A Data-Driven Iterative Optimization Compensation Method Based on PJIT-PLS for Gold Cyanidation Leaching Process

Gold cyanidation leaching process (GCLP) as the central unit operation in hydrometallurgy, which suffers from the problem of the optimal setting point based-model is difficult to reach the optimal working point in actual GCLP due to model error, which leads to lower economic benefit. Meanwhile, the process data contains noise and uncertainty on account of the fluctuation of raw material properties. Therefore, how to take the most of that data to make the production process run in the optimal state of economic benefit under the premise that the quality index meets the production requirements is an urgent problem to be solved. In this paper, a data-driven iterative optimization compensation strategy is proposed to solve aforementioned problems. Firstly, probabilistic principle component analysis (PPCA) method is used to preprocess process data for eliminating the effect of noise and uncertainty; Secondly, two relevant models are established between the operating variable increment and the economic benefit increment, the quality index increment based on just in time (JIT) and partial least squares (PLS) method; Finally, the optimal operating variable increment that maximizes the economic benefit increment can be optimized under the condition of quality index satisfies the production requirements and iterated at the new working point, which is constantly close to the optimal working point to improve the economic benefit. Simulation studies have verified the validity of proposed method.

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