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A Novel GASEN-PSOELM-based Ensemble Method for Quality Variable Prediction in Chemical Processes

Data-driven soft sensor technology is an effective way to realize the online prediction of those important yet difficult-to-measure quality and component variables in chemical processes. Considering the characteristics of strong nonlinearity, time-variability and multi-working conditions, the soft sensor models based on a single network are difficult to guarantee global-scale prediction accuracy, while the traditional integration methods usually lead to a decrease in prediction speed and an increase in storage space. To cope with these issues, this paper proposes a novel ensemble soft sensor method using genetic algorithm to selectively integrate an extreme learning machine optimized by particle swarm algorithm, named GASEN-PSOELM in short. Firstly, the sampling data are used to generate the training samples of the sub-models by Bootstrap method. Secondly, the candidate sub-models are established via the extreme learning machine optimized by particle swarm algorithm, and then selected by using genetic algorithm. Finally, the prediction outputs are obtained by simple averaging integration. The validity and effectiveness of the proposed method is verified by the chemical equipment and chemical process, separately. The experimental results show that the proposed GASEN-PSOELM method can not only improve the prediction speed, but also ensure the good prediction accuracy and generalization performance.