Efficiency and nox emission optimization by genetic algorithm of a coal-fired steam generator modeled with artificial neural networks - Rocha, Bárbara Pacheco da (2019)
This work is part of the development of a decision support model for the operation of a real steam generator. The study proposes a combined optimization that aims to find operating points that achieve the highest efficiency of the steam generator associated with lower NOx emissions, applying genetic algorithm to the output of artificial neural network (ANN) models. The database consists of 10 operating parameters collected over a year and a half with a half-hour step and treated statistically. The behavior of the steam generator is modeled by multilayer Perceptron artificial neural networks with separate outputs for efficiency and NOx emission. The evaluation metrics applied to the ANNs were mean absolute error (MAE), mean square error (MSE), mean percentage error (MAPE) and coefficient of determination (R2). The ANN for predicting efficiency behavior presents test MSE and MAE of 0.7572 and 0.6206, respectively, and RNA for NOx has test MSE and MAE of 312.43 and 12.36. The optimization targets 98% efficiency of the steam generator and 220.00 mg/mN³ of NOx emissions, and approaches these goals with 97.95% efficiency and 222.28 mg/mN³ of NOx emissions.