Machine learning: Overview of the recent progresses and implications for the process systems engineering field. (9th June 2018)

• Record Type: Journal Article
• Authors: Lee, Jay H.; Shin, Joohyun; Realff, Matthew J.
• Is Part Of: Computers & chemical engineering. Volume 114(2018)
• Page Start: 111
• Page End: 121
• Publication Date: 2018-06-09
• Subjects: Machine learning -- Deep learning -- Reinforcement learning -- Process systems engineering -- Stochastic decision problems; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.10.008
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Recent advances in deep learning and reinforcement learning (RL) are reviewed. Motivation, early problems and recent resolutions of deep learning are discussed. The idea of RL and its success in the Go game ( a la AlphaGo) are introduced. Applicability of RL to multi-stage decision problems in industries is discussed. Potential applications and research directions of ML in the PSE domains are given. Abstract: Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.