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.
Highlights
Boiler combustion optimization of reducing nitrogen oxide (NOx, for short) has become a hot topic during the past three decades. ere exist several currently typical ways in coping with the boiler combustion optimization problem.e first popular method is numerical simulation that is based on computational fluid dynamics (CFD) [1,2,3]
E first popular method is numerical simulation that is based on computational fluid dynamics (CFD) [1,2,3]
Researchers only focus on NOx emission reduction by using evolutional algorithms (EAs) to search the optimal set point of controllable process variables. e typical EAs include genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO)
Summary
Boiler combustion optimization of reducing nitrogen oxide (NOx, for short) has become a hot topic during the past three decades. ere exist several currently typical ways in coping with the boiler combustion optimization problem. To the best of our knowledge, there is few literature on boiler combustion optimization to reduce NOx emission and improve reheat steam temperature simultaneously. Motivated by the above statements, this paper aims to reduce NOx emission while achieving higher reheat steam temperature by proposing a jointed optimization method. En, a multiobjective artificial bee colony (MO-ABC) algorithm is adopted to search optimal inputs of predictors that can achieve lower NOx emission and higher reheat steam temperature from Pareto front. Is strategy can reduce NOx emission while maintaining higher reheat steam temperature to guarantee the safety in boiler combustion system control as well as the improvement of whole plant efficiency. On the basis of the rdCV-MO-NPLS modelling method, our proposed MO-ABC algorithm can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with NSGA-II and the experimental data.
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