@article{8620107,
  abstract     = {{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.}},
  articleno    = {{2544}},
  author       = {{Baetens, Jens and Van Eetvelde, Greet and Lemmens, Gert and Kayedpour, Nezmin and De Kooning, Jeroen and Vandevelde, Lieven}},
  issn         = {{1996-1073}},
  journal      = {{ENERGIES}},
  keywords     = {{dynamic modelling,Adaptive Neuro-Fuzzy Inference System (ANFIS),evaporative cooling,electrical flexibility,industry}},
  language     = {{eng}},
  number       = {{13}},
  pages        = {{17}},
  publisher    = {{MDPI}},
  title        = {{Thermal performance evaluation of an induced draft evaporative cooling system through Adaptive Neuro-Fuzzy Interference System (ANFIS) model and mathematical model}},
  url          = {{http://doi.org/10.3390/en12132544}},
  volume       = {{12}},
  year         = {{2019}},
}

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