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- W2353470190 abstract "In view of the high first cost of conventional oxygen content analyzers for industrial applications, their high maintenance expenses and low durability the authors have on the basis of comparing several commonly used methods come up with a new method for measuring oxygen content in flue gases. The proposed method involves an oxygen content soft sensing model set up through the use of a NNPLS (neutral network partial least square) approach based on statistical analyses and neural network technology. It enjoys both the merits of PLSR (partial least square regression) and neural network technology, making it possible to identify a target model by utilizing historical process data. A simulation verification of the method has been conducted by using on site industrial data. In addition, the simulation results are compared with traditional linear PLSR method and the direct neural network based modeling method. The results of comparison indicate that the soft sensing model based on the NNPLS approach features a more effective generalizing ability. Furthermore, an extension of a static model to a dynamic one was also performed." @default.
- W2353470190 created "2016-06-24" @default.
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- W2353470190 date "2002-01-01" @default.
- W2353470190 modified "2023-09-23" @default.
- W2353470190 title "The Study of a New Method Incorporating the Soft Sensing of Oxygen content in Flue Gases" @default.
- W2353470190 hasPublicationYear "2002" @default.
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