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Multi‐stage economic model predictive control for a gold cyanidation leaching process under uncertainty
AIChE Journal ( IF 3.7 ) Pub Date : 2020-09-01 , DOI: 10.1002/aic.17043
Runda Jia 1, 2 , Fengqi You 2
Affiliation  

In order to dynamically operate the gold cyanidation leaching process (GCLP) under uncertainty, a multi‐stage economic model predictive control (EMPC) is proposed for GCLP for the transient and steady‐state economic optimization. The proposed multi‐stage EMPC is composed of two steps. In the first step, the unmeasurable uncertain parameters are estimated by using Tikhonov regularization based method, so as to avoid amplification and propagation of the noise measurements into the estimation. Based on the estimated results, the scenario tree for multi‐stage EMPC is generated from the historical data using a data‐driven approach, and the control inputs are obtained from solving the resulting large nonlinear programming problem (NLP) at each sampling point. The resulting uncertainty model and the probability of each scenario are more consistent with the actual industrial GCLP, and the solutions are less conservative. The efficiency of the proposed multi‐stage EMPC is verified through a simulated industrial GCLP. Compared with other EMPC methods, including classic EMPC and multi‐stage EMPC with box uncertainty region, the proposed method can reduce the economic cost while accounting for the constraints at the same time.

中文翻译:

不确定条件下金氰化浸出过程的多阶段经济模型预测控制

为了在不确定性下动态地运行金氰化浸出过程(GCLP),针对瞬时和稳态经济优化,针对GCLP提出了多阶段经济模型预测控制(EMPC)。提议的多阶段EMPC由两个步骤组成。第一步,使用基于Tikhonov正则化的方法来估计无法测量的不确定参数,以避免将噪声测量结果放大和传播到估计中。基于估计的结果,使用数据驱动的方法从历史数据中生成多级EMPC的方案树,并通过解决每个采样点产生的大型非线性规划问题(NLP)获得控制输入。由此产生的不确定性模型和每种情况的概率与实际工业GCLP更加一致,解决方案也不太保守。拟议的多阶段EMPC的效率已通过模拟工业GCLP进行了验证。与传统的EMPC和具有箱式不确定区域的多级EMPC等其他EMPC方法相比,该方法可以在降低经济成本的同时兼顾约束。
更新日期:2020-09-01
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