconfigurationoftiny,uniformpores
that are spaced uniformly throughout the entire refractory brick to reduce thermal
conductivity. Refractory material selection is conducted based on its application
where the need for material to be chemically and physically stable in the high-
temperature application. In the present investigation, typical refractory material as
fire brick used in bogie hearth furnace will be replaced with lower thermal conductive
pyro block material on which a detailed comparative energy conservation analysis

Fig. 2.2 Furnace energy losses
is presented. The output data are verified and validated using the advanced data
analytics ANN technique based on the generic optimization process.
2.2 Methodology
The present investigation is performed to study the importance of refractory mate-
rial and its impact on the energy conservation system of heat treatment furnaces.
The practical validation is proposed in the study based on a comparative analysis
based on refractories with wall brick material, further replaced with the ceramic fibre
wall material. The flowing outline shows the performed detail of the investigation
(Fig. 2.3).
Investigating Conventional Refectories
Energy consumption analysis with respect to the Temperature and
Fuel Consumption.
Practical Investigation of Bogie hearth in comparative validation
between Firebrick wall and replaced Kaizen Ceramic Wall
Validating Results with Thermography
Establishing verification and validation using ANN Generic
Optimized Algorithm.
Fig. 2.3 Methodology flow chart

Fig. 2.4 Conventional fire
brick
2.2.1 Investigating Conventional Refractories
In Bogie Hearth furnaces, the conventional material used for the lining is made up of
firebricks, whose temperature rating of about 1649 C in preliminary life condition.
This material requires to make thermal insulation for the heat treatment furnace by
isolating generated heat inside the furnace. The typical advantage of thermal fire
brick offers effective working across wide temperature use; it has a lower level of
impurities and a lower level of shrinkage value. The disadvantage of this material
offer is that it has too many pores in structure, making it weaker with an increment
of the application life cycle. It does not provide soundproof coating; it is having
lower thermal resistance to the thermal properties. In the present investigation, the
BoogieHearthfurnacewas initiallyloadedwithconventional refractorybrick, further
replaced with lower conductive ceramic brick classified as a pyro block. The below
table shows the comparative properties between conventional fire brick and replaced
pyro block. The mathematical modelling of thermally insulated refractories is shown
in Figs. 2.4 and 2.5.
Table 2.1 represents the properties of both fire brick and ceramic fibre (pyro block)
2.2.2 Development Scope for Existing Boogie Furnace
Inthedesigningoperationoffurnaces,itisparticularlybeneficialtoutilizetheheating
value of fuel as economically as practicable in the design and operation of furnaces.
Inevitably, though, some of this heat is lost to the environment because of
1. Incomplete combustion of fuel
2. Flue gas sensible heat
3. Convection and radiation from the furnace wall.
Below is the key furnace classified resulting in heat loss.

Fig. 2.5 Ceramic fibre (pyro
block)
Table 2.1 Refractory
properties
Parameter Firebrick Pyro block
Density < 2300 160–240
Chemical composition Up to 1648 °C 1260 °C
Al2O3 <44% 44–50%
SiO2 <78% 50–56%
Thermal conductivity 1.2 < 0.340
2.2.2.1 Burner
The burner should burn its fuel efficiency by maintaining an adequate fuel-air ratio
in circulation mode. If this condition is not getting satisfied, there is a creation of
instability in the burner’s flame. Multiple burners are used to sustain flame and
achieve heat inside the furnace to keep the stability of flame creation.
2.2.2.2 Furnace
In several applications, firebricks have been phased out and replaced by cast plastic
refractories. Utilize the optimal insulation width. Reduce air and flue gas leakage
by improved furnace design. Increase the vertical depth of the furnace to allow for
increased heat transfer by radiation. Consider the likelihood of creating a temperature
profile by separating the furnace into zones to reduce the amount of fuel required,
the option of using a serial device, which often results in a reduction of energy
requirements.
The present investigation aims to increase the thermal stability of the furnace by
increasing production efficiency in lower proportionate consumption of fuel. The
replacement of conventional firebricks is done with the lower thermal conductive
pyro block material, which works as thermal insulation to store generated heat within

the furnace only. The validation of the presented study was generated by using
thermographic plots near the furnace’s outer door compared to the furnace wall
insulated with fire bricks vs the furnace wall insulated with lower thermal conductive
pyro block material. The following achieved results multi-objective study presented
using advanced data predictive artificial neural network study.
The artificial neural network (ANN) is the most recently developed and commonly
used technique for predicting parameters for various input and output values. In the
currentanalysis,theassociationdatapointsareusedasfeedbacktotheANN.InANN,
the Levenberg Med algorithm is used to consider feed-forward backpropagation.
70% of the data were used for preparation, 15% for research, and 15% for validation.
The number of neurons between the input and output layers varies, as is the degree
of neuron independence. The network with the lowest MSE error value and the
highest regression coefficient is considered. In the present study, the regressive multi-
optimization study generated between the input fuel value required to achieve the
desired output as a production quantity in a furnace in correlation with the exact
amount of energy needed.
Table 2.2 illustrates the possible areas where improvisation needs to be done along
with its priority (Table 2.3).
2.3 Implementation of Proposed Ceramic Fibre
The present investigation is carried over Boogie Hearth furnace investigated over two
types of refractory material: furnace with fire brick thermal refractory and pyro block
refractory. The practical validation is generated using thermographic plots placed on
the furnace’s door to account for the variable of heat lost in the environment between
both materials. In this investigation, replacing conventional material with pyro block,
a detailed energy account is implemented with the audit of kaizen implementation
as part of quality check and continuous improvement policy of energy conservation
approach. The below section shows the account for the implementation of kaizen
technology on the furnace refractories. Pyro block modules are ceramic fibre lining
devices explicitly developed for use in high-temperature furnaces. The module is
made from a high-purity mix of raw materials used to make standard and zirconia
type ceramic fibres. The monolithic fibre is easily sliced to match through holes
and modified in the field. Additionally, these modules are compact, have a low-heat
storage capacity, and have a long-lasting operation (Fig. 2.6).
The heat loss calculation is shown in Table 2.4. The prosed improvement of
pyro block offers versatile amounts of benefits to the working of the furnace by
keeping generated latent heat inside the furnace only, ensuring sustainability for the
production of the products. Due to the phenomenon of a pyro block, which offers
lower thermal conductivity on working furnace temperature, suggest that the storage
required in batches of output for the case of existing bricks lining gives around
2,448,236 kcal. In comparison, when the pyro block is implemented, the total heat
needed for a storage unit for batch-wise production is about 1,221,506 kcal. The

Table 2.2 Improvement kaizen SPN chart
Principles Questions to
be asked for
the process
Process No. Solution Effective The
benefit
to cost
ratio
Adaptability SPN
Modification Can we
modify the
furnace
design for
energy
conservation
Heating Furnace
lining
bricks
replaced by
ceramic
fibre
5 3 3 45
Combustion Used
biofuel
instead of
SKO-2
3 5 5 75
Automation Can we
automate the
process
partially or
fully with or
without a
close loop
system?
Combustion Oxygen
sensor for
to control
the excess
air
3 3 3 27
Utilities Can we
identify the
individual
elements of
energy
consumption
by looking
at the tree
structure of
utility
Ideal running Can we
record the
cycle in
terms of
energy
parameters
and reduce
the idle
running
time?
Utility Provision
of VFD
motor for
combustion
blower
3 3 3 27
Benchmarking Can we do a
benchmark
against the
most
efficient
process
within
Heating To improve
furnace
efficiency
up to 20
from 13%
2 2 2 8

Table 2.3 Solution priority
criteria chart index
Effective Cost benefit Adaptability
Solution priority number (SPN) criteria
Low 1 1 1
Medium 3 3 3
High 5 5 5
Fig. 2.6 Pyro block annealing to the wall section pre-processing
gross difference in value is about 52% which suggests the correct implementation of
the kaizen strategy (Fig. 2.7 and 2.8).
2.4 Validation of Results
The validation of results is generated by using thermographs to plot the thermal
visuals around the furnace door. The thermographic plot suggests how the furnace’s
thermal loss was encountered before implementing kaizen strategic pyro block where

