Process simulation and stochastic multiobjective optimisation of homogeneously acid-catalysed microalgal in-situ biodiesel production considering economic and environmental criteria

The present work aims at the simulation and multiobjective optimisation (MOO) of dry microalgae-based in-situ biodiesel plant, modelled using the Aspen Plus V11. The process optimisation was carried out by excel-based multiobjective optimisation (EMOO) considering the non-dominated sorting genetic algorithm-II (NSGA-II). Economic and environmental criteria were considered for constrained MOO with total annualised cost (TAC), organic wastes, and CO2 emissions as objectives. The statistical trade-offs were analysed by assessing the impacts of the decision variables on the chosen objectives. Firstly, bi-objective optimisation scenarios were studied, and finally, a tri-objective optimisation scenario was investigated. The results imply that the TAC increases with the decrease in organic waste generation and CO2 emissions. The decision-makers will be able to assess the Paretooptimal front to find the preferred optimal solution to enhance plant performance. The first rank solution in the generated Pareto-optimal front was chosen by the net flow method (NFM). Compared to the base case study with a TAC of 69.31 million USD, Scenario A, Scenario B, and Scenario C resulted in an optimal plant operation with a TAC of 62.64 million USD, 61.52 million USD, and 60.23 million USD, respectively, with a saving of 6.67 million USD, 7.79 million USD, and 9.08 million USD respectively. Simultaneous optimisation of all three conflicting objectives yielded significant reductions in TAC (13.1%), organic waste (55%), and CO2 emissions (41%), respectively.

Automated summary

This study focuses on the simulation and multiobjective optimization of a dry microalgae-based in-situ biodiesel plant. Using economic and environmental criteria, the plant operation was optimized to reduce total annualized cost, organic waste generation, and CO2 emissions. Different scenarios were studied and the results showed significant improvements in plant performance with reductions in cost, waste, and emissions.