Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

TLDR

Smart manufacturing is essential for improving quality in the process industry, yet green manufacturing faces major safety obstacles from hazardous chemicals and stringent regulations, making AI a promising solution. The study identifies and discusses key technical challenges in process safety—scarce labeling, knowledge‑based reasoning, heterogeneous data fusion, and dynamic risk assessment. State‑of‑the‑art AI methods are employed to address these challenges within the complex safety relations of the process industry.

Abstract

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.