Technical Architecture of Big Data Cloud Platform for Intelligent Washing Factory

Zheng, Chuanxing

doi:10.1109/icaibd51990.2021.9459049

Cited by 2 publications

(1 citation statement)

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“…How to monitor the working status of fxtures in real time during the cleaning process, accurately perceive harsh working conditions, and efectively ensure production quality remains a challenge [4,5]. Te core of intelligent cleaning is to collect multidimensional operation data of jigs, quickly mine real-time status, and timely return accurate control strategies [6]. In previous studies, the coal separation process was regarded as a physical and mathematical modeling problem, and the control strategy was usually generated by referring to the established model [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning and Auto‐Differential Evolution Co‐Guided Coal Washing

Zuo

2024

Discrete Dynamics in Nature and Society

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Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

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