Melt index inferential model plays an important role in the control and optimization of polypropylene production. This study proposed a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multilayer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (particle swarm optimization-sequential quadratics programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of melt index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables. The proposed hybrid inferential model is validated on recorded data from an industrial double-loop propylene-polymerization reaction process.

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In this paper, a novel predictive controller based on minimal resource allocation network for non-linear system is presented. The controller combines the advantages of MRAN and neural predictor. The implemented neural predictive controller not only effectively eliminates the most significant obstacles but also be very robustness. At last, the algorithm is applied in a high non-linear Continuous Stirred Tank Reactor (CSTR) pH process PRGHO and presents a better real-time control effect. Index Terms -Minimal Resource Allocation network. Model predictive control. Non-linear system. CSTR pH process model

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