Multi-factor optimization of bio-methanol production through gasification process via statistical methodology coupled with genetic algorithm

• Published in: Results in engineering 2023-12, Vol.20, p.101477, Article 101477
• Main Authors: Hedayati Moghaddam, Amin, Esfandyari, Morteza, Jafari, Dariush, Sakhaeinia, Hossein
• Format: Article
• Language: English
• Subjects: Bio-methanol; Design of experiment; Gasification; Genetic algorithm; Simulation
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2023.101477

This work innovatively explores the bio-methanol production process, conducts comprehensive analyses, develops statistical models, and optimizes operational conditions, contributing valuable insights to the field of sustainable energy production from biomass. Accordingly, bio-methanol production from biomass through gasification route was investigated and simulated using Aspen Plus software. The effects of operational parameters on energy duty of gasification reactor and the methanol production rate in syngas to methanol reactor were investigated. The parameters affecting the process performance including temperature, pressure, and steam/feed ratio were examined using the response surface methodology (RSM) by central composite design (CCD) technique. Analysis of variance (ANOVA) was performed, and two quadratic models were derived. The predicted R2 values of these models for methanol mass flowrate and energy duty were 0.9394 and 0.9363, respectively. The optimal operational conditions were identified using genetic algorithm (GA). The optimum values of temperature, pressure, and steam/feed ratio in gasification reactor were 900 °C, 4 bar, and 0.675, respectively. This condition leads to methanol mass flowrate and energy duty of 4.254 kg/s and 40736.355 kw, respectively. In addition, sensitivity analysis was performed on syngas to methanol reactor performance. •Performing simulation of bio-methanol production from biomass via gasification process.•Applying RSM to design experiments and develop statistical models for bio-methanol production and energy consumption.•Applying genetic algorithm for process optimization.•Performing sensitivity analysis of process performance vs. operative parameters.