Simultaneous optimization of economic, environmental and safety criteria for algal biodiesel process retrofitted using dividing wall column and multistage vapor recompression

The present study deals with the multiobjective optimization (MOO) of retrofitted in situ algal biodiesel process. Transesterification of the algal lipids is intensified using ultrasonication and catalyzed using the ionic liquid catalyst. Process includes the retrofitting of two conventional distillation columns into a dividing wall column (DWC), which is further intensified using multistage vapor recompression (DWC-MVR) in order to decrease the energy consumption and CO2 emission from the process. Excel based hybridised multiobjective differential evolutionary dynamic local search (HMODE-DLS) algorithm is used for the constrained MOO, whereas Aspen Plus is used for the process development. Break even cost (BEC), eco indicator (EI99) and individual risk (IR) are considered as objectives to evaluate economic, environmental impact, and safety of process, respectively. Initially, bi-objective case studies were analyzed and finally, all three objectives are studied in one case. Pareto optimal solutions obtained from HMODE-DLS algorithm are then ranked by the simple additive weighted method to find out the best solution. MOO resulted in the significant decrease in BEC (similar to 20%), EI99 (similar to 48%) and IR (similar to 10%).

Automated summary

The present study focuses on the multiobjective optimization of retrofitted in situ algal biodiesel process. By intensifying the transesterification of algal lipids with ultrasonication and catalyzing with an ionic liquid catalyst, and retrofitting conventional distillation columns into a dividing wall column, the energy consumption and CO2 emission of the process are significantly reduced. The multiobjective optimization results in a substantial decrease in cost, environmental impact, and safety risk.