Multi-flow integration process safety management at steelmaking production site through wireless sensor network and AI-enabled data prediction

Abstract

Industrial production process includes complex and high-risk operational procedures. Maintaining process safety and stability is essential to prevent casualties, equipment damage, and asset loss. Traditional process safety management (PSM) on production site heavily relies on manual inspections and video surveillance which inevitably overlook some hidden risks. Therefore, this paper proposes a novel multi-flow integration PSM framework to overcome the trouble. Firstly, risk factors of the material flow (MF) and energy flow (EF) are systematically analyzed and organized based on the 4M1E method. The wireless sensor network (WSN) is established by deploying multiple sensors which facilitate the data flow (DF), including data perception, collection, transmission and aggregation of different risk variables. Additionally, the analysis model to extract the information flow (IF) is obtained by comparing prediction performance of different deep learning (DL) models. Finally, response strategies against potential dangers are formulated and the control flow (CF) across production layers are achieved relying on the circulation of safety directives, the execution of control measures and the feedback. The effectiveness of this framework is verified in a steel continuous casting scenario through the acquisition of DF, the extraction of IF and the loop of CF. Results indicates that the Bi-LSTM model can achieve the most outstanding prediction performance with relative root mean square error of 4.2428%, root mean square error of 0.0551 and R-square of 0.9816. This study can aid in advancing the digitization and intelligence of the PSM and providing a practical research perspective and application mode.