Table 2.4 Furnace datasheet
Description Unit Existing Proposed Remarks
Furnace length M 5.40 5.40
Furnace width M 3.77 3.77
Furnace height M 2.55 2.30
Wall thickness M 0.45 0.4
Top thickness M 0.55 0.50
Bottom thickness M 0.75 0.55
Material Fire bricks Ceramic bricks
Furnace working temp. °C 880 880
Ambient temp. °C 28 28
Furnace heat losses kCal/Hr 34,231 17,831 16,400
Kwh/Hr 40 21 19
Furnace heat storages Kcal 4,950,000 1,530,000 3,420,000
Kwh 5776 1785 3991
Considering bricks 50% heat transfer Kwh/batch 2888 1785 1103
Fig. 2.7 Furnace before kaizen

Fig. 2.8 Furnace after installation of pyro block
inner refractories lines are equipped with fire bricks vs inner refractories lined with
pyro block (Tables 2.5 and 2.6).
2.4.1 Thermographs
From the above validation for comparative cases, it can be validated that when Boogie
Hearth furnace was implemented on conventional lining wall brick material, it fails
to store the maximum amount of generated heat inside the system thus, local hot stop
creation can be observed over the thermographs of furnace door in case 1, compar-
atively when the kaizen implementation allocated in strategic product development
to save the cost of burning fuel and increase the sustainability, thermograph visual
shows at the same furnace door, there is less creation of local hot spot; thus, the gener-
ated heat tends to be stored inside the furnace only. Furnace effectiveness concerning
the energy consumption to the product can be seen in Figs. 2.9 and 2.10
In heat treatment of any product, the primary intention is to generate heat and store
it inside the confined space so maximum heat can be used to treat the product. Heat
can produce by burning fuel inside the burner, so it is essential to monitor fuel spend
to achieve heat versus heat spend. In the present study, the initial furnace was loaded
with conventional fire brick refractories. The burning fuel LPG was net around 10,059
M3
, compared to when the furnace loaded with strategic Kaizen implemented pyro
block specific reduction fuel consumption has achieved for around same production
rate. The total energy conservation saving achieved around 50%. The saving of fuel

Table 2.5 Result validation
Energy conservation
Heat losses are reduced by insulation,
energy saving by using high-velocity
burners and furnace
Implementation
(target)
Investment cost: 2.40 RML
Before Kaizen After Kaizen
Consumption (basic calculation): (A) Consumption (basic calculation): (B)
Before implementation, energy
consumption
After implementation, tempering furnace LPG
consumption is 7.30 M3/MT
F or hardening furnace LPG consumption
was 13.34 M3/MT
Reduction: (A)–(B)
LPG 6.0 M3/MT, i.e. 35% energy saving
and ROI is 12% PM
CO2 reduction:
190. MT/ Year/F C
Heat treatment team LPG M3/MT Remarks
Before 13,34 Energy saving
After 6.94 45%
Saving 6.40
F or CO2 calculation:1 kg LPG–2.83 kgs
CO2 emits
Table 2.6 Energy
consumption report
LPG
consumption in
M3
Production in MT M3/MT
Before 3.023 271 11.15
3.494 215 16.25
3.542 268 13.22
10.059 754 13.34
After 1.031 146 7.08
1.204 169 7.14
1.299 198 6.56
1.593 251 6.35
5.127 763 6.72
Energy saving M3/MT 6.62
Percentage 50%

Fig. 2.9 Before Kaizen
Fig. 2.10 After Kaizen
leads to saving working costs and amount of CO2 emission in the environment. The
increased sustainability of the furnace is presented in Table 2.7. The expected CO2
emission for a standard 1 kg LPG tends to be a 1.5 kg/kg production value. In this,
1 M3
/MT = 1000 kg of production (https://people.exeter.ac.uk/TWDavies/energy_
conversion/Calculation%20of%20CO2%20emissions%20from%20fuels.htm).
Table 2.7 Effective
utilization of comparative fire
brick wall vs pyro block
S. No. Fire brick wall Pyro block
LPG 13.34 M3/MT 6.72 M3/MT
CO2 estimation 13,340 kg LPG ×
1.5 kg/Kg CO2
6720 kg LPG ×
1.5 kg/Kg CO2
Total CO2
(tonne/MT)
20.01 tonne/MT 10.01 tonne/MT
Effectiveness By pyro block = 45% less CO2 emission

2.5 Artificial Neural Network
The artificial neural network (ANN) is the most recently developed and commonly
used technique for predicting parameters for a range of input and output values. In the
currentanalysis,theassociationdatapointsareusedasfeedbacktotheANN.InANN,
the Leverberg Med algorithm is used to consider feed-forward backpropagation.
80% of the data were used for preparation, 10% for a test, and 10% for validation
(Talele et al. 2021; Talele et al. 2021; Talele et al. 2021). The number of neurons
between the input and output layers is varied. Analysis of neuron independence is
also conducted, with the network with the lowest MSE error value and the highest
regression coefficient being regarded. The present study uses a network of ten layers,
and it is found that the contribution of the ANN is specific and reliable in predicting
the working effectiveness of the furnace. The topmost close fitting of a curve can be
observed value near the one shown in Fig. 2.11.
This is the form of multi-objective analysis where the predictive correlation is built
between both cases to determine a correlative difference in the working effectiveness
of the furnace. The data visualization is performed by Python code where the object
is set to be the production value against which fuel must burn in the specific case. The
mathematical array developed in both the comparative cases, as shown in Fig. 2.12.
Figure 2.12 represents formulated data visualization with an available mathe-
matical array. A comparative plot can be seen as in Fig. 2.12, where the burning
value of instantaneous fuel is compared with the total effectiveness of the system. A
furnace equipped with a conventional lining of fire bricks consumes more fuel, with
the strategic kaizen implementation to change material of furnace wall with pyro
block lining results conversion of energy within system and consume less amount of
burning fuel.
2.6 Conclusion
Furnaces are one of the essential tools in the steel, forging, and metallurgy indus-
tries. As evolution occurs and the world moves towards net-zero emission, it is
essential to maximize energy utilization. Replacing existing refractories, using clean
fuel, and ensuring complete burning are the primary stages. The study performed
illustrates that ceramics play a prominent role and often are efficient solutions for
energy storage-related problems. We can conclude that ceramic fibres (pyro block)
can be used as efficient furnace linings to fulfil both cost-saving and energy saving
aspects during this performed experiment. As a result of low-thermal conductivity,
the amount of heat that was dissipated through the furnace walls was drastically
reduced. This helped keep the internal combustion chamber heated for a longer time
and equal temperature distributions.
As discussed, the thermal conductivity of conventional refractories is 1.2 W/Mk,
and that of ceramic is as low as 0.34. This lower thermal conductivity has reduced

Fig. 2.11 Coefficient of regression obtained from ANN
the conduction through walls. This results in maximum heat acquisition and reduced
burner operating time. The development of smart burner technology makes it possible
to control heating and concentration on areas with low temperatures. This has drasti-
cally reduced the fuel consumption required per batch. Alongside all these industrial
benefits, these low-thermal conductivity ceramic fibres contribute significantly to
the environment. These new furnace linings have reduced fuel consumption gives
clean burning without leaving any residues. It has also reduced CO2 emissions.
Furnaces are also equipped with oxygen sensor which prohibits fresh/non-polluted
air to escape through chimneys. This air is reheated using recuperators and reused for
better combustion. A multi-objective analysis is conducted based on neurons study
for practically validated data of the furnace. It can be seen that the plot of the neurons
is near to the value of 1 for the 3 cases, which predict the quality of results.

