Fuzzy controlled mine drainage system based on embedded system

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1365
Fuzzy controlled mine drainage system
based on embedded system
Deepak B
Assistant Professor,Department of Mechatronics,PPG institute of Technology, Coimbatore, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract— In mines, proper drainage should be
provided to improvestability,ensuresafetytoworkers,
reduce corrosion of mining equipments. But in mines,
the variables to be controlled are varies in a random
manner and they are not linear and certain. Because of
the unpredictable nature of the variables it is not
possible to design the empirical model accurately. This
proposed system is a combinationoffuzzylogiccontrol
and the electronicsembeddedsystem. Fuzzylogicdeals
with the uncertainties in the system and the embedded
system provide the better control, flexibility,
compactness and user-friendliness to the system.
Keywords- Fuzzy control; Embeddedsystem;
Mine drainage.
INTRODUCTION
In general terms, mining operations below a
particular level changes the hydraulic gradient, thus
affecting the groundwater and surface water flow. As a
consequence, flow of water may be induced from the
surroundingrockmasstowardstheminingexcavations
which may necessarily require pumping of large
quantities of water from mines.
The presenceofwaterinminingsitescreates arange
of operational and stability problems and requires
drainage to be carried out from the mine workings in
order to improve slope stability, avoid oxidation of
metallic sulphidesandreducecorrosionofminingplant
and equipment. It may affect the safety of workersalso.
The quality and quantity ofthedrainagewaterdepends
on a series of geological, hydro geological and mining
factors which can vary significantly from one mine to
another.
In mine drainage control system, the numbers of
working pumps are defined according to parameters
such as water level and its change rate. But it is difficult
to establish the empirical model precisely, because the
variables mentioned above are non-linear variable
parameters. Classical control has not been able to
satisfy the high accuracy of the control request. So in
this, combination of the intelligent control with the
microcontrollers is proposed. The fuzzy control
transforms the control policy indicated by the human
natural language into the digital or mathematical
function through the fuzzy set and the fuzzy inference,
and then uses the computer to realize the
predetermined control.
Combination of fuzzy controltheorywithembedded
system and applying it into mine drainage control
system not only solves the difficult problem that the
water drainage system is difficult to establish the
mathematical model but also raises the control
system's automated level. Since the embedded system
is reactive and real time constrained it gives better
control on drainage system.
Since the mines are also sensitive to different
parameters like temperature, humidity and pressure
etc.theseparametersarecontinuouslymonitoredusing
the respective sensors and the collected data is send to
the remote operator through a gsm modem for further
analysis.
FUZZY LOGIC CONTROL THEORY
Fuzzy control provides a formal methodology for
representing, manipulating, and implementing a
human’s heuristic knowledge about how to control a
system. Fuzzy logic deals with uncertainty in
engineering by attaching degrees of certainty to the
answer to a logical question.

The basic idea behind fuzzy logic control is to
incorporate the ‘expert experience’ of a human
operator in the design of a controller in controlling a
process whose input-output relationship is described
by a collection of fuzzy control rules (e.g. IF-THEN
rules) involving linguistic variables. This utilization of
linguistic variables, fuzzy control rules, and
approximate reasoning provides a means to
incorporate human expert experience in designing the
controller.
Fuzzy logic is not the answer to all technical
problems, but for control problems where simplicity
and speed of implementation is important then fuzzy
logic is a strong candidate. It combines the computer
based on control policies that are summarized by
operator experience andareexpressedbyusinghuman
language as well as the control rules which are
summarized through massive actual operational data.
Useful cases of fuzzy logic are:
-The controlprocessesaretoocomplextoanalyzeby
conventional quantitative techniques.
-The available sources of information are
interpreted qualitatively, inexactly, or
uncertainly.
The advantages which make the fuzzy control a
better choice are:
 Flexible
 Convenient user interface.
 Easy computation.
 Combine regulation algorithms and logic
reasoning allowing for integrated control
schemes.
 Can use multiple inputs and outputs sources.
 Very quick and cheaper to implement and can
be easily modified.
Main components of fuzzy controller are shown in
Fig. 1.
 The fuzzification interface: transforms input
crisp values into fuzzy values.
 The fuzzy rule base: contains knowledge of the
application domain and the control goals.
 The fuzzyinferenceengine:performsinference
for fuzzy control actions.
 The defuzzification interface.
Figure 1. Components of fuzzy controller
The input values of the drainage control system are
deviation (d) of water level and rate of change of
deviation (dl). The general structure of a fuzzy
controller is given in Fig.2.
The controller given in Fig.2 is a double input single
output fuzzy controller. It has two inputs and can reflect the
dynamic characteristics of the output variable accurately in
the control process. The fuzzy controller includes the input
value fuzzy, the fuzzy inference and the third part clarifying
processing.
Fuzzy Inference
Engine
Fuzzification
Defuzzification
Input values
Output
values
Fuzzy Rule base

