DMGAN: Adversarial Learning-Based Decision Making for Human-Level Plant-Wide Operation of Process Industries Under Uncertainties

To achieve plant-wide operational optimization and dynamic adjustment of operational index for an industrial process, knowledge-based methods have been widely employed over the past years. However, the extraction of knowledge base is a bottleneck for most existing approaches. To address this problem, we propose a novel framework based on the generative adversarial networks (GANs), termed as decision-making GAN (DMGAN), which directly learns from operational data and performs human-level decision making of the operational indices for plant-wide operation. In the proposed DMGAN, two adversarial criteria and three cycle consistency criteria are incorporated to encourage efficient posterior inference. To improve the generalization power of a generator with an increasing complexity of the industrial processes, a reinforced U-Net (RU-Net) is presented that improves the traditional U-Net by providing a more general combinator, a building block design, and drop-level regularization. In this article, we also propose three quantitative metrics for assessing the plant-wide operation performance. A case study based on the largest mineral processing factory in Western China is carried out, and the experimental results demonstrate the promising performance of the proposed DMGAN when compared with decision-making based on domain experts.

I. Introduction

With the development of process industries, their operation becomes more challenging due to the presence of uncertainty, nonlinearity, and complex dynamics in industrial processes. In plant-wide operations, the dynamic adjustment of the operational indices of different process units in a plant requires high-level technical and domain knowledge, which hinders the development of process industries toward a green and intelligent direction [1]–[4]. Therefore, the role of intelligence systems for knowledge modeling and decision making of plant-wide operations has received considerable attention from both academics and industrial practitioners.