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مجله کنترل فرآیند
جزئیات رویداد
اینستاگرام تی پی بین
همایش ، رویداد ، ژورنال
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  • online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

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    • تاریخ ارائه: 1392/01/01
    • تاریخ انتشار در تی پی بین: 1392/01/01
    • تعداد بازدید: 1097
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     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.

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