Sustainable NO[formula omitted] emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization

Abstract

Meeting increasingly stringent environmental targets while conforming to new operational demands of load-ramping and low-load operation presents a challenge for coal-fired thermal power stations. Artificial neural networks (ANNs) have been shown to predict efficiency and emissions of power stations, however, existing literature is comprised of offline studies and short-duration tests. In practice, ANN based combustion optimization systems (COS) have experienced mixed success due to the degradation of model performance over time and a lack of customizability. The current work presents a COS directed at reducing NOx at a 490 MW tangentially coal-fired power station over two years of live, closed-loop operation. The system employs a novel software platform called the Griffin AI Toolkit™, which includes a graphical user interface for ANN design and meta-optimization. A unique ANN utilizing swappable synapse weights for improved modeling of operational sub-regimes is validated against support vector regression (SVR), random forests (RF), and kernel partial least squares (KPLS) models, then deployed within the COS. The system leverages particle swarm optimization (PSO) to interrogate the models and perform real-time optimization. This work demonstrates: 1. Substantial NOx emission reductions by the station (22.5%), 2. A hybrid optimization approach applying genetic algorithm (GA) to select neural network structure and PSO to perform combustion optimization, 3. The realization of other performance benefits in an 87% reduction of high-temperature exceedances due to encapsulating operational best-practices within the platform, and 4. The sustainability of the COS in achieving a high degree of acceptance, with a final service factor of 86%.