Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

Authors

  • Hui Liu Norinco Group Planning and Research Institute, Beijing, 100070, China
  • Guihao Zhang Investment Management Department,Chongqing Hongyu Precision Industry Group Co., Ltd.,Chongqing, 402760, China

DOI:

https://doi.org/10.4108/ew.6098

Keywords:

Process industries, Energy Optimization, Data analytics, Machine learning, Soft sensing, Control, Optimization, Reinforcement learning, High-level decision-making, Robust optimization

Abstract

This paper reviews the current state of research in data analytics and machine learning techniques, focusing on their applications in process industrial manufacturing, particularly in control and optimization. Key areas for future research include selection and transfer learning for process monitoring, addressing time-varying characteristics, and enhancing data-driven optimal control with domain-specific knowledge. Additionally, the paper explores reinforcement learning techniques and robust optimization, including distributional robust optimization, for high-level decision-making. Emphasizing the importance of historical knowledge of plants and processes, this paper aims to identify knowledge gaps and pave the way for future research in data-driven strategies for process industries, with a particular emphasis on energy efficiency and optimization.

Downloads

Download data is not yet available.

References

Shang C, You F. Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. Engineering. 2019;5(6):1010-6. DOI: https://doi.org/10.1016/j.eng.2019.01.019

Giret A, Trentesaux D, Prabhu V. Sustainability in manufacturing operations scheduling: A state of the art review. Journal of Manufacturing Systems. 2015;37:126-40. DOI: https://doi.org/10.1016/j.jmsy.2015.08.002

Ramachandran KM, Tsokos CP. Mathematical statistics with applications in R: Academic Press; 2020.

Datta S. Materials design using computational intelligence techniques: Crc Press; 2016. DOI: https://doi.org/10.1201/9781315373003

Datta S, Chattopadhyay P. Soft computing techniques in advancement of structural metals. International Materials Reviews. 2013;58(8):475-504. DOI: https://doi.org/10.1179/1743280413Y.0000000021

Kumar S. Neural networks: a classroom approach: Tata McGraw-Hill Education; 2004.

Kundu M, Ganguly S, Datta S, Chattopadhyay P. Simulating time temperature transformation diagram of steel using artificial neural network. Materials and Manufacturing Processes. 2009;24(2):169-73. DOI: https://doi.org/10.1080/10426910802612239

Goldberg D. Genetic Algorithm in Search, Optimization and Machine Learning, Pearson Education. Low Price Edition, Delhi. 2005.

Deb K. Multi-objective optimization using evolutionary algorithms: John Wiley & Sons; 2001.

Liu S, Cheng H. Manufacturing Process Optimization in the Process Industry. International Journal of Information Technology and Web Engineering (IJITWE). 2024;19(1):1-20. DOI: https://doi.org/10.4018/IJITWE.338998

Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence. 2013;35(8):1798-828. DOI: https://doi.org/10.1109/TPAMI.2013.50

Nair V, Hinton GE, editors. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on machine learning (ICML-10); 2010.

Ge Z, Song Z, Ding SX, Huang B. Data mining and analytics in the process industry: The role of machine learning. Ieee Access. 2017;5:20590-616. DOI: https://doi.org/10.1109/ACCESS.2017.2756872

Shang C, Huang B, Yang F, Huang D. Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling. AIChE Journal. 2015;61(12):4126-39. DOI: https://doi.org/10.1002/aic.14937

Shang C, Huang B, Yang F, Huang D. Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control. 2016;39:21-34. DOI: https://doi.org/10.1016/j.jprocont.2015.12.004

Shu Y, Ming L, Cheng F, Zhang Z, Zhao J. Abnormal situation management: Challenges and opportunities in the big data era. Computers & Chemical Engineering. 2016;91:104-13. DOI: https://doi.org/10.1016/j.compchemeng.2016.04.011

Yu J, Qin SJ. Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models. AIChE Journal. 2008;54(7):1811-29. DOI: https://doi.org/10.1002/aic.11515

He QP, Wang J. Statistical process monitoring as a big data analytics tool for smart manufacturing. Journal of Process Control. 2018;67:35-43. DOI: https://doi.org/10.1016/j.jprocont.2017.06.012

Brosilow C, Tong M. Inferential control of processes: Part II. The structure and dynamics of inferential control systems. AIChE Journal. 1978;24(3):492-500. DOI: https://doi.org/10.1002/aic.690240314

