Multi-objective optimization of syngas production from bio-glycerol: A bi-reforming approach using NSGA-II

Abstract

This study investigates the multi-objective optimization (MOO) of syngas production from bio-glycerol using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) for constrained optimization in the bi-reforming process. Thermodynamic modelling of the system was conducted in Aspen Plus V12, applying the Gibbs free energy minimization method and accounting for all by-products. Key decision variables, including the water-to-glycerol ratio, CO₂-to-glycerol ratio, and reforming temperature, were examined for their impact on process performance. Results demonstrated that bi-reforming glycerol is more advantageous for syngas production compared to dry reforming, as it operates at lower temperatures, reducing coke formation. A glycerol bi-reforming process was simulated and optimized using MOO to maximize syngas production and CO₂ conversion while minimizing exergy loss and CO₂ emissions. The optimization revealed trade-offs among the objectives, with the Pareto front showcasing optimal solutions. Specifically, a higher water-to-glycerol ratio favoured a higher H₂-to-CO ratio but led to decreased CO₂ conversion and increased energy consumption and CO₂ emissions. The net flow method (NFM) is used to select the best optimal solutions from the Pareto front solutions based on the weight of the objective functions. Despite these trade-offs, bi-reforming of bio-glycerol was shown to be a viable method for producing syngas with a high H₂/CO ratio and low CO₂ emissions. The best optimal solutions obtained for the bi-reforming of glycerol show an SGR of ∼0.44 and CGR of 1.89, with the reforming temperature of 1202 K giving a CO₂ conversion of 42.55 % with exergy efficiency of 78.3 % and syngas production of 6.29 per mole of glycerol having H₂/CO ratio of 3.16.