Artificial Neural Networks and Experimental Data Analysis‐Based Biomass Combustion Machine's Dynamical Model Identification

ABSTRACT Because of many thermally, physically, and chemically interrelated phenomena, combustion biomass machines can be extremely difficult to operate. Research to develop a precise mathematical model to represent most of these machines is still in progress. This study established a model for predicting the inputs of a biomass machine. The model proposed is an inverse model. It is based on data experimentally obtained from a real‐world biomass machine. Given the complexity of the machine, it is critical to understand the signals that must be set at the input for it to perform optimally. Four ANN models were evaluated: the multilayer perceptron (MLP), the recurrent neural network (RNN), the long short‐term memory (LSTM), and the gated recurrent unit (GRU) models. These models consider the nonlinearity of the data. The inverse model based on the GRU approach outperformed the other ANNs tested, with a loss of 11.97% and an accuracy of 79.89%. The GRU‐based inverse model, relying on 17,281 experimental samples collected every 5 s, achieved a test MAE of 0.1197, RMSE of 0.1542, and accuracy of 79.89%, outperforming MLP, RNN, and LSTM, with prediction errors less than 6%, thus improving practical biomass combustion control. These two performance indicators were calculated from our test data (20% of the data). The difference in speed between forecasts and actual values was less than 6%. This is a step forward in better understanding biomass combustion machines and configuring appropriate input signals for them to operate in the desired mode.