Shardt YA, Hao H, Ding SX. A new soft-sensor-based process monitoring scheme incorporating infrequent KPI measurements. IEEE Transactions on Industrial Electronics. 2014;62(6):3843-51. DOI: https://doi.org/10.1109/TIE.2014.2364561

Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Computers & chemical engineering. 2009;33(4):795-814. DOI: https://doi.org/10.1016/j.compchemeng.2008.12.012

Ma Y, Huang B. Bayesian learning for dynamic feature extraction with application in soft sensing. IEEE Transactions on Industrial Electronics. 2017;64(9):7171-80. DOI: https://doi.org/10.1109/TIE.2017.2688970

Ma Y, Huang B. Extracting dynamic features with switching models for process data analytics and application in soft sensing. AIChE Journal. 2018;64(6):2037-51. DOI: https://doi.org/10.1002/aic.16059

Zhong W, Jiang C, Peng X, Li Z, Qian F. Online quality prediction of industrial terephthalic acid hydropurification process using modified regularized slow-feature analysis. Industrial & Engineering Chemistry Research. 2018;57(29):9604-14. DOI: https://doi.org/10.1021/acs.iecr.8b01270

Gao X, Shang C, Jiang Y, Huang D, Chen T. Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal. 2014;60(7):2525-32. DOI: https://doi.org/10.1002/aic.14455

Li F, Zhang J, Shang C, Huang D, Oko E, Wang M. Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering. 2018;130:997-1003. DOI: https://doi.org/10.1016/j.applthermaleng.2017.11.078

Ge Z. Process data analytics via probabilistic latent variable models: A tutorial review. Industrial & Engineering Chemistry Research. 2018;57(38):12646-61. DOI: https://doi.org/10.1021/acs.iecr.8b02913

Yuan X, Ge Z, Ye L, Song Z. Supervised neighborhood preserving embedding for feature extraction and its application for soft sensor modeling. Journal of Chemometrics. 2016;30(8):430-41. DOI: https://doi.org/10.1002/cem.2811

Hassine H, Barkallah M, Bellacicco A, Louati J, Riviere A, Haddar M. Multi objective optimization for sustainable manufacturing, application in turning. International Journal of Simulation Modelling. 2015;14(1):98-109. DOI: https://doi.org/10.2507/IJSIMM14(1)9.292

Chu Y, You F. Model-based integration of control and operations: Overview, challenges, advances, and opportunities. Computers & Chemical Engineering. 2015;83:2-20. DOI: https://doi.org/10.1016/j.compchemeng.2015.04.011

Appino RR, Ordiano JÁG, Mikut R, Faulwasser T, Hagenmeyer V. On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages. Applied energy. 2018;210:1207-18. DOI: https://doi.org/10.1016/j.apenergy.2017.08.133

Saltık MB, Özkan L, Ludlage JH, Weiland S, Van den Hof PM. An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects. Journal of Process Control. 2018;61:77-102. DOI: https://doi.org/10.1016/j.jprocont.2017.10.006

Shang C, You F. A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control. 2019;75:24-39. DOI: https://doi.org/10.1016/j.jprocont.2018.12.013

Shang C, Chen W-H, Stroock AD, You F. Robust model predictive control of irrigation systems with active uncertainty learning and data analytics. IEEE transactions on control systems technology. 2019;28(4):1493-504. DOI: https://doi.org/10.1109/TCST.2019.2916753

Rosolia U, Zhang X, Borrelli F. Data-driven predictive control for autonomous systems. Annual Review of Control, Robotics, and Autonomous Systems. 2018;1:259-86. DOI: https://doi.org/10.1146/annurev-control-060117-105215

Shapiro A, Dentcheva D, Ruszczynski A. Lectures on stochastic programming: modeling and theory: SIAM; 2021. DOI: https://doi.org/10.1137/1.9781611976595

Ben-Tal A, El Ghaoui L, Nemirovski A. Robust optimization: Princeton university press; 2009. DOI: https://doi.org/10.1515/9781400831050

Delage E, Ye Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations research. 2010;58(3):595-612. DOI: https://doi.org/10.1287/opre.1090.0741

