Optimization of an explosive waste incinerator via an artificial neural network surrogate model

In this study, the operating conditions for a novel method of explosive waste disposal have been optimized to minimize the formation of NOx emissions. Previous methods of disposal include rotary kilns but have disadvantages, especially associated with the formation of NOx. To address this, development of a fluidized bed reactor as an incinerator has been proposed. However, as the number of input variables to consider increases, optimization of the design through computational fluid dynamics (CFD) simulations alone becomes challenging, because of the significant computational time required. Thus, we proposed to build a surrogate model of CFD output using an artificial neural network (ANN) that can be used to locate the optimal operating conditions. To perform the optimization on five variables, finite ranges of these variables were selected and sampled using a Latin Hypercube Method, giving a total of 300 random input samples. CFD simulations were performed using each of these conditions, and the ANN surrogate model was designed to match the results. Due to its higher accuracy, the surrogate model from the Bayesian Regularization method was selected, which had an error rate of around 1% when compared to the CFD results. Using this surrogate model, a 34% reduction in NOx emissions from the reactor was achievable. Overall, the fluidized bed reactors show promise with regard to the reduction of NOx emissions from explosive waste disposal, and surrogate modeling with ANNs represents a useful method for reducing the CFD computation time used for multivariable optimization of such a system.