A Hybrid Genetic Programming–Gray Wolf Optimizer Approach for Process Optimization of Biodiesel Production

Biodiesel is one the most sought after alternate fuels in the current global need for sustainable and renewable energy sources due to their lower emissions and no major modification requirement to existing engines. However, the performance and productivity of the biodiesel production process are significantly dependent on the process parameters. In this regard, a novel hybrid genetic programming-gray wolf optimizer approach for the process optimization of biodiesel production is proposed in this paper. For an illustration of the proposed approach, kinematic viscosity is expressed as a symbolic regression metamodel to account for the influence of catalyst concentration, reaction temperature, alcohol-to-oil molar ratio, and reaction time. Then, the genetic programming-based symbolic regression metamodel is used as an objective function by the gray wolf optimizer to optimize the process parameters. The obtained results show that the proposed approach is simple, accurate, and robust.