![]() ![]() ![]() The methods are demonstrated on data collected from a boiler in Jianbi power plant, China, and we show that a wide range of solutions trading-off NOx and efficiency may be efficiently located. We give a novel algorithm for discovering the optimal trade-off for all load demands simultaneously. We discuss the variation of operating parameters along the trade-off front. A novel evolutionary multi-objective search algorithm is used to discover the probabilistic trade-off front between NOx and UBC, and we describe a new procedure for selecting parameters yielding the desired performance. The so-called Lean-Premix Technology 1, which permits the latter to achieve emissions as low as 9 ppm (at 15 O 2 ), is not applicable to IGCC gas turbines. We introduce the use of Gaussian process models to capture the uncertainties in NOx and UBC predictions arising from measurement error and data scarcity. Available combustion-based NOx control options for syngas-fired turbines are more limited than those available for natural gas-fired turbines. Generally, an analytical model for NOx emission or UBC is unavailable, and therefore data-driven models are used to optimise this multi-objective problem. Consequently there is a range of solutions that trade-off efficiency for emissions. Optimising combustion parameters to achieve a lower NOx emission often results in combustion inefficiency measured with the proportion of unburned coal content (UBC). Nonetheless, pollutant emissions – in particular Oxides of Nitrogen (NOx) – as a result of the combustion process in a boiler, are subject to strict legislation due to their damaging effects on the environment. ![]()
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