Modeling a Delayed Coking Process with GRNN and Double-Chain Based DNA Genetic Algorithm

Abstract

For characterization or optimization process, a computer prediction model is in demand. This paper describes an approach for modeling a delayed coking process using generalized regression neural network (GRNN) and a double-chain based DNA genetic algorithm (dc-DNAGA). In GRNN, the smoothing parameters have significant effect on the performance of the network. This paper presents an improved GA, dc-DNAGA, to optimize the smoothing parameters in GRNN. The dc-DNAGA is inspired by the biological DNA, where the smoothing parameters are coded in the double-chain chromosomes and modified genetic operators are employed to improve the global search ability of GA. To test the performance of the constructed model, it is used to predict the output of the test data which is not included in the training data. Compared with other reported methods, eight cross validation results show the advantage of the proposed technique that it predicts the new data more accurately.