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Neural Computing and Applications

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04.04.2020 | S. I : Hybridization of Neural Computing with Nature Inspired Algorithms

Synergetic fusion of energy optimization and waste heat reutilization using nature-inspired algorithms: a case study of Kraft recovery process

verfasst von: Smitarani Pati, Drishti Yadav, Om Prakash Verma

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

This article presents a novel energy management strategy of multiple-stage evaporator (MSE). The maximum efficiency of MSE is achieved by optimum selection of unknown steady-state process parameters such as vapor temperatures and liquor flow rates. Various energy reduction schemes (ERSs) have been integrated to achieve a substantial enhancement in energy efficiency. For energy optimization, a set of nonlinear mathematical models for various ERSs are formulated and transformed to optimization problems. Three nature-inspired algorithms, namely GA, DE and PSO, are employed to compute these optimal process parameters and hence evaluate the energy efficiency. The simulated results accentuate that these algorithms efficiently converge approximately at the same values. The results reveal that the hybrid model with maximum efficiency of 8.24 is characterized as the most energy-efficient operating strategy. The amalgamation of flash tanks with the intention of reutilizing the waste steam further enhances the energy efficiency by 4.97%, thereby proving to be the most prominent operating strategy with the highest efficiency of 8.65.

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Titel: Synergetic fusion of energy optimization and waste heat reutilization using nature-inspired algorithms: a case study of Kraft recovery process
verfasst von: Smitarani Pati
Drishti Yadav
Om Prakash Verma
Publikationsdatum: 04.04.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-04828-4

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