In Fig.2 the d and dl are the two input values whichdenote
the deviation and rate of change of deviation respectively. D
and DLare the fuzzy quantityafterthefuzzificationprocessof
d and dl respectively. V is the fuzzy control quantity and v is
the precise quantity of V afterclarifyingprocess.KdandKdlare
the fuzzy quantification factor of the d and dl respectivelyand
KV is the proportionality factor of v.
r
-
d dl
D DL
V v y y
Figure 2 . General structure of a fuzzy controller
Design procedure of fuzzy controller consists of
following steps
 Identify the inputs and their ranges and name
them.
 Identify the outputs and their ranges and
name them.
 Construct the rule base that the system will
operate under.
 Decide how the action will be executed by
assigning strengths to the rules.
 Combine the rules and defuzzify the output.
Water level d and rate of change of water level dl are
the input language variable to the fuzzy controller. The
output language variable is the number of waterpumps
that are going to run which is denoted as v.
We select [0,2] as the basic range of argumentation
of d and five 5 fuzzy subsets: AL (very low), BL ( low),
CL (middle), DL (high), EL(veryhigh)tocoverthebasic
range of argumentation of d . We select [0, 0.1] as the
basic range of argumentation of dl and three fuzzy
subsets: DR (drop), ST (stable), RS (rise) to cover the
basic range of argumentation of dl . We select [0, 3] as
the basic range of argumentation of u and five fuzzy
subsets: APN (all stop), SPN (open one), DPN (open
two), TPN (open three) , APN(open four) to cover the
basic range of argumentation of v .
ADDITIONAL SYSTEMS
Temperature monitoring sysytem
A thermistor is made use of in measuring the
temperature changes, relying on the change in its
resistancewithchangingtemperature. Therelationship
between the resistance and temperature is assumed to
be linear with
ΔR = kΔT
Where
ΔR = change in resistance
ΔT = change in temperature
k = first-order temperature coefficient of
resistance
Humidity monitoring sysytem
To ensure safe working environment in mines the
humidity content must be within the specified limit
prescribed by the industrial standards. So a humidity
sensors and monitoring system is included within the
proposed solution.
Pressure monitoring system
The pressure variations may also cause various
hazards in mines. So a pressure sensor is also
incorporated in the proposed system.
Fuzzy
quantification
factor, Kd
Controlled
function
KVFuzzy controller
Fuzzy
quantification
factor, Kdl

BLOCKDIAGRAM
To all sections
REALIZATION ON EMBEDDED
SYSTEM
In the past, several chips in separate packages
where required to configure a system. Now, just one
system on-chip can replace all of these, dramatically
reducing the packaging cost. Embedded system is any
electronic equipmentbuiltinintelligenceanddedicated
software.
It has several advantages than the current systems.
The integration of various ICs shortens the traveling
route and time of data to be transmitted resulting in
higher performance and also eliminates buffers and
other interfacecircuits.Asthenumberofcomponentsis
reduced, less power will be consumed. It is very
Micro
Controller
Amplifier
Float Sensor
Temperature
Sensor
Humidity
Sensor
Amplifier
Amplifier
LCD Display
Driver & Relay
Circuit
Driver & Relay
Circuit
Driver & Relay
Circuit
P
u
m
p
P
u
m
p
P
u
m
p
POWER SUPPLY
GSM
Modem
Pressure
Sensor
Amplifier

reactive, real time constrained, slimmer and more
compact: Housed in a single separate package, the chip
is smaller in size and therefore occupies less space on
the PCB. Hence products using embedded system are
slimmer and more compact.
Embedded system along with the Fuzzy logic
provides an efficient drainage system for mines. It has
morecontrolonthedrainageprocessinmines. Itmakes
the system more automatic. Embedded system gives a
better control scheme for the system.
CONCLUSION
Since the coal mine water control system are non-
linear, complex and difficult to establish the
mathematical model this article applies the fuzzy
control theory intothesystemandsuccessfulsolvesthe
problem which the traditional control cannot.
Meanwhile combining embedded system and the fuzzy
control, realizing the fuzzy controlstrategythroughthe
software, thus obtaining the water level ideal control.
REFERENCE
[1] ‘STUDY OF THE MINE DRAINAGE FUZZY CONTROL SYSTEM BASED ON
MATLAB’ BY GUIMEI WANG AND HUI SONG , HEBEI UNIVERSITY OF
ENGINEERING, CHINA.
[2] ‘Mine drainage and water resources’ by roger james,
national association of mining history organization at darely
dale.
[3] ‘ADAPTIVE NEUROFUZZY CONTROLLER TO REGULATE UTSG WATER
LEVEL IN NUCLEAR POWER PLANTS’ BY SUDATH R. MUNASINGHE,
MEMBER, IEEE, MIN-SOENG KIM, STUDENT MEMBER, IEEE, AND JU-JANG
LEE, SENIOR MEMBER, IEEE - IEEE TRANSACTIONS ON NUCLEAR SCIENCE,
VOL. 52, NO. 1, FEBRUARY 2005
[4] ‘Water level forecasting through fuzzy logic and artificial
neural network approaches’ by s. Alvisi, g. Mascellani, m.
Franchini, and a. Bardossy. University of stuttgart,
deutschland. Paper published in hydrology and earth system
sciences, 2005.
[5]. ‘Liquid level control by using fuzzy logic controller’
dharamniwas , aziz ahmad,varunredhuandumeshgupta.Al-
falah school of engineering & technology, dhauj, faridabad.
International journal of advances in engineering &
technology, july 2012.
[6]. ‘Control of groundwater in surface mining’ byc.obrawner,
consulting geographical engineerand professor,miningdept.
University of b.c. , canada.
[7]. ‘Water level control by fuzzy logic and neuralnetworks’by
daniel wu, fakhreddine karrayand insop song . University of
waterloo, waterloo.
[8] ‘Designing a fuzzy system with thefuzzysystem designer’ in
pid and fuzzy logic toolkit user manual.
Deepak B received the BTech
degree from the Department of
Electronics and Communication
Engineering in MEA Engineering
College(Calicut University) Kerala,
India in 2010 and ME degree from
the Department of Mechatronics in
Karpagam College of Engineering (Anna University)
Coimbatore , India in 2013.
He is currently working as an Assistant
Professor in the Department of Mechatronics with PPG
Institute of Technology(AnnaUniversity),Coimbatore,
India. His research interests include Mechatronics,
Robotics, and sensors.

Fuzzy controlled mine drainage system based on embedded system

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Fuzzy controlled mine drainage system based on embedded system