Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

Artificial neural networks and genetic algorithms are two intelligent approaches initially targeted to model human information processing and natural evolutionary process, with the aim of using the models in problem solving. During the last decade these two intelligent approaches have been widely applied to a variety of social, economic and engineering systems. In this paper, they have been shown as modelling tools to support human supervisory control to reduce fossil fuel power plant emissions, particularly NOx emissions. Human supervisory control of fossil fuel power generation plants has been studied, and the need of an advisory system for operator support is emphasized. Plant modelling is an important block in such an advisory system and is the key issue of this study. In particular, three artificial neural network models and a genetic algorithm-based grey-box model have been built to model and predict the NOx emissions in a coal-fired power plant. In non-linear dynamic system modelling, training data is always limited and cannot cover all system dynamics; therefore the generalization performance of the resultant model over unseen data is the focus of this study. These models will then be used in the advisory system to support human operators on aspects such as task analysis, condition monitoring and operation optimization, with the aim of improving thermal efficiency, reducing pollutant emissions and ensuring that the power system runs safely.