Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Process modeling and optimization of polymerization processes with long chain branching is currently an area of extensive research owing to the advantages and growing popularity of branched polymers. The highly complex nature of these reaction networks results in a large set of stiff ordinary differential equations to model them mathematically with adequate precision and accuracy. In such a scenario, where execution time of the model is expensive, the idea of going for online optimization and control of such processes seems to be a near impossible task. Catering these problems in the ongoing research, optimization using surrogate model obtained from a novel algorithm is proposed in this work as a solution. A Sobol set assisted artificial neural network replaces the computationally expensive kinetic model of long chain branched poly vinyl acetate as the fast and efficient surrogate model. The proposed multi-objective methodology allows the computationally expensive first principle model to determine the configuration of the neural network, which can emulate it with maximum accuracy along with sample size required. The algorithm introduces a logical way of designing ANN architectures where the outperformance of multiple layer networks justifies the elimination of heuristics approach to consider only single layer. The results of the proposed algorithm are compared with the results obtained using Kriging interpolator based another surrogate approach, for testing, validation and scope of improvement. The use of fast and efficient Sobol assisted ANN surrogate model makes the optimization process 10 times more efficient as compared to the case of optimization with computationally expensive kinetic model. The proposed ANN based surrogate is nearly 1.5 times as efficient as Kriging model in terms of number of expensive function evaluations. (C) 2015 Elsevier Ltd. All rights reserved.