Optimisation of an old 200 MW coal-fired boiler with urea injection through the use of supervised machine learning algorithms to achieve cleaner power generation

Abstract

Abstract Due to the ever more stringent environmental regulations focused on cleaner power generation, thermal power plants that produce energy by burning fossil fuels are forced to optimise combustion processes or invest in new, more modern combustion plants meeting the environmental regulations. The purpose of the research is economic-ecological optimisation based on the minimisation of the consumption of reagents and a thermodynamic analysis of the impact of the injection of urea into the combustion chamber, demonstrating the innovative aspect of the study. In order to minimise additional operating costs and ensure the half-hour average nitrogen oxides emission values below 200 mg/m3, we created supervised machine learning algorithms. The supervised machine learning algorithms are applied by using the supervised machine learning methods such as artificial neural network and local linear neuro-fuzzy models. The proposed non-linear models are based on a wide range of real process operational datasets from a combined heat and power system in a thermal power plant. The results show that by controlling urea direct injection the supervised machine learning algorithms significantly minimise the operating costs and ensure, at the same time, that requirements regarding the nitrogen oxides emissions prescribed by the European Union directive are met. Moreover, it is evident from the results of the analysis that 2.297 MW of heat from the firebox is consumed on average over the period analysed due to the evaporation and preheating of the mixture injected into the firebox via urea direct injection. As a result, the coal consumption and greenhouse gas emissions are increased.