Soft sensor modelling of acrolein conversion based on hidden Markov model of principle component analysis and fireworks algorithm

Abstract

Based on the existing propylene oxidation process, it is important to measure acrolein conversion for the production of acrylic acid. The gas chromatographic analyzer is generally used to analyze the acrolein conversion as an off‐line method. In this paper, a soft sensor modelling method of acrolein conversion based on the hidden Markov model with principle component analysis (PCA) and the fireworks algorithm (FWA) is proposed. Firstly, PCA is used to decrease the input variables of hidden Markov model. Then, FWA is applied to optimize the initial parameters of the hidden Markov model. Finally, the hidden Markov model based on PCA and the FWA is employed to predict the acrolein conversion. The proposed method is compared with the support vector machine (SVM), the artificial neural network (ANN), and the hidden Markov method (HMM) to show its superior performance.