A gradient optimization scheme for solution purification process

Abstract This paper presents a two-layer control scheme to address the difficulties in the modeling and control of solution purification process. Two concepts are extracted from the characteristics of solution purification process: additive utilization efficiency (AUE) and impurity removal ratio (IRR). The idea of gradient optimization of solution purification process, which transforms the economical optimization problem of solution purification process into finding an optimal decline gradient of the impurity ion concentration along the reactors, is proposed. A robust adaptive controller is designed to track the optimized impurity ion concentration in the presence of process uncertainties, disturbance and saturation. Oxidation reduction potential (ORP), which is a significant parameter of solution purification process, is also used in the scheme. The ability of the gradient optimization scheme is illustrated with a simulated case study of a cobalt removal process.

[1]  Weihua Gui,et al.  An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential , 2013 .

[2]  S. Kim,et al.  Estimation methods for efficiency of additive in removing impurity in hydrometallurgical purification process , 2007 .

[3]  Gordon Broderick,et al.  Adaptive control of a CSTR with a neural network model , 2001 .

[4]  Tadahisa Nishimura,et al.  Comparison between purification processes for zinc leach solutions with arsenic and antimony trioxides , 1992 .

[5]  B. Boyanov,et al.  REMOVAL OF COBALT AND NICKEL FROM ZINC SULPHATE SOLUTIONS USING ACTIVATED CEMENTATION , 2004 .

[6]  T. Østvold,et al.  Norzink removal of cobalt from zinc sulphate electrolytes , 1994 .

[7]  Fen Wu.  LMI-based robust model predictive control and its application to an industrial CSTR problem , 2001 .

[8]  Moustafa Elshafei,et al.  RBF neural network inferential sensor for process emission monitoring , 2013 .

[9]  S. Palmas,et al.  Kinetics of cobalt cementation on zinc powder , 1995 .

[10]  Dimitrios Filippou,et al.  The kinetics of cobalt removal by cementation from an industrial zinc electrolyte in the presence of Cu, Cd, Pb, Sb and Sn additives , 2001 .

[11]  Françoise Couenne,et al.  Lyapunov-based control of non isothermal continuous stirred tank reactors using irreversible thermodynamics , 2012 .

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

[13]  A. Dib,et al.  Mass transfer correlation of simultaneous removal by cementation of nickel and cobalt from sulfate industrial solution containing copper: Part I: Onto rotating zinc electrode disc , 2007 .

[14]  Jing Zhou,et al.  Robust Adaptive Control of Uncertain Nonlinear Systems in the Presence of Input Saturation and External Disturbance , 2011, IEEE Transactions on Automatic Control.

[15]  Kok Lay Teo,et al.  Optimal control problems arising in the zinc sulphate electrolyte purification process , 2012, J. Glob. Optim..

[16]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[17]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[18]  Chunhua Yang,et al.  Intelligent optimal setting control of a cobalt removal process , 2014 .

[19]  A. Dib,et al.  Mass transfer correlation of simultaneous removal by cementation of nickel and cobalt from sulphate industrial solution containing copper Part II: Onto zinc powder , 2006 .

[20]  T. Østvold,et al.  Products formed during cobalt cementation on zinc in zinc sulfate electrolytes , 2000 .

[21]  R. W. Lew.  The removal of cobalt from zinc sulphate electrolytes using the copper-antimoney process , 1994 .

[22]  Junfei Qiao,et al.  Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network , 2012 .

[23]  Gang Xie,et al.  Mechanism of cobalt removal from zinc sulfate solutions in the presence of cadmium , 2006 .

[24]  Alessandro Astolfi,et al.  Continuous stirred tank reactors: easy to stabilise? , 2003, Autom..

[25]  G. Houlachi,et al.  The Removal of Cobalt from Zinc Electrolyte by Cementation: A Critical Review , 2000 .

[26]  Dingli Yu,et al.  A stable self-learning PID control for multivariable time varying systems , 2007 .

[27]  Fabiola Angulo,et al.  A robust adaptive controller for an anaerobic digester with saturated input: Guarantees for the boundedness and convergence properties , 2012 .

[28]  Ying Zhang,et al.  Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis , 2004, IEEE Trans. Autom. Control..

[29]  Louis Theodore.  Continuous Stirred Tank Reactors , 2012 .

[30]  Pierdomenico Pepe,et al.  Observer-based nonlinear control law for a continuous stirred tank reactor with recycle ☆ , 2011 .

[31]  Hassan K. Khalil,et al.  Output feedback control of nonlinear systems using RBF neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[32]  David Dreisinger,et al.  A fundamental study of cobalt cementation by zinc dust in the presence of copper and antimony additives , 1996 .