Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations

Biodiesel production often results in the production of a significant amount of waste glycerol. Through various technological processes, waste glycerol can be sustainably utilized for the production of value-added products such as hydrogen. One such process used for waste glycerol conversion is the bioprocess, whereby thermophilic microorganisms are utilized. However, due to the complex mechanism of the bioprocess, it is uncertain how various input parameters are interrelated with biohydrogen production. In this study, a data-driven machine-learning approach is employed to model the prediction of biohydrogen from waste glycerol. Twelve configurations consisting of the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN) were investigated. The effect of using different combinations of activation functions such as hyperbolic tangent, identity, and sigmoid on the model’s performance was investigated. Moreover, the effect of two optimization algorithms, scaled conjugate gradient and gradient descent, on the model performance was also investigated. The performance analysis of the models revealed that the manner in which the activation functions are combined in the hidden and outer layers significantly influences the performance of various models. Similarly, the model performance was also influenced by the nature of the optimization algorithms. The MLPNN models displayed better predictive performance compared to the RBFNN models. The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. While the MLPNN configuration with the hyperbolic tangent as the hidden layer activation function and the sigmoid as the outer layer activation function yielded the best performance as indicated by an R2 of 0.978 and a RMSE of 9.91. The gradient descent optimization algorithm was observed to help improve the model’s performance. All the input variables significantly influence the predicted biohydrogen. However, waste glycerol has the most significant effects.