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Article type: Research Article
Authors: Adali, Tülaya; * | Bakal, Boraa | Sönmez, M. Kemalb | Fakory, Rezac
Affiliations: [a] Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA, E-mail: [email protected], [email protected] | [b] Institute for Systems Research, University of Maryland College Park, MD 20742, USA, E-mail: [email protected] | [c] Simulation, Systems & Services (S3) Technologies Company, Columbia, MD 21045, USA, E-mail: [email protected]
Correspondence: [*] Correspondence to: Prof. Tülay Adali
Note: [1] This work was supported by Maryland Industrial Partnerships and S3 Technologies under grants no: MIPS-1214.12 and MIPS-1214.24.
Abstract: This paper presents a time delay neural network (TDNN) model designed for the prediction of nitrogen oxides (NOx ) and carbon monoxide (CO) emissions from a fossil fuel power plant. NOx and CO emissions of the plant are determined as a function of other related time-series such as air ow rates and oxygen levels that are measured during the system operation. Correlation analysis is performed on the data to determine the location and the spread of cross-correlation between pairs of variables and this information is used to form a variable tapped delay line at the input of the network. We also introduce a neural network based preprocessor which employs an iterative regularization scheme to recover missing portions of CO data that are censored due to saturation of the measuring device. Prediction after training with the restored data set is observed to be significantly more accurate.
DOI: 10.3233/ICA-1999-6104
Journal: Integrated Computer-Aided Engineering, vol. 6, no. 1, pp. 27-40, 1999
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