کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
689213 | 889597 | 2011 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems](/preview/png/689213.png)
Boiler combustion optimization is a key measure to improve the energy efficiency and reduce pollutants emissions of power units. However, time-variability of boiler combustion systems and lack of adaptive regression models pose great challenges for the application of the boiler combustion optimization technique. A recent approach to address these issues is to use the least squares support vector machine (LS-SVM), a computationally attractive machine learning technique with rather legible training processes and topologic structures, to model boiler combustion systems. In this paper, we propose an adaptive algorithm for the LS-SVM model, namely adaptive least squares support vector machine (ALS-SVM), with the aim of developing an adaptive boiler combustion model. The fundamental mechanism of the proposed algorithm is firstly introduced, followed by a detailed discussion on key functional components of the algorithm, including online updating of model parameters. A case study using a time-varying nonlinear function is then provided for model validation purposes, where model results illustrate that adaptive LS-SVM models can fit variable characteristics accurately after being updated with the ALS-SVM method. Based on the introduction to the proposed algorithm and the case study, a discussion is then delivered on the potential of applying the proposed ALS-SVM method in a boiler combustion optimization system, and a real-life fossil fuel power plant is taken as an instance to demonstrate its feasibility. Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.
► An online adaptive method, namely ALS-SVM, has been proposed for the LS-SVM model.
► The updating of model parameters with ALS-SVM is attractive in computation.
► A typical function and one real-life example validate the performance of ALS-SVM.
► Time-varying boiler combustion systems can be modeled with ALS-SVM.
► ALS-SVM holds many potential uses in the boiler combustion optimization techniques.
Journal: Journal of Process Control - Volume 21, Issue 7, August 2011, Pages 1040–1048