Fig. 2.12 Before and after case for burned fuel versus effective ratio
References
Chen WH, Chung YC, Liu JL (2005) Analysis on energy consumption and performance of reheating
furnaces in a hot strip mill. Int Commun Heat Mass Transfer 32:695–706
Cvinolobov NP, Brovkin VL (2004) Furnaces of ferrous metallurgy: Learner’s guide for universities.
Porogi, Dnepropetrovsk, p 154
Dubey SK, Srinivasan P (2014) Development of three-dimensional transient numerical heat conduc-
tion model with the growth of oxide scale for steel billet reheat simulation. Int J Thermal Sci
84:214–227.Z
HanSH,ChangD,HuhC(2011)Efficiencyanalysisofradiativeslabheatinginawalking-beam-type
reheating furnace. Energy 36:1265–1272
https://people.exeter.ac.uk/TWDavies/energy_conversion/Calculation%20of%20CO2%20emis
sions%20from%20fuels.htm
Jaklič A, Vode F, Kolenko T (2007) Online simulation model of the slab-reheating process in a
pusher-type furnace. Appl Therm Eng 27(5–6):1105–1114
Källén M (2012) Energy efficiency opportunities within the heat treatment industry. Division of
Heat and Power Technology, Chalmers University of Technology Göteborg, Sweden
Kim JG, Huh KY (2000) Prediction of transient slab temperature distribution in the reheating
furnace of a walking-beam type for rolling of steel slabs. ISIJ Int 40(11)
Kim JG, Huh KY, Kim IT (2000) Three-dimensional analysis of the walking-beam-type slab
reheating furnace in hot strip mills. Nume Heat Transfer: Part A: Appl 38(6):589–609
Lisienko VG, et al (2016) IOP Conf Ser: Mater Sci Eng 150:012023
Mayr B, et al CFD and experimental analysis of a 115 kW natural gas-fired lab-scale furnace under
oxy-fuel and air-fuel conditions. Fuel 159:864–875
Mayr B, et al (2017) CFD analysis of a pusher type reheating furnace and the billet heating
characteristic. Appl Thermal Eng 115:986–994
Quested PN et al (2009) Measurement and estimation of thermophysical properties of nickel-based
superalloys. Mater Sci Technol 25(2):154–162
Romenets VA, et al (2005) Romelt process. M.: MISiS, Publishing House “Ore and Metalls”, p 400

Rusinowski H, Szega M (2001) The influence of the operational parameters of chamber furnaces
on the consumption of the chemical energy of fuels. Energy 26:1121–1133
Stål och värmebehandling – En handbook (2010) Swerea IVF
Talele V, Mathew VK, Sonawane N, Sanap S, Chandak A, Nema A (2021) CFD and ANN approach
to predict the flow pattern around the square and rectangular bluff body for high Reynolds number.
Mater Today: Proc 1(47):3177–3185
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over Ahmed body for optimal topology selection between upper and under bodywork using ANN
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“Akademkniga”, p 464

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Kirjeessään Sigridille hän lausui sydämellisesti ottavansa osaa
hänen onneensa ja ajatuksissaan ja rukouksissaan muistavansa
häntä niinkuin ennenkin.
Muutamia viikkoja senjälkeen hän ällistyi nähdessään loistavan
seurueen karahuttavan pihaan; ratsastajat kiiruhtivat sisään, ja
Kustaa näki ikkunastaan komeita tataarilaisia hevosia kallisarvoisine,
loistavine suitsineen.
Isä Anselm syöksyi sisään.
"Ottakaa paras takki yllenne", änkytti hän, "Venäjän tsaarin
lähettiläät ovat täällä pyytäen tavata teitä heti."
"Mitä he minusta tahtovat?" kysyi Kustaa hämmästyksissään,
mutta ollen liian vähän tottunut toimiansa itse määräämään hän
noudatti käskyä ja jouduttihe lähetystöä vastaanottamaan.

Kaksi komeasti puettua venäläistä odotti häntä luostarisalissa.
Nähdessään hänet, he heittäytyivät maahan tahtoen suudella
hänen käsiään.
Melkein säikähtäen Kustaa vetäytyi taaksepäin kysyen mitä he
halusivat.
"Me tuomme teille tervehdyksen Venäjän tsaarilta, mahtavalta
Boris
Godunovilta", vastasivat he.
"Olen valmis kuulemaan hänen käskyjään", vastasi Kustaa aivan
ymmällään.
"Hän toivoo, että heti seuraisit meitä Moskovaan", vastasi se
heistä, joka näytti etevämmältä.
"Mitä minulla on siellä tekemistä?"
"Korkea herramme antaa sinulle itse tiedon siitä."
"Antakaa minulle edes muutaman päivän miettimisaika", vastasi
ällistynyt nuorukainen.
"Se olkoon myönnetty sillä ehdolla, ettet puhu asiasta kenenkään
kanssa."
"Onko se välttämätöntä?"
"Kaikkia askeleitasi vartioidaan tästä alkaen."

"Todistukseksi siitä, että todella olemme mahtavan tsaarin
lähettämät, lähettää hän sinulle tämän."
Toinen lähettiläistä ojensi hänelle pienen, helmillä ja jalokivillä
runsaasti koristetun lippaan.
Toinen antoi avaimen sanoen samalla, ettei nuori herra saanut
aukaista lipasta muuten kuin ollessaan yksin.
Kustaa otti ne ihmetellen.
"Kahden päivän kuluttua samaan aikaan", sanoi lähetti ja kumarsi
taas syvään.
Oven ulkopuolella seisoi joukko venäläisiä palvelijoita; he
lankesivat maahan hänen ohi mennessään.
Mitähän lipas sisälsi, se tuntui aivan kevyeltä. Tultuaan
työhuoneeseensa hän pani sen pöydälle ja otti avaimen.
Hän aukaisi lippaan.
Taivas, mikä näky! Tyttö, niin kaunis, ettei hän luullut koskaan
moista nähneensä.
Eloisat, veitikkamaiset silmät katsoivat suoraan häneen, ja
suloinen suu hymyili hänelle.
Kuva oli ohuen hopeaharson verhossa, kehyksenä oli pelkkiä
jalokiviä, jotka arvossa vastasivat kokonaista kuningaskuntaa.
Oliko tämä lahja hänelle, halveksitulle, ja kuuluiko neitokin
lahjaan?

Sitä hän ei saanut selville, ja jos hän kysyisi, ei hän ehkä saisi
vastausta… ei, hän päätti odottaa ja haaveilla onnesta, joka ei
koskaan tässä maailmassa tule hänen omakseen.
Isä Anselm ei tahtonut velvollisuutensa tuntevana kokkina joutua
häpeään koreiden muukalaisten silmissä, mutta nämä olivatkin
varatut kaikkien mahdollisuuksien varalle; heillä oli mukanaan omat
ruokavaransa ja oma kyökkimestarinsa, ja yhdistetyistä varoista
saatiin näin maukas ateria.
Isäntänä tuli Kustaan juoda vierasten malja, mutta se mieto viini,
jota hän käytti, olikin vaihdettu väkevämpään ja tulisempaan, ja sitä
nauttiessaan hän tunsi rohkeutensa ja voimiensa kasvavan. Mitäpä
siinä oli sopimatonta, että tsaari tahtoi tavata Kustaata, Eerik XIV:n
poikaa.
Seuraavana päivänä hän ilmoitti lähetystölle olevansa valmis
matkaan.
Mutta nyt odotti häntä uusi yllätys.
Useita kalliita, puoleksi itämaisia pukuja oli varattu häntä varten,
ja kun hän jonkun verran vastustettuaan oli taipunut pukeutumaan
semmoiseen, huudahtivat venäläiset, että hän nyt näytti
syntyperäiseltä ruhtinaalta ja kuninkaalta.
Kustaa käsitti sen varmaankin tyhjäksi kohteliaisuudeksi, oikeata
tarkoitusta hän ei aavistanut.
Taivalta tehtiin kaikella mukavuudella, ja nuoren herran
yksinkertainen ja vaatimaton esiintyminen kaikesta siitä
kunnioituksesta huolimatta, mikä hänen osakseen tuli, valtasi siihen

