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

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 i...

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Bibliographic Details
Published in:Neural computing & applications 2021-09, Vol.33 (17), p.10751-10770
Main Authors: Pati, Smitarani, Yadav, Drishti, Verma, Om Prakash
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Energy conversion efficiency
Energy efficiency
Energy management
Evaporators
Flow velocity
Image Processing and Computer Vision
Mathematical models
Optimization
Probability and Statistics in Computer Science
Process parameters
S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
Special Issue on Hybridization between Neural Computing and Nature Inspired Algorithms for Solving Multi-Criteria Decision-Making Problems
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04828-4