Modeling and coordinative optimization of NO x emission and efficiency of utility boilers with neural network

Abstract

An empirical model to predict the boiler efficiency and pollutant emissions was developed with artificial neural networks based on the experimental data on a 360 MW W-flame coal fired boiler. The temperature of the furnace was selected as an intermediate variable in the hybrid model so that the predictive precision of NO x emissions was enhanced. The predictive precision of the hybrid model was improved compared with the direct model. Three optimal operational objects were proposed in order to minimize the fuel and environmental costs. Based on the neural network model and optimal objects, a genetic algorithm was employed to seek real-time solution every 30 seconds. Optimum manipulated variables such as excess air, primary air and secondary air were obtained under different optimal objects. The above algorithm interconnected with a distributed control system (DCS) formed the supervisory control and achieved real-time coordinated optimization control of utility boilers.

Kinetic analysis of NO-Char reaction

“…The reaction of nitrogen monoxide with char is of special interest due to its importance in producing or reducing NO emission from combustion system such as fluidized bed or pulverized coal combustion boiler [1][2][3][4][5][6][7]. Most published studies focused on common temperatures in fluidized bed, which are between 873 and 1,273 K [8][9][10][11].…”

“…where X is carbon conversion defined by: (4) At constant temperature, under kinetic control, the rate is a function of the concentration of reactants. Assuming first-order dependence on carbon concentration and pseudo-zero-order dependence on gas concentration (constant partial pressure of the reactant gas or excess gas), the specific reaction rate can be written as: (5) In general, the carbon active sites concentration can be written as a function of carbon conversion [9,16,17], i.e., C C =f(X).…”

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

“…is issue with multiobjective optimization in boiler combustion systems has gained great attention at the second stage. Traditionally, boiler combustion optimization is implemented to reduce NOx emission and increase boiler efficiency by EAs (for example, see works [7][8][9][10]). ANNs with multiobjective GA (MOGA) are employed on boiler combustion optimization.…”

Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers

“…Due to the complexity of formation mechanism of NO x , neither static principle models nor dynamic principle models can be calculated accurately on-line. For this issue, experimental models are usually used in practice [1][2][3][4][5]. Static experiments take a long time, and the average value can reduce the error caused by the disturbances effectively, so the static models are usually reliable [1,[4][5][6][7].…”

“…For this issue, experimental models are usually used in practice [1][2][3][4][5]. Static experiments take a long time, and the average value can reduce the error caused by the disturbances effectively, so the static models are usually reliable [1,[4][5][6][7]. Taking account of the security of the units, step response is the common choice in dynamic experiments.…”