määrään venäläisten sydämet, että he Moskovaan saavuttaessa
lankesivat hänen jalkoihinsa pyytäen hänen suojelustaan.
Uneksiko hän vai oliko valveilla? Kaikkiin kysymyksiin oli hänelle
vastattu:
"Odottakaa, kunnes saavumme Moskovaan."
Nyt hän oli siellä. Jo kuului kellojen soittoa kaikista kirkoista,
tykinlaukaukset tärisyttivät rakennuksia, ja kansa aaltoili kaduilla
huutaen: "Eläköön!"
Alhaalla linnanportilla otti uhkea Boris itse hänet vastaan syleillen
häntä.
Käsikädessä he menivät portaita ylös; ensi huoneissa lankesivat
kaikki polvilleen, sitten kumartelivat korkeat herrat lattiaan asti ja
lopuksi tuli muutamia huoneita, jotka olivat aivan tyhjät.
Näkymättömät kädet vetivät oviverhot syrjään, sieltä säteili
häikäisevä valo, ja siellä seisoi tuo ihana kuva todellisena,
hopeausvaan kietoutuneena.
Nuoret ihanat neitoset ympäröivät häntä, mutta Kustaa ei nähnyt
ketään muuta kuin hänet yksin, ja melkein tietämättään hän polvistui
hänen eteensä.
Samoin kuin kuvassa katsoi neito nytkin häneen, tumma puna
nousi hänen hienoille poskilleen, ja hän loi nopean silmäyksen
tsaariin.
Tämä antoi merkin, ja hovineitosien kevyt parvi leijaili pois.

He olivat nyt kolmisin.
Ihana kuva heitti pois hopeahuntunsa, ja tuossa seisoi nyt
valkoiseen puettu neitonen kainona ja hämillään.
"Tyttäreni Maria", sanoi tsaari.
"Taivaan kuningattarelta hän näyttää", huudahti Kustaa edelleen
polvillaan, "ja ainoastaan näin rohkenen osoittaa hänelle
kunnioitustani."
"Nouskaa, ritari!" sanoi neito ojentaen hänelle kätensä.
Hän totteli, mutta pitikin edelleen häntä kädestä.
Silloin Maria katsoi veitikkamaisesti hymyillen häneen.
"Niin, niin", huudahti Kustaa ihastuksissaan, "tuollainen olette
kuvassa, tuollaisena olen yöt-päivät nähnyt teidät edessäni siitä asti
kuin sain tämän kalliin kuvan!"
Hänellä oli medaljongi povellaan; hän otti sen esiin ja suuteli sitä.
Punastuen pani Maria kätensä silmilleen.
"Tiesinpä", sanoi Boris nauraen, "että kuva olisi kylliksi
haihduttamaan teidän epäröimisenne; te rakastatte Mariaa, niin
tekee jokainen, jonka olen suonut nähdä hänet, mutta teille minä
annan hänet, jos onnistutte voittamaan hänen rakkautensa."
Kustaa ei tiennyt uskoako korviaan.
"Jotta voisitte sopia asiasta, jätän teidät hetkiseksi kahden", lisäsi
venäläinen. "Katsokaa, että asiat sukeutuvat minulle mieliksi."

Ja hän käydä lynkytti nauraen pois.
Mutta Kustaa seisoi liikkumattomana tuijottaen kauniiseen tyttöön.
"Maria!" virkkoi hän.
Hämillään katseli tyttö häntä; sitten hän istui sohvaan ja peitti
kasvonsa käsillään.
Kustaa polvistui hänen viereensä. "Työnnätkö minut luotasi?"
kysäisi hän.
"En", vastasi tyttö ja ojensi hänelle kätensä. "Mutta älkää
polvistuko noin isäni nähden, sillä täällä Venäjällä ei ole tapa niin."
"Mutta kun olemme kahden?"
"Silloin saatte tehdä niin, niin kauan kuin se huvittaa minua."
"Ja kuinka pitkä aika se on?"
"Mistäpä sen tietäisin… laskekaa irti käteni."
"Etkö tahdo antaa sitä minulle ainaiseksi?"
"Se ei riipu minusta. Mutta sanokaa mitä lausuitte kuvan
nähdessänne."
"En mitään, seisoin kuin sokaistuna!"
"Komeiden jalokivien tähdenkö!"
"En, Maria, vaan sinun tähtesi. Minä suutelin kuvaa ja ajattelin…"
"Mitä ajattelitte?" kysyi hän uteliaasti.

"Jospa kerran saisin sulkea hänet syliini." Kustaa rohkeni kietoa
kätensä hänen vyötäisilleen.
"Entä sitten?" kysyi Maria.
"Ja saisin painaa suudelman hänen huulilleen."
"Sitähän ei mikään estäne."
"Sallitteko, Maria?"
"Sallin", sanoi tyttö ja suuteli häntä rivakasti, "te olette niin
toisenlainen kuin kaikki muut."
"Mistä sen tiedätte?" kysyi Kustaa huomattavasti jäähtyneenä
istuen hänen viereensä.
"En ole kuuro enkä sokea, niin että tiedän sen kylläkin. Sinä olet
hyvin toisenlainen kuin muut", lisäsi hän nojautuen häneen.
"Sinäkin olet hyvin toisenlainen kuin muut", toisti Kustaa katsoen
hänen säteileviin silmiinsä, "mutta myöskin ihanampi kuin kaikki
muut."
Nopeasti hypähti Maria hänen vierestään. "Tahdonpa tanssia
sinulle."
Kevyenä kuin keijukainen hän liiteli hänen ympärillään, milloin
ojentaen kätensä häntä kohden, milloin väistyen. Kustaa koetti saada
kiinni hänet, mutta se oli mahdotonta; hän tunsi kyllä hänen
hengähdyksensä poskellaan, mutta samassa silmänräpäyksessä hän
oli poissa.
Silloin kuuluivat tsaarin raskaat askeleet.

Tuossa tuokiossa istui Maria sohvalla ja osoitti Kustaalle paikan
vähän loitompana.
"No—o?" kysyi Boris jo ovessa.
"Hyvä että tulit, rakas isä", sanoi Maria. "Vieraasi ei ole virkkanut
kymmentä sanaa."
"Mitä, oletko niin lumonnut hänet? No, tulkoon huomenna
uudelleen."
Kahdeksan päivää jatkui näitä käyntejä; lemmenkiihko oli
sokaissut järjen, ja mielettömyyteen asti Kustaa rakasti tätä
ihmeellistä tyttöä, joka kaikessa oppimattomuudessaan, melkeinpä
raakuudessaan oli täydellinen keimailija.
"Anna hänet puolisokseni!" huudahti Kustaa kahdeksantena
päivänä, kun
Boris tuli heidän luokseen. "Minä en voi elää ilman häntä."
Boris katsahti tyttäreensä.
Tämä ei ollut koskaan näyttänyt niin riemuitsevalta. "Me
rakastamme toisiamme", sanoi hän, "ja odotamme vain
suostumustasi."
"Puhukaamme myötäjäisistä", sanoi Boris.
"Saadakseni hänet tahdon tehdä työtä kuin päivätyöläinen", lausui
Kustaa painaen sulotarta povelleen.
"Annetaan isän määrätä", sanoi tyttö hyväillen Kustaan poskea.

