The gold hydrometallurgy process has been prevalent in the hydrometallurgical industry. With a further understanding of the process, the process mechanistic model has been constructed for performing control and optimization. However, due to random disturbances and changing operating conditions, the operating performance of the nominal model-based process has changed. To further perform real-time optimization of the gold hydrometallurgy process, a novel real-time optimization (RTO) compensation method based on the process operating performance optimality assessment (POPOA) is proposed in this paper. Firstly, the operating performance of the online process is judged by the process operating performance optimality assessment algorithm. For the case that the process operating performance is the optimal grade, in which the process operating state is close to the optimal state, but it is not in the optimal state due to some small disturbances in the process, the self-optimizing control (SOC) method is realized by taking the combinations of measurements as the controlled variables (CVs), which are tracked at the optimally insensitive set-points. Furthermore, under the condition of the operating performance is non-optimal grade due to the influence of serious disturbances or obvious change of operating conditions, a similarity detection procedure is complemented based on a SOC model base for searching the CVs set-points relative to similar models. When there is no usable set-point, it is necessary to perform the SOC offline modeling to update the model, or to find similar cases in the historical optimal operational case base to realize the compensation operation strategy based on just-in-time (JIT). Specifically, when similar samples cannot be found in the historical SOC base and the optimal operational case base, the plant-wide optimization model must be rebuilt to meet the requirements of the actual industrial production process. Finally, an industrial simulation experiment of the hydrometallurgical production process is carried out. The simulation results verify the effectiveness and practicability of the proposed new RTO compensation scheme.

黄金湿法冶金工艺已在湿法冶金行业盛行。随着对过程的进一步了解，建立了过程机理模型以进行控制和优化。然而，由于随机干扰和不断变化的运行条件，基于标称模型的过程的运行性能发生了变化。为了进一步对黄金湿法冶金过程进行实时优化，本文提出了一种基于过程操作性能优化评估（POPOA）的新型实时优化（RTO）补偿方法。首先，通过过程运行性能优化评估算法判断在线过程的运行性能。对于工艺运行性能为最优等级的情况，过程运行状态接近最优状态，但由于过程中的一些小扰动而不处于最优状态，自优化控制（SOC）方法是通过以测量值的组合为受控变量 (CV)，在最佳不敏感设定点处进行跟踪。此外，在由于严重扰动的影响或运行条件的明显变化而导致运行性能非最佳等级的情况下，基于SOC模型库补充相似性检测程序，用于搜索相对于相似的CVs设定点。楷模。当没有可用的设定点时，需要执行 SOC 离线建模来更新模型，或在历史最佳运营案例库中寻找类似案例，实现基于准时制（JIT）的薪酬运营策略。具体来说，当在历史SOC库和最优运行案例库中找不到相似的样本时，必须重新构建全厂优化模型以满足实际工业生产过程的要求。最后，对湿法冶金生产过程进行了工业模拟实验。仿真结果验证了所提出的新型RTO补偿方案的有效性和实用性。必须重建全厂优化模型以满足实际工业生产过程的要求。最后，对湿法冶金生产过程进行了工业模拟实验。仿真结果验证了所提出的新型RTO补偿方案的有效性和实用性。必须重建全厂优化模型以满足实际工业生产过程的要求。最后，对湿法冶金生产过程进行了工业模拟实验。仿真结果验证了所提出的新型RTO补偿方案的有效性和实用性。