The least-time optimization for abnormity control of a complex industrial process

Abstract

Modern complex industrial processes are prone to errors because of interactions between humans, the external environment and the equipment. When the abnormity degree of a system increases, the system can generate failures or even accidents, which result in serious economic loss or even personal casualties. Therefore, it is necessary to take effective measures to remove the abnormity as soon as possible. This problem can be described as the least-time optimization problem. This paper analyses an abnormity by summarizing and comparing related concepts in the researched results. Based on these concepts, a control strategy for the abnormity in a complex industrial process is proposed by analysing the experience of operators on site. Taking the abnormity in the thickening process of gold hydrometallurgy as an example, this paper explores how the abnormity control problem can be transformed into the least-time optimization problem. Technical and mechanical constraints are described. Simulation results indicate that the proposed strategy can assist the operators to regulate the control variables and recover the abnormity as soon as possible. This produces better performance than the existing regulations on site.

Image-based visual servoing with depth estimation

“…Nowadays, the vision of robot is involved in all walks of our life, especially in the field of industrial automation (Dong and Zhang, 2020). For example, the application of robot vision in industrial process monitoring (Li and Feng, 2020), control and optimization (Wang et al, 2017), real-time monitoring and control of industrial cyber-physical systems, data-driven monitoring (Li et al, 2018a) and safety control foundations of industrial cyber-physical systems, and beyond. Visual servoing (VS) is a robotic arm motion control process that uses imaging information obtained by visual sensor to move marked features to target positions (Chaumette and Hutchinson, 2006).…”