Integrated Optimal Control for Electrolyte Temperature With Temporal Causal Network and Reinforcement Learning

doi:10.1109/TNNLS.2023.3278729

Integrated Optimal Control for Electrolyte Temperature With Temporal Causal Network and Reinforcement Learning

2024-05-01

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DOI：10.1109/TNNLS.2023.3278729

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The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal control of electrolyte temperature faces the following challenges. First, the temporal causal relationship between process variables and current efficiency makes it difficult to estimate the current efficiency accurately and set the optimal electrolyte temperature. Second, the substantial fluctuation of influencing variables of electrolyte temperature leads to difficulty in maintaining the electrolyte temperature close to the optimum point. Third, due to the complex mechanism, building a dynamic electrowinning process model is intractable. Hence, it is a problem of index optimal control in the multivariable fluctuation scenario without process modeling. To get around this issue, an integrated optimal control method based on temporal causal network and reinforcement learning (RL) is proposed. First, the working conditions are divided and the temporal causal network is used to estimate current efficiency accurately to solve the optimal electrolyte temperature under multiple working conditions. Then, an RL controller is established under each working condition, and the optimal electrolyte temperature is placed into the controller's reward function to assist in control strategy learning. An experiment case study of the zinc electrowinning process is provided to verify the effectiveness of the proposed method and to show that it can stabilize the electrolyte temperature within the optimal range without modeling.

Keywords:

Electrolytes

Zinc

Process control

Optimal control

Indexes

Ions

Electrochemical processes

Electrolyte temperature

optimal control

reinforcement learning (RL)

temporal causality

zinc electrowinning process (ZEP)

Integrated Optimal Control for Electrolyte Temperature With Temporal Causal Network and Reinforcement Learning

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