Development of various machine learning and deep learning models to predict glycerol biorefining processes

Abstract

Biorefining biological waste to produce eco-friendly fuels and by-products is essential in transitioning from non-renewable energies. However, the analysis of the processes in the laboratory necessitates a substantial investment of both time and money. The present study has developed machine learning (ML) models for evaluating biofuel products through glycerol biorefining for the first time. This study evaluates the performance of different ML algorithms, including recurrent neural network (RNN), random forest (RF), adaptive boosting (AdaBoost), Bayesian ridge (BR), and elastic net linear regression (ENLR). This research by machine learning algorithms can be created the formulas and the models for the hydrogen content (H2) by the hydrogen production and time, the hydrogen production by the H2 and time, OLR by PH and time, OLR by the hydrogen production and time, and the hydrogen production by OLR and time. Using the RNN for predicting the future of H2, hydrogen production, and OLR with the least error. The best R-Squared for the formulas is between 0.951 and 0.994 with the linear and the polynomial forms (by degrees 2, 3, 4). The best R-Squared for the models is between 0.998 and 0.999 with the linear form. MAEs for the formula and the model of H2, respectively, are 2.475347126 and 0.46588143, and MAEs for the formula and the model of the hydrogen production, respectively, are 23.44120285 and 7.03283978. MAEs of OLR by PH and OLR by the hydrogen production for formulas and models are 2.095157 and 000001 by PH and 3.148667 and 0.000001 by the hydrogen production. MAEs for the formula and the model of the hydrogen production by OLR, respectively, are 19.025255 and 2.718604.