## 2 Experimental and Model Configuration  
The overall process flow for the modeling of biohydrogen production from waste glycerol is depicted in Figure 1. Typically, there are four stages for the prediction of biohydrogen production namely: data acquisition, model configuration, model evaluation, and model deployment. The data acquisition stage involved experiments on biohydrogen production. In the experimental batch run, waste glycerol was utilized as the substrate. The experimental runs were performed in a 100-mL serum bottle as described by [PERSON] and [PERSON] [15]. The fermentation medium in the serum bottle consisted of waste glycerol, urea, Na\({}_{2}\)HPO\({}_{4}\), Endo-nutrient, and inoculum. According to the design, the concentrations of each medium element were changed. The experiment was designed to investigate the effect of Urea Endo-nutrient, Na\({}_{2}\)HPO\({}_{4}\), and the amount of waste glycerol per liter of the medium on biohydrogen production. Experimental data consisting of 29 runs in batch mode, was employed for training various models used in this study. In the configuration stage, a multilayer perceptron neural network, denoted MLPNN, and a radial basis function neural network, denoted RBFNN (Figure 2), were employed. The MLPNN is a form of feed-forward neural network configured to achieve optimal performance [18]. The network configuration consists of three distinct types of layers, which include an input layer, an output layer, and a hidden layer [19]. The signal to be processed was brought into the system at the input layer. While the output layer facilitated the prediction of the targeted parameters. In an MLPNN, the true computational engine was comprised of an arbitrary number of interconnected hidden layers that were located between the input and output layers [20]. An MLPNN functions similarly to a feed-forward network in that data flows forward from the input layer to the output layer [21]. The neurons of the MLPNN were often trained using a technique known as  
Figure 1: Overall process flow for the modeling of biohydrogen production from waste glycerol. backpropagation learning [22]. Since MLPNNs were designed to be able to approximate any continuous function, they may be able to tackle problems that cannot be separated linearly. Typically, the configuration of the MLPNN consisted of three steps namely: variable selection, partitioning of the datasets for training and testing, and architecture specification. In step 1, the variables (both dependent and independent) were selected and loaded into the IBM Neural Network Modeller (IBM SPSS Version 22 environment). The datasets were subsequently partitioned in such a way that 70% was used for training and 30% for testing. The activation function for the hidden and outer layers was subsequently selected. This was important because how successfully the network model learns the training dataset will depend on the activation function that was used for the hidden layer. The kind of predictions the model can produce will depend on the activation function that is used for the output layer. Therefore, the activation function for any neural network model must be carefully selected. The effect of combining various activation functions on the model's performance was investigated. The activation functions investigated included hyperbolic, sigmoid, and identity functions. Scaled conjugate gradient and gradient descent were employed as the optimization algorithms for training. The learning rate of the model, which controls how much the weights are adjusted at each update, was initially set at 0.1. The learning rate changed as the training proceeded for the MLPNN, whereas the values remained unchanged for the RBFNN. The learning rate reduction, in epochs, was set to 10, which is the number of epochs of the training sample required to reduce the initial learning rate to its lower boundary.