A gradient optimization scheme for solution purification process

Tang

et al. 2023

Can J Chem Eng

Self Cite

The cobalt removal process with arsenic salt of zinc hydrometallurgy has serious non‐linearity, uncertainty, and mutual coupling. Its accurate dynamic modelling has always been a challenging problem. On the basis of in‐depth analysis of cobalt removal process and reaction mechanism, considering the cascade relationship between the reactors, a dynamic synergistic continuously stirred tank reactor (SCSTR) mechanism model of the cobalt removal process was constructed. Aiming at the unknown parameters in the SCSTR model, the idea of the Kalman filter was introduced, and the unknown parameters were characterized as unknown states; a method of estimating the unknown model parameters was developed using the augmented state equation and the unscented Kalman filter (UKF) algorithm. Simulation results with industrial data of a zinc smeltery showed that the parameter estimation model has high accuracy, and the estimated parameters can be used in the SCSTR model. An intensive simulation analysis of the dynamic characteristics of the complete SCSTR model was carried out to verify the influence of different input disturbances on the output ion concentration of each reactor, which demonstrated the excellent dynamic performance and potential of the model. Ultimately, according to the industrial calculation analysis, the SCSTR model has a guiding effect on the addition of zinc powder in the reactors, overcomes the blindness in the production process, and provides a momentous basis for the optimization control of the cobalt removal process.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

Tang

et al. 2023

Can J Chem Eng

Self Cite

“…In 2015, Sun et al proposed a two‐layer control scheme and the idea of gradient optimization for the solution purification process, and designed a robust adaptive controller to track the optimized impurity ion concentrations in the presence of process uncertainty. [ 17 ] However, the performance of this scheme was affected by the accuracy of the process model. Wang et al developed an ICSTR model under time‐varying conditions and designed a predictive controller to achieve optimal operation of the cobalt removal process.…”

Section: Introductionmentioning

confidence: 99%

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Tang

et al. 2023

Can J Chem Eng

To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

“…Owing to the complexity of production, the adjustment process will be complicated and time‐consuming. Taking the cobalt removal process 3, 4 as an example, an adjustment process usually lasts one or two hours whereas a complete production process takes only about four hours. Hence, the operating performance of the transition state will seriously affect the smooth and efficient operation of the production.…”

Section: Introductionmentioning

confidence: 99%

Operating Performance Assessment for Transition State of Industrial Processes

Sun

et al. 2020

Chem Eng & Technol

Self Cite

Traditional operating performance assessment approaches mainly focus on the steady state. However, the assessment indicators designed for the steady state cannot be directly applied to the transition state. Moreover, identifying the process state for a multivariable system is difficult and time-consuming. An operating performance assessment method for the transition state is developed. In the proposed framework, a transition state detection method is first proposed to detect the transition state from the data automatically and accurately. Then, a systematic set of several special indicators is developed based on the characteristic of the transition state. The effectiveness of the developed method is demonstrated with a case study of an industrial hydrocracking process.