"Vanha tsaarisuku on sammunut", virkkoi Boris, "teistä on
polveutuva uusi; tervehdin teitä Venäjän tsaarina ja tsaarittarena."
Kustaa seisoi sanattomana hämmästyksestä.
Mutta Marialta pääsi riemuhuuto. Hän syleili isäänsä ja hyväili
Kustaata ylenpalttisesti.
"Tähän liitän vain kaksi ehtoa", lisäsi venäläinen.
"Anna minun suostua niihin meidän molempain puolesta", sanoi
Maria.
"Ensiksikin tulee hänen kääntyä meidän uskontoomme ja toiseksi
tulee hänen ruveta vaatimaan itselleen Ruotsin kruunua. Minä autan
häntä miehilläni ja rahoillani."
Kustaa huokasi syvään. "Molemmat ovat yhtä mahdottomat",
sanoi hän.
"Kustaa!" kirkaisi Maria.
"Jumala yksin tietää, kuinka suuresti sinua rakastan", vastasi
Kustaa, "mutta rakkaus äitiini, uskontooni ja isänmaahani on minulle
sama kuin elämäni. Mielelläni antaisin henkeni Ruotsin puolesta,
mutta Ruotsin miesten ei tarvitse koskaan vuodattaa vertansa eikä
kuolla, jotta minä siitä jotakin voittaisin."
"Mitä olet sinä velkapää heille, jotka niin ovat sinua kohdelleet?"
huudahti Maria.
"Heidän väärä menettelynsä ei olisi minään puolustuksena minulle,
jos unhottaisin vannomani pyhät valat."

"Tahdonpa sanoa sanan minäkin", puuttui puheeseen Boris.
"Tarjoomani suuret edut olisivat panneet kenen tahansa toisen pään
pyörälle, mutta teidän päänne on varmaan jo entuudestaan pyörällä,
koska ette lankea polvillenne ja kiitä minua. On yhdentekevää mitä
te uskotte tai ette usko; uskonto on verho, johon muodon vuoksi
pukeudutaan. Jos pelkäätte ruotsalaisten miekaniskuja, niin tahdon
sanoa teille, että päälliköt ovat liian viisaita etunenässä kulkeakseen,
ja lopuksi" — tässä hän löi nyrkillään pöytään, niin että se meni
sirpaleiksi — "lopuksi tahdon sanoa teille: Ajatelkaa asiata
huomiseen, silloin tahdon saada vastauksen." Näin sanoen hän lähti
huoneesta.
Mutta nyt alkoi vaikein taistelu.
Maria ahdisti häntä rukouksin ja kyynelin, käytti kaikkia
houkutuskeinoja saadakseen hänet suostumaan, mutta Kustaa
väisteli hänen hyväilyjään sanoen surullisesti: "Maria, älä kiusaa
minua, sinä vain lisäät kärsimystäni, mutta päätöstäni et voi
muuttaa."
Sitä Maria kuitenkin juuri tahtoi; mitä merkitsivät uskollisuus ja
lupaukset sellaiselle, joka ei koskaan ollut niitä pitänyt. Mutta kun
hän näki, että kaikki ponnistukset olivat turhat, pääsi hänessä
valloille se raivo, joka kiehui hänen sydämessään; ensin hän pyysi
kuvansa takaisin, ja kun oli sen saanut, rupesi hän solvaamaan
Kustaata, nimitti häntä narriksi ja sanoi, ettei ollut koskaan välittänyt
hänestä.
Kustaa ei kuullut mitä hän sanoi, hän istui masentuneena,
hervahtuneena.

Silloin hän vielä kerran tunsi hänen käsivartensa kaulallaan ja kuuli
kuiskauksen: "Tule ja seuraa minua!"
"Käärme!" vastasi hän ja ravisti hänet luotaan.
Kirkuen tyttö pakeni, ja Kustaa palasi huoneeseensa.
Seuraavana aamuna Kustaa löysi omat vaatteensa sänkynsä
laidalta, ja tuskin hän oli pukeutunut niihin, ennenkuin tuli eräs
palvelija, joka tsaarin määräyksestä käski hänen seurata.
Kurjat ajopelit odottivat, ja kaikin puolin vaivalloisen matkan
jälkeen hän saapui vankilaan.
Nyt oli hänellä hyvä aika miettiä vaihtelevaa elämäänsä; hän
huomasi pian, että rakkautensa Mariaan oli ollut vain huumausta, ja
hän piti elämää vankilassa siedettävämpänä kuin sitä, mitä olisi
saanut viettää hänen kanssaan.
Menettelyänsä ei hän hetkeäkään katunut. "Toisin ei voinut
käydä", sanoi hän itsekseen. "Olen vain tehnyt velvollisuuteni."
Mutta kovia päiviä hän nyt sai kokea, ei ainoastaan vankilan
yksinäisyyttä ja niukkaa ravintoa, vaan myös kaiken toiminnan
puutetta.
Eräänä päivänä oli kärpänen eksynyt sisään ikkunarautojen välitse;
hän murensi hitusen leipää sille ja sai sitten ilokseen nähdä sen
palaavan joka päivä ottamaan osansa hänen niukasta ruuastaan.
Mutta kun syksy tuli ja kärpänen kuoli, silloin hän itki katkeria
kyyneliä, ikäänkuin olisi menettänyt rakkaan ystävän.

Vanginvartijankin kävi sääliksi häntä, ja hän vei hänelle vankityrmään
häkin, jossa oli peipponen.
Kuinka hän rakasti ja vaalikaan sitä! Pieni eläin istui hänen
kädellään ja söi hänen suustaan.
Mutta kun kevät tuli, istui pikku laulaja ikkunalla ja kaiutti
kaihoisena säveltänsä.
Eräänä päivänä, tuodessaan tapansa mukaan vangille hänen
pienen päiväannoksensa, vartija huomasi hänen itkeneen.
"Minusta voisi teidät hyvästi laskea vapaaksi", sanoi vartija.
"En minä itseni vuoksi, vaan tuon tähden", sanoi Kustaa osoittaen
lintua.
"Tahtoisitteko laskea sen pois?"
"Olisin teille siitä hyvin kiitollinen."
"Etteköhän tulisi kaipaamaan sitä?"
"Sen ilosta iloitsisin minäkin."
Peippo sai vapautensa, ja Kustaa puhui senjälkeen useasti siitä,
kuinka onnellinen lintu nyt oli.
Viimein muutettiin hänetkin pieneen Kashinin kaupunkiin; siellä
hän pääsi vapaaksi ja sai mielensä mukaan työskennellä
tutkimuksissaan.
Mutta hänen voimansa olivat jo murtuneet, kuvat hänen
menneisyydestään astuivat taas hänen mielikuvitukseensa. Hän oli

toisinaan nuorukainen, joka torjui luotansa jesuiittoja, kun he
tahtoivat viekoitella häntä vaatimaan isänsä kruunua, toisinaan
kerjäläinen, joka etsi sisartansa Sigismundin hovista, toisinaan oli
hän tsaarin hovissa halveksien torjuen kiusauksia luotaan.
Kashenka-joen rannalla sai harhaileva kuninkaanpoika hiljaisen
hautansa kauniissa koivikossa 1607.
Hiljaisena ja huomiota herättämättä hän oli elänyt; hänen
suuruutensa oli siinä, että hän oli torjunut luotaan kiusauksia. Hiljaa
ja huomiota herättämättä hän myös poistui.
19.
LEHTI KÄÄNTYY.
Herttuan ja ylhäisemmän aatelin väli oli melkein alusta alkaen
kireä.
Kuninkaan poika ja perinnöllinen ruhtinas ei sietänyt, että kukaan
asettui hänen arvoisekseen.
Ylhäisellä aatelistolla oli myöskin kuninkaallista verta suonissaan,
heidän sukuluettelonsa olivat yhtä loistavat kuin hänenkin, ja
sentähden loukkasi heidän ylpeyttään se, että Kaarle valitsi
ulkomaisen ruhtinattaren morsiamekseen, ja vielä enemmän he
loukkaantuivat, kun hän Juhanan naimisiin mennessä peittelemättä
lausui, että Vaasan suvun jäsenten tulisi valita puolisonsa
ulkomaisista ruhtinasperheistä.

