Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler

Abstract

This paper puts forward a new viewpoint on optimization of boiler combustion, namely, reducing NOx emission while maintaining higher reheat steam temperature rather than reducing NOx emission while improving boiler efficiency like traditional practices. Firstly, a set of multioutputs nonlinear partial least squares (MO-NPLS) models are established as predictors to predict these two indicators. To guarantee better predictive performance, repeated double cross-validation (rdCV) strategy is proposed to identify the structure as well as parameters of the predictors. Afterward, some controllable process variables, taken as inputs of the predictors, are then optimized by minimizing NOx emission and maximizing reheat steam temperature via multiobjective artificial bee colony (MO-ABC). Results show that our rdCV-MO-NPLS model with MO-ABC optimization methods can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with nondominated sorting genetic algorithm II (NSGA-II) and combustion adjustment experimental data on a real 1000 MW boiler.

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

“…In addition, Refs. [130,131] are multi-objective optimization works. Pattanayak et al [146] developed a groundbreaking optimization system based on thermodynamics and an ANN model.…”

Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

“…The flow chart of the artificial bee colony algorithm is shown in Figure 7. For more details and demos of MOABC, please refer to [29,30].…”

“…There is a tradeoff problem in the outer-loop filter design of the cascaded DOB system, which is different from that of the single-loop DOB and will be solved through a robust loop shaping design method. In order to obtain better control performance and system robustness at the same time, the inner-and outer-loop PI controllers are optimized by the Pareto-based multi-objective artificial bee colony algorithm (MOABC), which has been widely used in different fields [27][28][29][30]. Compared with other intelligent algorithms, it has the advantages of less preset parameters, a concise and clear structure and is convenient for implementation and application.…”