LAPSE:2023.21174
Published Article
LAPSE:2023.21174
Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model
March 21, 2023
The shift from fossil fuel to more renewable electricity generation will require the broader implementation of Demand Side Response (DSR) into the grid. Utility processes in industry are suited for this, having a large thermal time constant or buffer, and large electricity consumption. A widespread utility system in industry is an induced draft evaporative cooling tower. Considering the safety aspect, such a process needs to maintain cooling water temperature within predefined safe boundaries. Therefore, in this paper, two modelling methods for the prediction of the basin temperature of an induced draft evaporative cooling tower are proposed. Both a white box and a black box methodology are presented, based on the physical principles of fluid dynamics and adaptive neuro-fuzzy interference system (ANFIS) modelling, respectively. By analysing the accuracy of both models with a focus to cooling tower fan state changes, i.e., DSR purposes, it is shown that the white box model performs best. Fostering the idea of using such a system for DSR purposes, the concept of design for flexibility is also touched upon, discussing the thermal mass. Pre-cooling, where the temperature of the cooling water basin is lowered before a fan switch off period, was simulated with the white box model. It was shown that beneficial pre-cooling (to lower the temperature peak) is limited in time.
Keywords
Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Modelling, electrical flexibility, evaporative cooling, industry
Suggested Citation
Baetens J, Van Eetvelde G, Lemmens G, Kayedpour N, De Kooning JDM, Vandevelde L. Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model. (2023). LAPSE:2023.21174
Author Affiliations
Baetens J: Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium [ORCID]
Van Eetvelde G: Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium; INEOS Group, 1180 [ORCID]
Lemmens G: INEOS Group, 1180 Rolle, Switzerland
Kayedpour N: Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium [ORCID]
De Kooning JDM: Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium [ORCID]
Vandevelde L: Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium; EEDT-DC Flanders M [ORCID]
Journal Name
Energies
Volume
12
Issue
13
Article Number
E2544
Year
2019
Publication Date
2019-07-02
Published Version
ISSN
1996-1073
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Original Submission
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PII: en12132544, Publication Type: Journal Article
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LAPSE:2023.21174
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doi:10.3390/en12132544
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Mar 21, 2023
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