Proof energy@work midih oc2-demo_day
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The experiment aimed to monitor energy and gas consumption on an aircraft parts production line. Sensors were installed to measure consumption of autoclaves and machines. Apache Flink processed streaming data and Orion stored it. Knowage visualized trends and provided indicators. The system identified optimization rules, applying a new recipe reducing energy 11.12%, gases 12.92%, and production time 8%, meeting KPI targets. COVID-19 impacted sensor installation but rules still provided decision support.
Proof energy@work midih oc2-demo_day
- 1. PROOF EXPERIMENT RESULTS Speaker: Giuseppe Mastandrea (E@W)
- 3. Who We Are RELEVANT NUMBERS Innovative SME R&D unit composed by 6 researchers Wide experience in energy efficiency, ICT, Automation and Control, Artificial Intelligence, Communication Protocols, Demand Response 5 ongoing H2020 projects (inteGRIDy, eDREAM, RE-COGNITION, POCITYF, iPRODUCE) 2 I4MS Experimentations (MIDIH, Cloudifacturing) Energy@Work is an innovative Italian SME, active in intelligent ICT and Artificial Intelligence technologies for energy efficiency in buildings energy grids and industry
- 4. AS IS SCENARIO AND CHALLENGES
- 5. Experimental Facility Production site of a company that produce hybrid composite material parts for the aeronautical sector located in Brindisi (Apulia, IT)
- 6. Production process ROLLING Moulds produced internally Hybrid composite material fibre (Kevlar Fiberglass) AUTOCLAVE (polymerization) MOULDS REMOVAL (manual process) DEBURRING, ASSEMBLY AND PREPARATION PAINTINGQUALITY CHECK air conditioning system conduits ATR 72 aircraft PROOF has been validated and carried out on the production line for ATR 72 aircraft air conditioning conduits systems - NAMS modification
- 7. Available functionalities and HW/SW Infrastructure 1. Monitoring of the polymerization process in the autoclave: only the initial temperature, the final temperature, the descent and ascent temperature rate and the pressure to be maintained inside the autoclave are indicated by the PLC
- 8. Available functionalities and HW/SW Infrastructure 2. Monitoring of the overall propane and nitrogen consumptions in the company through analog meter, without the possibility to monitor the specific polymerization process
- 9. Experiment Challenges • To monitor the energy consumptions on autoclaves and on numerical control machines • To monitor nitrogen and propane consumption on autoclave polymerization processes • To develop a Decision Support System able to put in relation already available polymerization process information (temperature and pressure trends) with the new gathered data to optimize the “recipes” for polymerization in the autoclaves taking into account the relative costs • To raise awareness about the costs for the production of components with the aim to enable a better evaluation of their sales prices
- 10. TO BE SCENARIO AND EXPERIMENT OVERVIEW
- 11. Experiment Overview MIDIH Components Field Sensors E@W CPS Gateway PROOF experiment aims at the realization of a “plug-and-play” sustainability monitoring system to address a global assessment of energy and gas consumption on the production line of hybrid composite material parts for the aeronautical sector. The system is composed of sensors which enable the gathering of data related to gas and electricity consumption collected through the E@W Smart Gateway. MIDIH reference architecture has been exploited for data gathering (Orion Context Broker), advanced data processing (Apache Flink) and visualization (Knowage).
- 12. EXPERIMENT ARCHITECTURE AND TECHNICAL DETAILS
- 13. Experiment IT Architecture • PROOF CPS Gateway, based on EPO Gateway, modular, scalable and compliant with the most common wireless and wired IoT protocols (MODBUS RTU over RS485 has been used during this experimentation) • PROOF IoT Middleware and Device Management that manage the interfacing with the cloud by a couple of closely cooperating components: FIWARE IDAS Backend Agent and FIWARE ORION Context Broker facilitating the communication with the cloud services for the storage and data processing; • PROOF Event-Data Processing that manage the storage of data and the stream data processing thanks to Apache Flink; • PROOF HMI advanced visual analytics, based on FIWARE KNOWAGE, to enable data visualization, trends visualization and provide indicators to the production operator.
- 14. New data gathered Variable Description Machine/equipment involved Type EA1 Amount of energy consumed by the first autoclave A1 energy efficiency PA1 Amount of propane consumed by the first autoclave A1 production process NA1 Amount of nitrogen consumed by the first autoclave A1 production process EA2 Amount of energy consumed by the second autoclave A2 energy efficiency ENMC1 Amount of energy consumed by the first numerical control machines NMC1 energy efficiency ENMC2 Amount of energy consumed by the second numerical control machines NMC2 energy efficiency ENMC3 Amount of energy consumed by the third numerical control machines NMC3 energy efficiency
- 15. PROOF CPS Gateway • Interfaces with field devices (sensors and eventual actuators) • Sends data via cloud-based interface software in secure mode for data storage and the implementation of the control strategies (support of REST API and MQTT) • Local Database and Real time local analysis engine to optimize the computing infrastructure by distributing load between the cloud and fog nodes/gateways in an intelligent way • Supports state-of-the-art security standards, such as SSL/TLS for transport security that allow an end-to-end secure communication providing authentication, integrity and encryption of the data above the transport layer. • Hardware security thanks to Trusted Platform Module on board (Zymbit security Module)
- 16. PROOF IoT Middleware and Device Management Orion Context Broker has been used to store data from field devices. Each sensor has been associated with a Fiware Entity, which keeps track of all information related to the devices (e.g. last measurement, average consumption, consumption in the last period, etc ...). These Data Entity are updated with each new measurement thanks to communication with the system database (PROOF Data Management and Storage module) and Apache Flink.
