An Intelliqent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

摘要

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artiflcial neural network is applied to model static non- linear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal- fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control al- gorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained op- timization problems, is described and its results are suc- cessfully validated against an of the-shelf tool for solving mathematical programming problems. Encouraging re- sults obtained using plant data from one of Common- wealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelli- gent controller will also enable the determination of the most cost-effective operating conditions of the FLGR sys- tem by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is