Molemminpuoliseen tyytymättömyyteen oli sekoittunut koko
joukko omien pyyteiden tavoittelemista.
Kaarle piti jäykästi huolta omista eduistaan ja joutui sentähden
usein riitaan, etenkin Eerik Sparren, Sundbyn herran, ja Hogenskild
Bielken, Åkerön herran kanssa.
Molempain suuret tilukset olivat ruhtinaskunnassa, ja
kumpainenkin, mutta varsinkin herra Hogenskild, tunsi oman
arvonsa.
He asettuivat sentähden voimakkaasti ja kursailematta hänen
yrityksiään vastaan, vetosivat etuoikeuksiinsa ja saivat tavallisesti
suojaa kuninkaalta.
Kun herttua ja Åkerön jäykkä herra 1588 olivat joutuneet
kiivaaseen kiistaan, kirjoitti jälkimäinen häikäilemättä ylpeälle
riitaveljelleen — että vanha kuningas Kustaa kyllä oli ollut niitä
kuninkaita, jotka ovat edistäneet valtakunnan parasta, mutta että
hän olikin vallanperimysoikeuden kautta saanut siitä suuremman
palkan kuin kukaan muu Ruotsin kuninkaista. Aatelisto toivoi
sentähden vastavuoroonsa saavansa nauttia vapauksiaan,
jonkatähden herra Hogenskild nyt pyysi, että hänen armonsa
herttuan voudit eivät tahtoisi olla niin paljon tekemisissä aateliston
talonpoikien kanssa, kuin tähän asti liiankin usein oli tapahtunut.
On melkein varmaa, ettei kirje tullut suosiollisesti vastaanotetuksi,
ja Kaarle kirjoitti itse siitä Juhanalle vakuuttaen, että aatelistolla oli
aikomus kumota vallanperimysoikeus.
Mutta kuningas pelkäsi siihen aikaan enemmän herttuaa kuin
aatelistoa ja ilmaisi sentähden neuvostossa, minkä varoituksen oli

saanut.
Tämän johdosta keskinäinen kauna yhä kasvoi. Aatelisto piti
Kaarlea kuningasvallan voimakkaimpana tukena ja ylimysvallan
mahtavimpana vastustajana sen kukistamispyrinnöissä.
Erittäinkin ärsytti Kaarlea se, että neuvosto kaikissa veljesten
välisissä riitakysymyksissä aina asettui kuninkaan puolelle.
He koettivat kyllä rauhan säilyttämiseksi estää vihollisuuksien
ilmipuhkeamista, mutta heti kun oli kysymyksessä herttuan
ruhtinaallisten etuoikeuksien rajoittaminen, asettuivat he aina
kuninkaan puolelle.
Etenkin oli Eerik Sparre sellaisissa tilaisuuksissa etukynnessä.
Vesteråsin sopimuksessa hän oli esittänyt kirjan "Pro Rege, Lege
et Grege", jossa hän koetti todistaa, kuinka ruhtinasten liian suuret
vapaudet voivat tulla valtakunnan rauhalle vaaralliseksi.
Kirja saavutti, niinkuin helposti saattoi arvatakin, kuninkaan
mieltymyksen, ja sen tekijä korotettiin valtaneuvokseksi ja sai
osakseen kuninkaan erityisen suosion.
Mutta tästä hetkestä asti kyti Kaarlessa leppymätön viha Eerik
Sparrea kohtaan, ja hän piti tätä salaisena, mutta katkerimpana
vihamiehenään.
Nämä salaiset kuohut odottivat purkautumistaan.
Sigismundin nimittäminen Puolan kuninkaaksi oli ensi aiheena
tähän.

Herttua oli neuvonut luopumaan tästä, mutta nyt se oli
tapahtunut, ja hän arveli, syystä kylläkin, tulleensa askeleen
lähemmäksi valtaistuinta.
Aateli taas puolestaan toivoi etuoikeuksiensa laajennusta sekä
varakuninkaallista valtaa, kun Sigismund melkein aina tuli olemaan
Puolassa.
Ja Juhana puolestaan oli toivonut, että hänen rakas poikansa näin
saadun asemansa nojassa olisi käynyt paljon voimakkaammaksi
kaikkia vihollisiaan.
Mutta jälkeenpäin, kun poika oli poissa, Juhana menetti melkein
kokonaan malttinsa.
Miten olisi hänen mahdollista hallita yhtaikaa kahta valtakuntaa,
jotka asemansa, valtiolaitoksensa ja uskontonsa puolesta olivat
toisistaan niin erillään kuin Ruotsi ja Puola?
Eikö Sigismundin poissaollessa kumpaisenkin, niin hyvin herttuan
kuin aatelinkin, uhkaava kunnianhimo pääsisi vapaasti riehumaan, ja
miten se päättyisi?
Riitaiset vaalit Puolassa eivät ennustaneet uudelle kuninkaalle
mitään hyvää, ei ainakaan niin levottomassa valtakunnassa… koko
Itävallan voima saattoi yhtyä häntä vastaan.
Ja horjuvaisena kuten ainakin tahtoi Juhana, että Sigismund
kääntyisi kotiin.
Me tiedämme, että ne Ruotsin herrat, jotka seurasivat
Sigismundia, estivät sen.

Myöskin Ruotsissa puhui neuvostopuolue valtakunnan vaarasta
siinä tapauksessa, että Venäjän suuriruhtinas tulisi valituksi, sekä
puolalaisten oikeutetusta harmista sen johdosta, että oli osoitettu
niin suurta halveksimista heidän kruunulleen, jos Sigismund siitä
luopuisi; olisi ollut parempi olla koskaan sitä tavoittelematta. Puolan
leskikuningatar olisi pakotettu suorittamaan takaussitoumuksensa, ja
se olisi sitäkin kohtuuttomampaa, kun hän oli luottanut kuningas
Juhanan lupaukseen, että Sigismund ottaa vastaan kutsumuksen, jos
tulee valituksi.
Kalmarin kokouksessa oli Juhana asettanut Sigismundin istumaan
viereensä valtaistuimelle, jotta heitä yhteisesti kunnioitettaisiin
kuninkaina.
Lisäksi tuli tähän vielä koko kansan ihailu. Useat olivat nähneet,
kaikki kuulleet puhuttavan kalliiksi käyneestä lähetystöstä, riemun
ilmaisuista vaalin tuloksen johdosta ja sitten odottamattomasta,
selittämättömästä keikauksesta.
Sellaisista syistä täytyi Juhanan taipua, mutta hän teki sen raskain
mielin.
Mutta kaikki toivotut edut raukesivat. Puolalaisilla ei ollut mitään
halua ottaa osaa aiottuun sotaretkeen Venäjää vastaan.
Katumus ja kaipaus täytti Juhanan sielun; ei pienintäkään voittoa,
ja hän oli lähettänyt pois Sigismundin, ainoan, johon hän luotti ja
jonka kanssa voi puhua.
Niinkuin kaikki heikot luonteet heitti Juhana syyn toisten niskoille.
Milloin oli herttua, milloin valtakunnan herrat muka saaneet aikaan
Sigismundin lähdön.

