Predicting glass furnace output using statistical and neural computing methods

This paper describes the development of predictive models for glass production at a regional manufacturing company. The objectives of the models are to predict the actual batch tonnage produced per week from the glass furnace based on the planned production schedule. Four modelling methods were explored: (i) linear regression; (ii) nonlinear regression; (iii) artificial neural network using back-propagation; and (iv) radial basis function neural network. Using 175 cases of production schedule data and subsequent furnace output, the two neural network-based prediction models resulted in lower average absolute error and lower maximum absolute error than the linear or nonlinear regression models. Accurate neural network-based prediction models of furnace output will subsequently be used in the overall production planning system by utilizing estimates of furnace output to determine the necessary energy, raw material, repair and personnel requirements of the glass manufacturing facility.