Total Suspended Particle Emissions Modelling in an Industrial Boiler

Guillermo Ronquillo-Lomeli, Gilberto Herrera-Ruiz, José Gabriel Ríos-Moreno, Irving Alfredo Alejandro Ramirez-Maya and Mario Trejo-Perea
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Guillermo Ronquillo-Lomeli: Department of Energy, Center for Engineering and Industrial Development, Santiago de Querétaro 76125, México
Gilberto Herrera-Ruiz: Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México
José Gabriel Ríos-Moreno: Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México
Irving Alfredo Alejandro Ramirez-Maya: Department of Energy, Center for Engineering and Industrial Development, Santiago de Querétaro 76125, México
Mario Trejo-Perea: Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México

Energies, 2018, vol. 11, issue 11, 1-17

Abstract: Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry.

Keywords: system identification; parameter estimation; system modelling; model reduction; polynomial expansion; orthogonal least square; industrial process (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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