Hän oleskeli enimmäkseen Kalmarissa ollakseen poikaansa
vähänkin lähempänä.
Hän lähetti tälle kirjeen toisensa perästä rukoillen ja taivutellen
häntä heittämään myrskyisen Puolan ja palaamaan isänsä luokse.
Samaan aikaan, 1588, kirjoitti myöskin Kaarle Sigismundille
kehottaen häntä menemään avioliittoon, heitä kun ei ollut enempää
kuin kolme miespuolista Vaasan sukua ja oli vedettävä yhtä köyttä;
oli näet olemassa puolue, joka oli saanut aikaan paljon kaunaa
veljesten ja omaisten välillä viimeksi kuluneina vuosina.
Yhdeksänvuotisen avioliittonsa aikana oli Maria Pfalzilainen
lahjoittanut puolisolleen kuusi lasta, mutta kaikki ne olivat kuolleet
pienokaisina paitsi tytär Katariina.
Herttua pelkäsi siis, että suku sammuisi, ja se oli kaikin mokomin
estettävä.
Samaan aikaan kirjoitti myöskin Juhana pojalleen, että oli
paljastunut kavalia salahankkeita sekä että oli olemassa henkilöitä,
jotka salaa toimiskelivat siihen suuntaan, että kuningassuku kuolisi ja
he saisivat vallan käsiinsä.
Sellaisissa olosuhteissa kävivät maan asiat huonommiksi kuin
koskaan ennen.
Neuvosto katsoi sopivaksi huomauttaa kuninkaalle, että hänen
hovissaan ja maatiloillaan meneteltiin hyvin taitamattomasti
nautinnoissa ja kulotuksissa.
Hovijunkkareilla, lakeijoilla, tallimestareilla, henkivartijoilla ja
muulla irtolaisjoukolla ei ollut mitään määrää, ja vaimoineen

lapsineen nämä seurasivat hovia ollen maalle suureksi rasitukseksi.
Veronkannossa ei ollut mitään järjestystä, eikä tileistä saanut mitään
tolkkua.
Samalla valitettiin, että kuningas piti liian monta ja kallista
rakennusmestaria, vaikka valtiolla jo oli komeita rakennuksia kylläkin.
Läänityksien antamisessa meneteltiin hyvin huolimattomasti, ja
monet saivat niitä vallan ansioitta; samalla pyysi neuvosto, että
annettuja määräyksiä noudatettaisiin ja ettei kuningas jättäisi
täytäntöön panematta mitä itse oli aikaisemmin säätänyt.
Juhana kävi hyvin katkeraksi ja puhui siihen suuntaan, että hänen
sotatarpeiden tähden ehkä oli pakko peruuttaa kaikki aateliston
läänitykset niin hyvin neuvoston jäseniltä kuin muiltakin.
Hänen luonteensa kävi aina pahemmaksi, kun hän kaipasi
poikaansa ja oli kyllästynyt hallitushuoliin.
Tyytymättömänä neuvostoon tahtoi hän kuitenkin hän tässä niin
pitkälle, että piti aina hallussaan valtiorahaston avaimia, niin ettei
voitu kirjeen viejääkään lähettää, ellei kuningas itse antanut rahaa.
Todellisuudessa jäi hallitus ala-arvoisten henkilöjen ja
onnenonkijain huostaan.
Näin muodostui kuninkaan ympärille "sihteerihallitus" — sanoo
Geijer — joka sittemmin Ruotsissa yksivaltaisuuteen taipuvain
hallitsijain aikana tuli kylläkin kuuluisaksi.
Yrjö Pietarinpoikaa voidaan sanoa tämän joukkueen isäksi
Ruotsissa; ja hänen poikansa Eerik Yrjönpoika Tegel tuli, kaikkine

ansioineen mitä hänellä on Ruotsin historiassa, sekä isäänsä että
äitiinsä.
Kuninkaan muista suosikeista tunnemme jo Juhana Henrikinpojan.
Lisäksi mainittakoon Olavi Sverkerinpoika eli Perkeleenpoika.
Hänestä on jo puhuttu "Kaavunkääntäjän" nimellä, koska hän horjui
ja vaappui puolueiden välillä.
Henrik Matinpoika, joka aateloitiin nimellä Huggut, kuului hänkin
Juhanan suosikkeihin, ja jos vielä lisäämme hänen lankonsa, toisen
hyvin tunnetun henkilön, Antti Niilonpoika Sabelfanan, sekä
kronikoitsijan, Rasmus Ludviginpojan, niin olemme luetelleet ne
miehet, joiden käsiin hallitus näinä vuosina oli uskottu.
Syyspuoleen 1588 levisi huhu, että Juhana ja Sigismund aikoivat
käydä tapaamassa toisiaan seuraavana kesänä Räävelissä.
Salaisia lähettejä kulki kuningasten välillä.
Neuvostossa ei kukaan muu tuntenut salaisuutta kuin Klaus
Fleming, joka oli kuninkaan erinomaisessa suosiossa sentähden, että
oli neuvonut Sigismundia jättämään Puolan-matkansa.
Muut neuvosherrat sanoivat häntä uskottomaksi veljeksi ja
koettivat turhaan saada kuninkaan kirjureilta tietää mitä oli tekeillä.
Kerrottiin molempain kuninkaiden puuhaavan rauhaa Venäjän
kanssa, mutta marraskuussa samana vuonna Juhana julkaisi
säätyjen ja neuvoston mieltä kysymättä valtakunnan rahvaalle
käskyn yleisesti koota varoja sotaa varten sekä julistuksen erityisestä
veronkannosta vapaaehtoisen lainan nimellä. Samalla vaati hän
aatelistolta täydellistä ratsupalvelusta, vieläpä kehotti heitä

varustamaan enemmänkin kuin heidän laillinen velvollisuutensa
määräsi, koska kuningas itse, iästään huolimatta, tahtoi omassa
persoonassaan uskaltaa vihollista vastaan kunniakkaan rauhan
saavuttamiseksi.
Alkupuolella vuotta 1589 kutsuttiin kokous Upsalaan. Neuvosto
"aavisti joitakin kummallisia ja merkillisiä syitä"; he saattoivat vain
neuvoa luopumaan matkasta ja sotavarustuksista; välirauha
venäläisten kanssa ei ollut vielä loppunut, kahden vuoden kato oli
lisännyt kurjuutta maassa, ja rutto raivosi Suomessa ja Liivinmaassa.
Mutta Juhana ei hyväksynyt mitään syitä. Mitä kiivaimmin hän
huudahti pitävänsä kavalluksena kaikkia luopumisneuvoja ja että hän
tahtoi mennä Liivinmaahan poikaansa katsomaan, vaikka kansaa
kaatuisi kuin heinää kesällä viikatteen edessä.
Neuvosto kysyi, eikö matkaa lykättäisi tuonnemmaksi, kunnes
saataisiin tietää, sallivatko puolalaiset kuningas Sigismundin lähteä
Rääveliin; voisivathan he luulla, että hän ajatteli karkaamista.
Silloin Juhana hypähti pystyyn sanoen olevan verratonta
hupsumaisuutta luulla Sigismundin aikovan jättää Puolan. Joka
semmoisesta syystä neuvoi kuninkaita luopumaan yhtymisestä, oli
varmaan kavaltaja.
Neuvosto vaikeni, mutta nyt he tunsivat toisensa.
Kuningas neuvotteli taas uskottujensa kanssa, ja neuvosto oli
kirjeenvaihdossa Sigismundin hovissa olevain hengenheimolaistensa
kanssa.

Mutta varustuksia joudutettiin, ja kuningas oli liian malttamaton
sotaväkeä odottamaan.
Hän astui laivaan Tukholmassa kesäkuun 3 p:nä hirveän
ukonilman raivotessa, ja muassaan oli hänellä kuningatar sekä
muutaman kuukauden vanha poika; etevimmät neuvoston jäsenistä
seurasivat heitä, ja matkalle otettiin myös sotaväkeä, mikäli sitä oli
ehtinyt kokoontua.
Rääveliin saavuttuaan he saivat viikkokausia odottaa Sigismundia.
Kun vihdoinkin tuli tieto, että hän oli lähenemässä, nousi Juhana
ratsunsa selkään ja ratsasti suurine seurueineen rakastettua
poikaansa vastaan neljännespenikulman kaupungin ulkopuolelle.
Isä ja poika lankesivat toistensa syliin, ja Juhana itki ilosta.
Totuttuun tapaansa tulivat Puolan herrat suurine aseellisine
seurueineen, ja etenkin nyt, kun piti näyttäytyä Ruotsin herroille, oli
komeus niin ylellistä kuin suinkin oli voitu saada.
Sekä puolalaiset että ruotsalaiset olivat leiriytyneet telttoihin
kaupungin ympärille; vain ylhäisimmät herroista olivat voineet
majoittua kaupunkiin.
Iloissaan ja ihastuksissaan Juhana palasi rakastetun poikansa
kanssa linnaan, jossa Kustaa Banér oli hommannut kuninkaan
käskystä kaikki parhaansa mukaan.
Ensimäisten juhlallisten tervehdysten jälkeen sulkeutuivat Juhana
ja Sigismund erityiseen huoneeseen, johon ei kumpaisenkaan
neuvosherroja laskettu.