Bertsimas D, Thiele A. Robust and data-driven optimization: modern decision making under uncertainty. Models, methods, and applications for innovative decision making: INFORMS; 2006. p. 95-122. DOI: https://doi.org/10.1287/educ.1063.0022

Campi MC, Garatti S. The exact feasibility of randomized solutions of uncertain convex programs. SIAM Journal on Optimization. 2008;19(3):1211-30. DOI: https://doi.org/10.1137/07069821X

Birge JR, Louveaux F. Introduction to stochastic programming: Springer Science & Business Media; 2011. DOI: https://doi.org/10.1007/978-1-4614-0237-4

Shang C, Huang X, You F. Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering. 2017;106:464-79. DOI: https://doi.org/10.1016/j.compchemeng.2017.07.004

Ning C, You F. Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods. Computers & Chemical Engineering. 2018;112:190-210. DOI: https://doi.org/10.1016/j.compchemeng.2018.02.007

MacGregor J, Bruwer M, Miletic I, Cardin M, Liu Z. Latent variable models and big data in the process industries. IFAC-PapersOnLine. 2015;48(8):520-4. DOI: https://doi.org/10.1016/j.ifacol.2015.09.020

Shu Y, Zhao J. Data-driven causal inference based on a modified transfer entropy. Computers & Chemical Engineering. 2013;57:173-80. DOI: https://doi.org/10.1016/j.compchemeng.2013.05.011

Qin SJ. Survey on data-driven industrial process monitoring and diagnosis. Annual reviews in control. 2012;36(2):220-34. DOI: https://doi.org/10.1016/j.arcontrol.2012.09.004

Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on knowledge and data engineering. 2009;22(10):1345-59. DOI: https://doi.org/10.1109/TKDE.2009.191

Wang R, Edgar TF, Baldea M, Nixon M, Wojsznis W, Dunia R. A geometric method for batch data visualization, process monitoring and fault detection. Journal of Process Control. 2018;67:197-205. DOI: https://doi.org/10.1016/j.jprocont.2017.05.011

Kadlec P, Grbić R, Gabrys B. Review of adaptation mechanisms for data-driven soft sensors. Computers & chemical engineering. 2011;35(1):1-24. DOI: https://doi.org/10.1016/j.compchemeng.2010.07.034

Morariu O, Morariu C, Borangiu T, Răileanu S. Manufacturing systems at scale with big data streaming and online machine learning. Service Orientation in Holonic and Multi-Agent Manufacturing: Proceedings of SOHOMA 2017. 2018:253-64. DOI: https://doi.org/10.1007/978-3-319-73751-5_19

Geiger A, Lenz P, Urtasun R, editors. Are we ready for autonomous driving? the kitti vision benchmark suite. 2012 IEEE conference on computer vision and pattern recognition; 2012: IEEE. DOI: https://doi.org/10.1109/CVPR.2012.6248074

Duchesne C, Liu J, MacGregor J. Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems. 2012;117:116-28. DOI: https://doi.org/10.1016/j.chemolab.2012.04.003

Chen M, Khare S, Huang B. A unified recursive just-in-time approach with industrial near infrared spectroscopy application. Chemometrics and Intelligent Laboratory Systems. 2014;135:133-40. DOI: https://doi.org/10.1016/j.chemolab.2014.04.007

Liu D, Yang X, Wang D, Wei Q. Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints. IEEE transactions on cybernetics. 2015;45(7):1372-85. DOI: https://doi.org/10.1109/TCYB.2015.2417170

Duan Y, Chen X, Houthooft R, Schulman J, Abbeel P, editors. Benchmarking deep reinforcement learning for continuous control. International conference on machine learning; 2016: PMLR.

Mattera G, Caggiano A, Nele L. Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing. Journal of Intelligent Manufacturing. 2024:1-20. DOI: https://doi.org/10.1007/s10845-023-02307-w

Wiesemann W, Kuhn D, Sim M. Distributionally robust convex optimization. Operations research. 2014;62(6):1358-76. DOI: https://doi.org/10.1287/opre.2014.1314

Downloads

Published

04-06-2024

How to Cite

1.
Liu H, Zhang G. Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing . EAI Endorsed Trans Energy Web [Internet]. 2024 Jun. 4 [cited 2024 Jul. 13];11. Available from: https://publications.eai.eu/index.php/ew/article/view/6098