Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms

Abstract

Global warming, climate change, fossil fuel depletion and steep hikes in the price of environmentally friendly hydrocarbons motivate researchers to investigate CO2 hydrogenation for hydrocarbons production. However, due to the reaction complexities and varieties of produced species, the process mechanism and subsequently estimation of the kinetic parameters have been controversial yet. Therefore, estimating the kinetic parameters using Artificial Bee Colony (ABC) and Differential Evolution (DE) optimization algorithms based on Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism is proposed as a possible remedy to fulfil the requirements. To this end, a one-dimensional heterogeneous model comprising detailed reaction rates of reverse water gas shift (RWGS), Fisher-Tropsch (FT) reactions and direct hydrogenation (DH) of CO2 is developed. It is observed that ABC exhibiting 6.3% error in predicting total hydrocarbons selectivity is superior to DE algorithm with 32.9% error. Therefore, the model employed the estimated kinetic parameters obtained via ABC algorithm, is exploited for products distribution analysis. Results reveal that maximum 73.21% hydrocarbons (C1C4) selectivity can be achieved at 573 K and 1 MPa with 0.85% error compared to the experimental value of 72.59%. Accordingly, the proposed model can be exploited as a powerful tool for evaluating and predicting the performance of CO2 hydrogenation to hydrocarbons process.