Ei kulunut monta päivää, ennenkuin yleisesti puhuttiin, että
kuningas Sigismund aikoi palata Ruotsiin. Puolan herroissa oli
huomattavana yhä kiihtyvä levottomuus, ja sekä heidän että
ruotsalaisten epäluulo katkeroitti molempain kuningasten
yhdessäoloa.
Suurissa kesteissä syyskuun 2 p:nä saapui kirje Sigismundille.
Siinä mainittiin, että tataarit olivat hyökänneet Puolaan ja että
hänen läsnäolonsa siellä oli aivan välttämätön.
Nuori kuningas nousi heti pöydästä ja viittasi puolalaisia
neuvosherrojansa seuraamaan.
Neuvoteltiin mitä nyt olisi tehtävä, ja kaikki olivat yksimielisiä siitä,
että oli kiireesti lähdettävä paluumatkalle.
Siihen oli Sigismund ensin taipuvainen, mutta sitten hän heti sanoi
tahtovansa miettiä asiaa huomiseen.
Silloin hän selitti, että ensin oli saatava päätökseen
rauhankeskustelut Venäjän kanssa, ja sitten olivat kaikki rukoukset ja
vakuuttelut turhia.
Sillä aikaa oli Ruotsin sotapäällystö, luultavasti neuvosherrain
jouduttamana, valmistanut kirjelmän, jossa pyydettiin neuvostoa
estelemään Sigismundin kotimatkaa, josta huhu kertoi, ja kaikessa
pitämään huolta isänmaan parhaasta.
Kestien jälkeisenä päivänä, syyskuun 3:ntena, ilmoitti Olavi
Sverkerinpoika neuvostolle kuningas Juhanan puolesta, että
Sigismund matkustaa Ruotsiin kruunattavaksi ja että neuvosherrat
eivät saa tehdä mitään vastaväitteitä.

Sillä aikaa rukoiltiin Sigismundia hartaasti rientämään ahdistetun
kansansa avuksi.
Puolalaiset rukoilivat, pyytelivät ja varoittelivat, mutta Sigismund ei
voinut tehdä päätöstään.
Jesuiitta Skarge piti julkisen puheen, jossa hän kuvaili tataarien
hävityksiä ja kehotti kaikkia, mutta etenkin kuninkaita, tekemään
lopun moisesta petomaisuudesta. Jumala antaa rangaistuksensa
kohdata niitä, jotka sallivat viatonta verta vuodatettavan, sanoi hän.
Monta puolalaista lähti, ja jäljellejääneissä oli katkeruus yhtä suuri
kuin tyytymättömyys ja seurausten pelko ruotsalaisissa.
Voisihan tämä johtaa julkiseen vihollisuuteen Puolan kanssa, ja
sen sijaan että toivottiin rauhaa Venäjän kanssa, joutuisi Ruotsi ehkä
sotaan molempien maiden kanssa yhtaikaa.
Nyt jos koskaan oli voimakkain sanoin todistettava, miten
järjetöntä oli kiihottaa uusia vihollisia, kun tuskin voitiin vanhojakaan
vastustaa.
Lisäksi pidettiin halveksittavana sitä tapaa, millä Sigismund
viekoiteltaisiin pois valtakunnastaan ja alamaistensa keskuudesta.
Yleinen mielipide aatelistossa, sotajoukossa ja porvaristossa niin
hyvin Räävelissä ja Riiassa kuin yleensä koko maassa oli, että tämä
oli sanottava säälimättä ja peittelemättä.
Syyskuun 5 p:nä kokoontuivat neuvosto, aatelisto ja sotapäällystö
Räävelin tuomiokirkkoon, missä sovittiin siitä, että oli lähetettävä
molemmille kuninkaille yhteinen kirjelmä, joka sitten kyhättiinkin
heti.

Erityisessä Sigismundille osoitetussa kirjeessä pyysi neuvosto, että
hän tahtoisi olla myötävaikuttamassa varman ja siedettävän rauhan
aikaansaamiseksi, koska sotaväki oli tyytymätön 19-vuotiseen
sotaretkeen ja koska varma rauha oli parempi kuin epävarma verinen
voitto. Hätä maassa oli nyt jo tullut niin suureksi, että alamaisten
sorron pitäisi käydä heidän majesteettiensa sydämelle.
Rahvas köyhtyi monista maksuista ja veroista, jotka kävivät
vieläkin raskaammiksi veronkantajain omanvoitonpyynnön tähden.
Enimmäkseen olivat kamreerit ja kyökkikirjurit määräilleet niitä,
eikä niihin oltu suostuttu yhteisestä neuvonpiteestä, niinkuin laki
määräsi.
Paljon kansaa, hevosia ja karjaa oli kolmena viimeksikuluneena
vuotena kuollut, ja kovan, jokavuotisen sotaväenoton tähden oli
monesta talosta mennyt kolme neljä poikaa.
Kahdentoista tai kuudentoista vuoden aikana oli kuningas luvannut
vähentää veroja, mutta sen sijaan oli niitä aina lisätty.
Sopimaton ja tuhlaava hovinpito ilman järjestystä ja kuuliaisuutta,
niin myöskin suuret linna- ja kirkkorakennukset, joihin kuningas oli
ryhtynyt vastoin neuvoston mieltä — kaikki se lisäsi rahvaan
köyhtymistä, niin että siellä, missä ennen oli ollut peltoja ja niittyjä,
oli nyt suurta metsää, ja missä ennen vuosikausia oli ollut
hyvinvoipia talonpoikia, siellä kuljeksittiin nyt keppikerjäläisinä, pussi
kainalossa.
Kolmasosa kaupungeista oli autiona, ja papistossa vallitsi suuri
hajaannus liturgian tähden.

"Heidän majesteettinsa huomatkoot tällaiset seikat ja ajatelkoot,
ettei mikään valtakunta maailmassa ole niin mahtava, ettei sitä
epäjärjestyksen ja pitkällisten sotien kautta voisi saada perikatoon."
Molemmille kuninkaille yhteisesti osoitettu kirjelmä sisälsi
seuraavaa:
Oli päässyt liikkeeseen huhu, että Sigismund aikoi nyt seurata
isäänsä Ruotsiin ja jättää Puolan. Siihen ei kyllä oltu paljon luotettu,
mutta tahdottiin sittenkin pyytää kuninkaita tarkoin punnitsemaan
niin tärkeätä asiaa.
Saattoi kyllä ymmärtää, että Sigismund ikävöi levottomasta
Puolasta perintövaltakuntaansa, samoin kuin myöskin että isän
sydän mielellään näkisi hänen palaavan kotiin, kun vanhuus ja
kaipuu saattoivat kuninkaan yhä kyllästyneemmäksi, ärtyisämmäksi
ja kärsimättömämmäksi hallitustoimiin.
Mutta sitävastoin muistettakoon, että jos Sigismund nyt jättäisi
puolalaiset, niin näistä tulisi Ruotsin vihollisia ja he yhdistyisivät
venäläisiin, jolloin Tanskakin ehkä käyttäisi sopivaa tilaisuutta.
Tästä voisi olla seurauksena, että ruotsalaiset suuttuisivat ja
ryhtyisivät johonkin väkivaltaan kuninkaita vastaan, jotka olivat
tuollaisen kurjuuden saaneet aikaan.
Lopuksi ajatelkoot heidän majesteettinsa kuninkaallista nimeänsä
ja mainettansa, kirjeitänsä, uskollisuuttansa ja kunniaansa.
Varjelkoon Jumala kuninkaan sanaa olemasta niin löyhällä
perustuksella!

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