INTELLIGENT MANGEMENT OF ENERGY SYSTEM IN MANUFACTURING PROCESS USING DATA-DRIVEN STOCHASTIC MODEL

Shokhjakhon Abdufattokhov; Toir Makhamatkhujaev

doi:10.5281/zenodo.10001543

INTELLIGENT MANGEMENT OF ENERGY SYSTEM IN MANUFACTURING PROCESS USING DATA-DRIVEN STOCHASTIC MODEL

13.10.2023 International Scientific Journal "Science and Innovation". Series A. Volume 2 Issue 10

Shokhjakhon Abdufattokhov, Toir Makhamatkhujaev

Abstract. To overcome the environmental impacts of a manufacturing factory over its life cycle, the role of sustainable energy effectiveness is vital. For this reason, implementing energy conservation technologies to empower energy efficiency has become an essential business for most manufacturing plants. Data-driven control setups are a novel idea to handle the energy efficiency of such complex systems, while machine learning is becoming well-known in the system engineering community. In this paper, a new approach together with optimal control application is considered to open promising energy-saving ideas through investigating machines of a factory using machine learning, specifically, Gaussian Processes (GP), where the model is built by correlating the dynamics, complexity, and interrelated energy consumption recordings. We connect the idea with controlling a manufacturing system energy in an optimized way, where the Model Predictive Control loop delivers optimal solutions for each control time step. In the end, a numerical example is demonstrated to give a clear picture of the proposed modelling method's potential.

Keywords: gaussian processes, machine learning, model predictive control, stochastic model, sustainable manufacturing

References:

1. Abdufattokhov Sh., Muhiddinov B., Stochastic Approach for System Identification using Machine Learning, International Conference on Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, IEEE proceedings, 2019. 2. Dhankhar, and K. Solanki. A comprehensive review of tools and techniques for big data analysis. International Journal of Emerging Trends in Engineering Research, Vol. 7, No. 11, pp. 556-562, 2019 3. Herrmann C, Thiede S. Process chain simulation to foster energy efficiency in manufacturing, CIRP Journal of Manufacturing Science and Technology, Volume 1, Issue 4, pp. 221-229, 2009, https://doi.org/10.1016/j.cirpj.2009.06.005. 4. Hesselbach J, Herrmann C, Detzer R, Martin L, Thiede S, Lüdemann B. Energy Efficiency through optimized coordination of production and technical building services, 15th CIRP International Conference on Life Cycle Engineering, 17-19 March 2008. 5. Devoldere T, Dewulf W, Deprez W, Willems B, Duflou JR. Improvement Potential for Energy Consumption in Discrete Part Production Machines, 15th CIRP International Conference on Life Cycle Engineering, 2007, https://doi.org/10.1007/978-1-84628-935-4\_54. 6. Devoldere T, Dewulf W, Deprez W. Energy related life cycle impact and cost reduction opportunities in machine design: the laser cutting case, 15th CIRP International Conference on Life Cycle Engineering, 2008. 7. Herrmann C, Bogdanski G, Zein A. Industrial Smart Metering–Application of Information Technology Systems to Improve Energy Efficiency in Manufacturing, 43rd CIRP International Conference on Manufacturing Systems, 2010. 8. Magori B, Yashiro T, Hiroshi S. Development research of real-time monitoring and optimized control for energy conservation and CO2 reduction of existing buildings, proceedings of World SB14 Barcelona, 2014. 9. Bauerdick C JH, Helfert M, Menz B, Abele E. A Common Software Framework for Energy Data Based Monitoring and Controlling for Machine Power Peak Reduction and Workpiece Quality Improvements, Procedia CIRP, Volume 61, pp. 359-364, 2017, https://doi: 10.1016/j.procir.2016.11.226. 10. S. Abdufattokhov, K. Ibragimova, M. Khaydarova, A. Abdurakhmanov, , “Data-Driven Finite Horizon Control Based On Gaussian Processes And Its Application To Building Climate Control,” International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 13, no. 2, pp. 40-47, 2021. 11. David Sturzenegger, Dimitrios Gyalistras, Manfred Morari, and Roy Smith. Model predictive climate control of a swiss office building: Implementation, results, and cost–benefit analysis. IEEE Transactions on Control Systems Technology, 24(1), pp. 1-12, 2016, https://doi: 10.1109/TCST.2015.2415411. 12. Abdufattokhov Sh., Muhiddinov B., Probabilistic Approach for System Identification using Machine Learning, International Conference on Information Science and Communications Technologies (ICISCT), IEEE proceedings, 2019, https://doi: 10.1109/ICISCT47635.2019.9012025. 13. D. A. Petrosov, R. A. Vashchenko, A. A. Stepovoi, and N. V. Petrosova, Application of artificial neural networks in genetic algorithm control problems, International Journal of Emerging Trends in Engineering Research, vol. 8, no. 1, pp. 177–181, 2020. https://doi.org/10.30534/ijeter/2020/24812020 14. Galiana, F., Handschin, E., and Fiechter, A., Identification of stochastic electric load models from physical data, Automatic Control, IEEE Transactions 19(6), pp. 887-893, 1974, https://doi: 10.1109/TAC.1974.1100724. 15. Thompson, K.: Implementation of Gaussian process models for non-linear system identification, PhD thesis, University of Glasgow, Glasgow, 2009. 16. Dong, B., Cao, C., and Lee, S. E., Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings 37(5), p. 545-553, 2005, https://doi.org/10.1016/j.enbuild.2004.09.009. 17. Quinonero-Candela, J., C.E. Rasmussen. Analysis of Some Methods for Reduced Rank Gaussian Process Regression. In: Murray-Smith, R. and R. Shorten (Eds.), ``Switching and Learning in Feedback Systems", Lecture Notes in Computer Science, Vol. 1, p. 33-55, 2005, https://doi.org/10.1007/978-3-540-30560-6\_4. 18. Solak, E., R. Murray-Smith, W.E. Leithead, D.J. Leith, and C.E. Rassmusen, Derivative observations in Gaussian Process Models of Dynamic Systems. In: Becker, S., S. Thrun, K. Obermayer (Eds.), ``Advances in Neural Information Processing Systems", MIT Press, pp. 529-536, 2003, https://doi.org/10.1145/1330598.1330647. 19. Rasmussen, C. E. and Williams, C. K. I., Gaussian processes for machine learning, Vol.1, MIT press, Cambridge, MA, 2006. 20. Jus Kocijan., Modelling and control of dynamic systems using Gaussian process models, Springer, 2016. 21. D. Angeli and J. Rawlings, Economic optimization using model predictive control with a terminal cost. Annual Reviews in Control, 35(2), pp.178-186, 2011, https://doi: 10.1016/j.arcontrol.2011.10.011. 22. M. Diehl, G. Bock, J. Schloder, R. Findeisen, Z. Nagy, and F. Allgöwer, Real-time optimization and nonlinear model predictive control of processes governed by differential–algebraic equations. Journal of Process Control, 12(4), pp. 577-588, 2002. 23. B. Kouvaritakis, M. Cannon, P. Couchman, MPC as a tool for sustainable development integrated policy assessment. IEEE Trans. Autom. Control, 51(1), pp. 145-149, 2006, https://DOI: 10.1109/TAC.2005.861702. 24. P. Patrinos, S. Trimboli, A. Bemporad, Stochastic MPC for real-time market-based optimal power dispatch, in Proceedings of the 50th Conference on Decision and Control, Orlando, USA, pp. 7111-7116, 2011, https://doi:10.1109/CDC.2011.6160798.