Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models

Biodiesel is an attractive biofuel because it can be used directly with the traditional petro diesel engines, either as a substitute or as a blending component. There are several alternatives that can be used to produce biodiesel. In this work, we developed a superstructure optimization model to synthesize the optimum biodiesel production plant, i.e., the one that gives the minimum net present sink. To reduce the computational cost of solving the resulting disjunctive programming, the surrogate models utilizing artificial neural networks (ANNs), have been developed to replace the unit operation, thermodynamics and mixing models. The optimum solution with the alkali-catalyzed reactor (obtained in five CPU seconds) has a total net present sink of about $41 million, which differs less than one percent from the result obtained by modeling the solution in a process simulator. However, this level of accuracy required a large amount of data to train the ANNs.

► There are several process alternatives to produce biodiesel. ► Developed a superstructure optimization model to synthesize the optimum biodiesel production plant. ► Utilized artificial neural networks (ANNs) as surrogate models to replace the unit operation, thermodynamics and mixing models. ► ANNs reduced computation cost. ► The optimum solution: alkali-catalyzed reactor (obtained in five CPU seconds) with a total net present sink of about $41 million.