A novel select-best and prepotency evolution algorithm and its application to develop industrial oxidation reaction macrokinetic model
A novel evolution algorithm with the select-best and prepotency operator (SPO), named select-best and prepotency evolution algorithm (SPEA), is proposed. The main genetic operators of SPEA are the proposed SPO and the uniform mutation operator. The SPO is defined as follows. Every individual in population has the same chance to select the best individual within its neighborhood range (the select-best range.) and produce new individuals through the crossover with the selected individual. Then the best one of two new individuals is selected as one individual of the next generation. With the SPO that preserves the diversity of individuals to avoid premature and can make excellent individuals be selected many a time, SPEA possesses advantages over conventional genetic algorithms. To compare the performances of SPEA with those of the real-coded genetic algorithm (GA), SPEA and the real-coded GA were applied to search the global optimal solution of a benchmark function. The comparison results demonstrated that SPEA spends less CPU time than the real-coded GA, the on-line, off-line, and local searching performances of SPEA are superior to those of the real-coded GA, and the probability of obtaining the global optimal solution for SPEA is larger than that for the real-coded GA. In addition, the relationship between the select-best neighborhood range and the CPU time consumed by SPEA was analyzed as well as the relationship between the select-best neighborhood range and the ratio of obtaining the global optimal solution. The results demonstrated that the proper ratio of the individual number in the select-best neighborhood to that in the population was 6%. Finally, SPEA was applied to develop a macrokinetic model of the industrial oxidation reaction of p-xylene to terephthalic acid (OXTA) in an Amoco reactor. The macrokinetic model based on the intrinsic kinetics model introduced the correction coefficients into the rate constants of the intrinsic kinetics model to indicate the mass transfer effects presented in the Amoco reactor. Then, with the data of the industrial OXTA, SPEA was employed to obtain the optimal correction coefficients, and the macrokinetic model with high precision for the industrial OXTA was developed. (c) 2005 Elsevier Ltd. All rights reserved.
evolution algorithm macrokinetic model genetic algorithm optimization radial basis functions partial least squares