Set-Point Tracking and Multi-Objective Optimization-Based PID Control for the Goethite Process

The goethite process is complicated since its chemical reactions interact with each other, making its control and optimization in industrial production difficult. The goal of the goethite process is to make the outlet ion concentration satisfy the technical requirements with minimal process consumption. To simplify the difficulties of optimization in the goethite process, an optimization method based on a set-point tracking strategy is proposed. The set-point tracking strategy is used to transform the complex state constraints into an additional objective. Therefore, the single optimization control problem for the goethite process is transformed into a bi-objective optimization control problem. Furthermore, PID controllers are adopted to control the addition amounts of zinc oxide and oxygen in the goethite process. The optimal parameters of the PID controllers are obtained via a multi-objective state transition algorithm (MOSTA). The performance of MOSTA is verified by several benchmark test functions with performance matrices. The control performance reveals that the proposed method is an effective way to control the process and can not only reduce the zinc oxide and oxygen addition amounts compared with manual operation and traditional PID control but also reject disturbances. The proposed method can satisfy the industrial requirements with less energy consumption.

[1]  Paulo Moura Oliveira,et al.  From single to many-objective PID controller design using particle swarm optimization , 2017 .

[2]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[3]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[4]  Xuefang Li,et al.  Adaptive Boundary Iterative Learning Control for an Euler–Bernoulli Beam System With Input Constraint , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Xiaojun Zhou,et al.  Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy , 2017 .

[6]  Xiaojun Zhou,et al.  A dynamic state transition algorithm with application to sensor network localization , 2015, Neurocomputing.

[7]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[8]  Weihua Gui,et al.  Dynamic modeling and optimal control of goethite process based on the rate-controlling step , 2017 .

[9]  Ponnuthurai N. Suganthan,et al.  Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization , 2011, Inf. Sci..

[10]  R. Ahmad,et al.  Performance optimization of a microchannel heat sink using the Improved Strength Pareto Evolutionary Algorithm (SPEA2) , 2015 .

[11]  Xiaojun Zhou,et al.  State Transition Algorithm , 2012, ArXiv.

[12]  J. C. Balarini,et al.  Importance of roasted sulphide concentrates characterization in the hydrometallurgical extraction of zinc , 2008 .

[13]  Kok Lay Teo,et al.  Optimal control for zinc solution purification based on interacting CSTR models , 2012 .

[14]  L. Caccetta,et al.  An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy , 2015 .

[15]  A. Seyrankaya,et al.  Precipitation of impurity ions from zinc leach solutions with high iron contents - A special emphasis on cobalt precipitation , 2016 .

[16]  J. R. Carvalho,et al.  Iron recovery from sulphate leach liquors in zinc hydrometallurgy , 2003 .

[17]  Yongfeng Chang,et al.  Removal of iron from acidic leach liquor of lateritic nickel ore by goethite precipitate , 2010 .

[18]  G. Chiandussi,et al.  Comparison of multi-objective optimization methodologies for engineering applications , 2012, Comput. Math. Appl..

[19]  Xiaojun Zhou,et al.  A Two-stage State Transition Algorithm for Constrained Engineering Optimization Problems , 2018 .

[20]  W. Gui,et al.  A new multi-threshold image segmentation approach using state transition algorithm , 2017 .

[21]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[22]  Xie Shi Optimal Control of Oxidizing Rate for Iron Precipitation Process in Zinc Hydrometallurgy , 2015 .

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Xiaojun Zhou,et al.  A Novel Cognitively Inspired State Transition Algorithm for Solving the Linear Bi-Level Programming Problem , 2018, Cognitive Computation.

[25]  Shyam R Asolekar,et al.  Jarosite characteristics and its utilisation potentials. , 2006, The Science of the total environment.