Artificial Neural Networks for Performance Prediction of Biomass Gasification: A Tool for Sustainable Energy Prediction
Keywords:
Biomass Gasification, Artificial Neural Network, Sustainable Energy, Renewable EnergyAbstract
Biomass gasification is one of the essential thermochemical processes for the production of clean syngas, which is consistent with the global energy agenda towards the development of renewable energy resources. Yet, the prediction of the operating parameters of the biomass gasification system is still challenging due to the nonlinear nature of the system dynamics. The development of an Artificial Neural Network (ANN) model for predicting the composition of syngas produced in bubbling fluidized bed gasifiers using 321 datasets available in literature is presented. Two models, namely Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP), are used in this study. The FFBP network with two hidden layers was found to be the best ANN model with coefficients of determinations R² > 0.97 and Mean Absolute Percentage Error (MAPE) between 7 and 12 percent for CO, H₂, CO₂, CH₄, and gas yield. The best ANN model is the FFBP network architecture with two hidden layers trained through One-Hot Encoding. The ANN model obtains R² > 0.85 and MAPE < 18% for CO₂, H₂, CO, and gas yield in all types of bed materials. A systematic approach for selecting the optimal topology of the network through the 5-fold cross-validation procedure was used. Through sensitivity analysis using the permutation method, it was found that the key parameters affecting syngas quality were the ash content (average 24.5%) and carbon content (average 14.7%), and unexpectedly, the temperature and steam/biomass ratio had minor effects. This study can contribute to the sustainable development agenda by improving the energy efficiency and reducing the cost of experimentation towards the achievement of the United Nations’ Sustainable Development Goals (SDGs) 7 and 9. The ANN-based model can be considered as a computationally efficient tool for the prediction of the biomass gasification system towards the development of the green economy
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