Parameters Estimation of Enzymatic Reaction Model for Biodiesel Synthesis by Using Real Coded Genetic Algorithm with Some Crossover Operations

Abstract

Along with the increase in population and industry in many countries, the fuel oil demand also increases. Petroleum exploration on a large scale will accelerate the depletion of petroleum reserves. One alternative to meet fuel needs is the discovery of biodiesel which is renewable alternative energy. Synthesis biodiesel is carried out through an enzymatic reaction. In the enzymatic reaction model making biodiesel, there are parameters that must be estimated. The estimated parameters of the enzymatic reaction model will determine the success of the reaction. The parameter estimation of the enzymatic reaction model can be done using local optimization or global optimization algorithms, but the local optimization algorithm has a major disadvantage, the optimal value obtained is the local optimal value. Genetic algorithms are global optimization algorithms that are capable of working on high-dimensional problems. The success of genetic algorithms is determined by chromosome models, crossover operations, and mutation operations. The use of improper crossover operations often produces local optimum solutions. There are various types of crossover operation, each of which has weaknesses and advantages. This paper studies the parameters estimation of the enzymatic reaction model for biodiesel synthesis by using genetic algorithms with some crossover operation.