Predictive Control Study for Evaporation Process Based on Fuzzy Least Square Support Vector Machine
Aiming at the characteristics of multivariable, nonlinearity, large time-delay in alumina evaporation process and the different features of various temporal and spatial samples, a predictive control strategy combining the fuzzy least square support vector machine (LS-SVM) with weight factor and chaotic particle swarm optimization (CPSO) with last out mechanism was proposed. To achieve rolling optimization in predictive control, a CPSO algorithm with last out mechanism was introduced to calculate the control sequence. The experimental verification analysis was performed using the industrial production data from evaporation process of an alumina plant. The results show that percentage of the samples with prediction relative error less than 8% was up to 93.9%, while the outlet concentration was stabilized at about 240 g/L, and the prediction control performance is greatly improved and plays a role in reducing energy consumption.