- 17. PROOF Event Data Processing Apache Flink has been used as a stream data processing tool. In particular, DataStream API has been used to process data from the field. Each data flow has been processed by implementing specific operations (e.g., calculation of the average and total consumption in a specific time window). The processed data has been reported on Orion Context Broker via HTTP POST in order to update the relative Entity.
- 18. PROOF HMI advanced visual analytics FIWARE KNOWAGE has been used to visualize the real time data coming from the field and to provide data analytics and message for decision support to the operators. In particular, all the data about of the energy and gas consumptions of the machineries involved by the experimentation are displayed to the operator providing indicator on the base of their elaboration and reporting whether the consumptions are in line with what is expected or not. Furthermore, for all types of measurements, it is possible to view a graph to see the trend in a specific time window with a summary table that includes the daily consumption (with relative alerts) and the last measurements gathered.
- 19. Experiment Report The main objective of the experiment is to provide decision support to the production managers to improve their awareness of energy and gas consumptions of the production process and the relationship with the production costs. Thanks to PROOF the production operator can: • Obtain online information on the energy and gas consumption trends of autoclaves and numerical control machines • Consult online gas consumption and check the effects of polymerization "recipes" in order to evaluate the definition of new recipes and better positioning of components in the autoclave having a better understanding of the costs related to the gas and energy consumptions related to the production process
- 20. COVID-19: Risks management • A subset of sensors has been installed with respect to what expected at the beginning of the project due the difficulties related the spread of COVID-19 (sensor supply companies closed during the lockdown period, specific new procedures to be followed for the installation of sensors, increasement of the installation costs, …) • A simplified set of rules together with the company domain experts has been defined with the aim to provide decision support to the production managers even in the absence of a consolidated historical data (to train and validate the algorithms – ongoing for future use) due to the unexpected and unavoidable delays in the installation of sensors.
- 21. KPIs AND BUSINESS IMPACT
- 22. Validation Process • The validation resulted in the full monitoring of a six hours duration production cycle concerning the polymerization process of twenty components of conduits for air conditioning systems for ATR 72 aircraft • Offline data analysis and processing has been carried out with domain expert staff identifying a set of rules associated with corrective actions to be implemented in case of specific events during the production of the same components • The system, as configured after the offline data analysis was tested on the production of the same components resulting in the application of new “recipe” and components arrangement inside the autoclave • On the base of the validation process results it was possible to evaluate the experiment KPI
- 23. KPIs (1) ID Typology Description Target Value Evaluation KPI1 Technical Usage of MIDIH compliant software components At least 3 Fiware Orion Context Broker, Apache Flink and Fiware Knowage are part of the IT Architecture and have been successfully integrated, tested and validated during the experimentation KPI2 Technical Electrical/gas consumption reduction 10% The experiment has resulted in a reduction of 11,12% of energy consumption and 12,92% of the gases consumptions KPI3 Technical CO2 reduction 10% Objective achieved. Directly related to the KPI3. The CO2 emission intensity (kg CO2/kWh) has been calculated using a coefficient that quantified the kg CO2 per kWh (0,256 for Italy). https://www.eea.europa.eu/ KPI4 Technical Time process improvement 8% Comparing the production cycle before and after the application of the new rules (that have set changes in some internal parameters of the production process), it has been obtained improvements in terms of reduction of production time equal to 9,14%
- 24. KPIs (2) ID Typology Description Target Value Evaluation KPI5 Technical Waste parts/products reduction 4% Since the entry into operation of PROOF, on the pilot site, no damage has been noticed (no polymerization process has been compromised) so the waste and the "no-quality product" due to process malfunction can be considered as zero KPI6 Business Attractive solution for the final user >90% Questionnaire has been submitted to the production site staff obtaining an indicator on the end-user satisfaction equal to 94%. KPI7 Business Number of potential partners/customers 2 Apart from the test site, two other companies, Innovaction Soc. Cop (IT) and GLN S.A.(PT), have expressed interest in a built solution.
- 25. PROOF Benefits • Increase in production quality. The analysis of the relations between the already available polymerization process information (temperature and pressure trend) and the energy and gas consumption monitoring allows the optimization of the polymerization "recipes" and the arrangement of the components in the autoclaves in consideration of the cost of the process; • Reduction of production cost. Data collecting from the manufacturing chain allows the continuous monitoring of the factory and the identification of production anomalies and bottlenecks, and so that reduce the cost related to the process energy and gas consumptions; • Decision support interface. Report of data gathered and their analytics allows a better evaluation of the decision to be done • Increase of product margin benefits. Thanks to the reduction of the production costs and the improvement of the company awareness of the components production costs, a better evaluation of the components sales prices it will be possible, increasing the margin benefit or study the possibility to offer a more competitive price in the market • Easy to use. Implying a reduction in the training time of employees
- 26. Business Impact Energy@Work is oriented to exploit and propose PROOF based services for two Revenue Streams: RS1: E@W Ind-IoT for energy and environmental monitoring in process industries RS2: E@W iCore for industrial process optimization and advanced decision making Preliminary business plan has been depicted as a starting point to evaluate the potential revenues generated by the exploitable results from PROOF, and therefore to orient the E@W board for further investments.
- 27. THANK YOU!