[54] The total number of epochs used for training was 500. The loss function applied was mean absolute error (MAE), which was chosen over mean squared error (MSE), so the model would not be biased towards predicting regions of poor process performance particularly accurately. An example is at low working capacities when specific thermal energy and specific electrical energy are very high and sensitive to small changes in input values. Hence, this region would be prone to large prediction errors and greatly penalised by MSE. A hyperparameter optimisation was performed using K-fold crossvalidation using 7 folds and a grid search, and the number of dense layers and the width of the dense layers were varied as hyperparameters. A key point is that a singular ANN was trained to predict all five outputs: specific thermal energy, specific electricity, productivity, purity, and working capacity. Email: jpy1@hw.ac.uk RCCS, Heriot-Watt University