Based on RNA genetic operations and DNA sequence model under selection and mutation, an electronic RNA genetic algorithm (RNA-GA) with improved crossover and mutation operator is proposed. The proposed algorithm can be implemented on real biochemical reaction after simple transition, thus, the brute force method of DNA computing can be broken. The convergence analysis of the proposed algorithm shows that RNA-GA with elitist strategy can converge in probability to the global optimum. Comparisons of RNA-GA with standard genetic algorithm (SGA) for typical test functions show the advantages and efficiency of the proposed algorithm. As illustrations, the RNA-GA is implemented on parameter estimation of a heavy oil thermal cracking 3-lumping model and a fluid catalytic cracking unit (FCCU) main fractionator. In both cases, it is shown that the methodology is effective in parameter estimation of chemical processes. (c) 2007 Published by Elsevier Ltd.