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Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011
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ABSTRACT: In safety engineering, lower and upper explosion limits are the important indices to evaluate the safety of multi-component explosive gas mixture such as hydrogen and methane. There is a nonlinear dependence of explosion limits on the composition (components and theirs concentration) of multi-component explosive gas mixture. Therefore, a least square support vector regression (LS-SVR) model was proposed to establish a non-linear model between the composition of the explosive mixture and the explosion limits. The results show that the LS-SVR model predicted explosion limits with good accuracy. The selection of input variables for the LS-SVR showed significant effect on the predictive accuracy.
E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on; 12/2010
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ABSTRACT: The present work focuses on low NO<sub>x</sub> emissions combustion modification of a 300MW dual-furnaces coal-fired utility boiler through a combination of support vector regression (SVR) and a novel and modern differential evolution optimization technique (DE). SVR, used as a more versatile type of regression tool, was employed to build a complex model between NO<sub>x</sub> emissions and operating conditions by using available experimental results in a case boiler. The trained SVR model performed well in predicting the NO<sub>x</sub> emissions with an average relative error of less than 1.14% compared with the experimental results in the case boiler. The optimal ten inputs (namely operating conditions to be optimized by operators of the boiler) of NO<sub>x</sub> emissions characteristics model were regulated by DE so that low NO<sub>x</sub> emissions were achieved, given that the boiler load is determined. Two cases were optimized in this work to check the possibility of reducing NO<sub>x</sub> emissions by DE under high and low boiler load. The time response of DE was typical of 20 sec, at the same time with the better quality of optimized results. Remarkable good results were obtained when DE was used to optimize NO<sub>x</sub> emissions of this boiler, supporting its applicability for the development of an advanced on-line and real-time low NO<sub>x</sub> emissions combustion optimization software package in modern power plants.
Natural Computation (ICNC), 2010 Sixth International Conference on; 09/2010
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ABSTRACT: NO<sub>x</sub> emission from coal combustion poses terrible threat to the surrounding environment. In order to mitigate NO<sub>x</sub> emission for coal combustion of a coal-fired boiler, a nonlinear regression model based on support vector regression (SVR) was employed to build a relationship between NO<sub>x</sub> emissions and operating parameters of the case boiler. Then, an optimization tool based on simulated annealing (SA) was utilized to regulating the combustion parameters of the case boiler aiming to achieve low NO<sub>x</sub> emission. The six levels of secondary air velocities and four levels of primary air velocities were chosen as design variables. Remarkable good results were obtained when SA was used to optimize NO<sub>x</sub> emissions of this boiler, supporting its applicability for the development of an advanced on-line and real-time low NO<sub>x</sub> emissions combustion optimization software package in modern power plants.
Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on; 08/2010
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ABSTRACT: Nitrogen oxide (NO<sub>x</sub>) is one of main pollutants emitted from coal fired power plants and is a significant pollutant source in the environment. Therefore, the monitoring or prediction of NO<sub>x</sub> emissions is an indispensable process in coal-fired power plant so as to control NO<sub>x</sub> emissions. In this paper, NO<sub>x</sub> emissions modeling for real-time operation and control of a 300MWe coal-fired power generation plant is studied. A least square support vector regression (LS-SVR) model was proposed to establish a non-linear model between the parameters of the boiler and the NO<sub>x</sub> emissions. The results show that the LS-SVR model predicted NO<sub>x</sub> emissions with good accuracy. LS-SVR model is much more accurate than the GRNN model previously reported by the authors. LS-SVR model will be a good alternative to a neural network based model which is commonly used to implement the predictive emission monitoring system (PEMS).
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on; 07/2010
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ABSTRACT: Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, the control parameters are critical to the performance of SVR and also difficult to be selected. For actual applications in most cases, self-modeling of studied systems without any manual operation was needed. In this work, ant colony (ACO) optimization was developed to search the optimal control parameters so as to achieve this purpose. ACO is a meta-heuristic optimization algorithm for solving both discrete and continuous optimization problems. As a case study to demonstrate the applicability of the proposed method, SVR model is constructed for correlating historic data comprising values of operating and output variables of a boiler. Parameters selection was performed with the help of ACO. Next, model inputs describing process operating variables are also optimized using ACO with a view to maximize the combustion efficiency of the boiler. The results showed that the proposed approach, by comparing with neural network model, was an efficient way to model boiler in automation style with good predictive accuracy. ACO and SVR provide a useful tool for maximizing the combustion efficiency of boiler. Also, the method can be easily extended to other applications.
Natural Computation, 2008. ICNC '08. Fourth International Conference on; 11/2008
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ABSTRACT: The current study presented a generalized regression neural network (GRNN) based approach to predict nitrogen oxides (NOx) emitted from coal-fired boiler. A novel 'multiple' smoothing parameters, which is different from the standard algorithm in which only single smoothing parameter was adopted (Matlab neural network toolbox, for example), were assigned to GRNN model. K-means clustering algorithm was developed so as to reduce the number of smoothing parameters. The training data was firstly partitioned into groups (the number of groups was much smaller than that of training samples) using K-means clustering. A smoothing parameter was then assigned to this group.??A recently emerging estimation of distribution algorithm (EDA) was employed to optimize the multiple smoothing parameters. EDA presented in this paper was a kind of optimization algorithm based on Gaussian probability distribution. As a case study, the proposed approach was applied to establish a non-linear model between the parameters of the coal-fired boiler and the NOx emissions. The results showed that the number of cluster has significant effect on the predictive accuracy of GRNN model. GRNN model with multiple smoothing parameters showed better agreement than that with only one smoothing parameter. The modeling errors on the testing subset were 1.24% and 1.62% for GRNN models trained by the present algorithm and the standard algorithm, respectively.
International Conference on Natural Computation. 10/2008; 2:91-95.
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ABSTRACT: The formation of nitrogen oxides (NOx) associated with coal combustion systems is a significant pollutant source in the environment as the utilization of fossil fuels continues to increase, and the monitoring of NOx emissions is an indispensable process in coal-fired power plant so as to control NOx emissions. A novel "one-pass" neural network, generalized regression neural network (GRNN) was proposed to establish a non-linear model between the parameters of the boiler and the NOx emissions. The selection of the GRNN model's parameter is discussed. The method presented in this paper is applied to a case boiler of 300 MW steam capacity. The results show that the GRNN model predicted NOx emissions much more accurate than the widely-used "iterative" BPNN model and the multiple linear regression model. The main advantage of the GRNN model, by comparing with the traditional BPNN model, consists of the certainty of the predictive result, simplicity in network structure, quick convergence rate and much better predictive accuracy, especially for the case with a very large number of training samples. This approach will be a good alternative to the BPNN model which is commonly used to implement the predictive emission monitoring system (PEMS).
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on; 06/2008
