A soft sensor based on adaptive fuzzy neural network and support vector regression for industrial melt index prediction

• Build an soft sensor based on fuzzy neural network (FNN) for MI prediction
• Use predefined threshold to dynamically change the rule number in the FNN model
• Use support vector regression to gain better generalization ability
• Apply the soft sensor to real industrial polypropylene plant

An adaptive soft sensor for online monitoring melt index (MI), an important variable determining the product quality in the industrial propylene polymerization (PP) process, is proposed, where fuzzy neural network (FNN) is served as the basic model for its powerful nonlinear approximation ability as a machine learning method. However, considering the difficulty of structure determination of the FNN, an adaptive fuzzy neural network (A-FNN) is subsequently developed to determine the number of fuzzy rules, where a novel adaptive method dynamically changes the structure of the model by the predefined thresholds. Furthermore, in order to get better generalization ability of the soft sensor, support vector regression (SVR) is introduced for parameter tuning, where the output function is transformed into an SVR based optimization problem. The online soft sensor is also carried out on a real industrial PP plant as illustration, where the soft sensors including the SVR, FNN–SVR and A-FNN–SVR models are compared in detail. The research results show that the proposed soft sensor achieves a good performance in the industrial MI prediction process.