Optimizing combustion process by adaptive tuning technology based on Integrated Genetic Algorithm and Computational Fluid Dynamics

Low carbon economy and the current dominant role of fossil fuel power plants in serving electricity require the decrease of emission due to carbon dioxide by improving the boiler efficiency. Literature review and industry practice show that neural network based methods are applied to improve the boiler efficiency in a number of power plants. However, slagging and fouling are still serious problems which impair the efficiency of heat transfer and degrade the performance of a boiler in power plant that uses fossil fuels with high fouling tendency. This paper proposes a new strategy which can be applied to optimize the boiler combustion process by an online adaptive tuning technology based on Integrating Genetic Algorithm (GA) with Computational Fluid Dynamics (CFDs). First, a simple heat transfer case with slagging influence consideration is modeled using CFD. Second, an online GA is applied to optimize the complex process in which a fire ball and a slagging layer are simulated. Finally, Simulink programs are created to simulate how to integrate GA with CFD to optimize the heat transfer process where slag deposit is considered. The model results show that the optimized thermal dynamic system obtains higher heat transfer efficiency than one without optimizing.