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

Hui Liu; Guihao Zhang

doi:10.4108/ew.6098

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.

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