Electrolysis Energy Consumption Of The Zinc-based Multi-objective Particle Swarm Algorithm Optimization Method And Its Application

The energy consumption is an important economic index of electrolytic zinc process(EZP) which zinc is deposited in the zinc sulfate solution with direct current. EZP is characterized as complicated technology, and the influential factors of energy consumption of EZP are multiple, which are also strong coupling and serious nonlinear. The process parameters are adjusted empirically, but disregarding their comprehensive optimization. Therefore, the energy consumption of EZP is high at present.To solve this problem, a current efficiency and cell voltage SVM model related to the current density, the sulfate concentration, the zinc concentration and the electrolyte solution temperature was established by analyzing the electrochemical reaction of EZP, and then the model was corrected with production data. Morever, a multi-objective optimization model for the energy consumption of EZP was built based on the proposed SVM model of EZP. In the model, the current density, the sulfate concentration, the zinc concentration and the electrolyte solution temperature were assumed to be variables, considered output and production process as constraint, and aimed to minimize the energy consumption and the power cost.The multi-objective particle swarm optimization(MOPSO) was studied. Based on the problems that the final solution can't spread to the pareto front uniformly and it is insufficient in convergence which obtained by particle swarm optimization(PSO) in multi-objective optimization problems, a MOPSO algorithm on the basis of archiving and weighted cofficient was proposed. The conception of semi-feasible region and the rules for the competition and selection which introduced archive to preserve best infeasible solutions were introduced to treat constrained conditions, and the selection of operating semi-feasible region was designed. The personal archiving and weighted coefficient method was used for personal best selection, and the real-time mutation strategy was introduced to avoid getting into the optimizing locality when the global best was selected from the external archive with roulette selection. A large number of standard functions were taken to test its performance, and the simulation results demonstrated that the proposed algorithm is feasible and effective.Finally, the proposed MOPSO was applied to solve the energy consumption multi-objective optimization model of EZP. Then the satisfactory solution was obtained by multiple attribute decision making method based on TOPSIS, and the energy consumption optimization of EZP was implemented.