Predictive modeling of biomass gasification with machine learning-based regression methods

Biomass gasification is a promising power generation process due to its ability to utilize waste materials and similar renewable energy sources. Predicting the outcomes of this process is a critical step to efficiently obtain the optimal amount of products. For this purpose, various kinetic and equilibrium models are proposed, but the assumptions made in these models significantly reduced their practical usability and consistency. More recently, machine learning methods have started been employed, but the limited selection of methods and lack of implementation of cross-validation techniques caused insufficiency to obtain unbiased performance evaluations. In this study, we employed four regression techniques, i.e., polynomial regression, support vector regression, decision tree regression and multilayer perceptron to predict CO, CO2, CH4, H2 and HHV outputs of the biomass gasification process. The data set is experimentally collected via downdraft fixed-bed gasifier. PCA technique is applied to the extracted features to prevent multicollinearity and to increase computational efficiency. Performances of the proposed regression methods are evaluated with k-fold cross validation. Multilayer perceptron and decision tree regression performed the best among other methods by achieving R2> 0.9 for the majority of outputs and were able to outperform other modeling approaches.