Exergy analysis and optimization of a solar and biomass-assisted methanol synthesis system using artificial neural networks
Energy Sources, Part A: Recovery, Utilization and Environmental Effects, cilt.48, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 48 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/15567036.2026.2699334
- Dergi Adı: Energy Sources, Part A: Recovery, Utilization and Environmental Effects
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Applied Science & Technology Source, Compendex, Environment Index, Greenfile, INSPEC, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Engineering Source (EBSCO)
- Anahtar Kelimeler: Artificial neural network, energy, exergy, gasification, methanol synthesis
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
This study proposes and analyzes a solar tower-assisted methanol synthesis system integrated with a biomass gasification unit. The system's exergy and energy performances are investigated using Aspen Plus software. At the same time, a dedicated solar tower model is developed in Python to utilize the high amount of heat due to concentrated solar radiation. Parametric analyses are conducted to examine the effects of the steam-to-biomass (S/B) ratio and gasification temperature on syngas composition, methanol, methane, and hydrogen production rates. The results reveal that the overall system achieves an energy efficiency of 43% and an exergy efficiency of 61.3%. The radial basis function neural network (RBFNN) employed for optimization comprises two input neurons (S/B ratio and gasification temperature), one hidden layer with Gaussian radial basis activation functions, and an output layer that predicts methanol, methane, and hydrogen production rates. The dataset generated by Aspen Plus simulations is split into 70% training, 15% validation, and 15% testing. Methanol synthesis predictions based on the S/B ratio achieve minimum error metrics, with mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.0053, 0.0514, and 0.0729, respectively.