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

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
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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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Abbreviations

λ :

Latent heat of vaporization (kJ/kg)

A :

Heat transfer area (m2)

Cp :

Specific heat

f :

Feed

h :

Enthalpy (kJ/h)

h c :

Enthalpy of condensate (kJ/h)

h LP :

Liquor enthalpy for product flash tank

H :

Enthalpy of vapor (kJ/h)

H vp :

Enthalpy of vapor out from product flash tank (kJ/h)

i :

Stage number

J :

Objective function

k :

Feed split fraction

L :

Liquor flow rate (kg/s)

L f :

Weak liquor feed flow rate

L p :

Concentrated product liquor flow rate

m :

Vapor fraction of preheater send to the seventh stage

U :

Overall heat transfer coefficient (kW/m2°C)

V :

Vapor flow (kg/s)

V p :

Vapor out from product flash tank

X :

Concentration of liquor

y :

Steam split fraction

z :

Decision variables

CFV:

Condensate flash vessel

CO:

Condensate out

DE:

Differential evolution

ERS:

Energy reduction scheme

FFT:

Feed flash tank

GA:

Genetic algorithm

NIA:

Nature-inspired algorithm

PFT:

Product flash tank

PH:

Preheater

PSO:

Particle swarm optimization

SNLAE:

Set of nonlinear algebraic equations

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar, India

    Smitarani Pati, Drishti Yadav & Om Prakash Verma

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  1. Smitarani Pati
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Pati, S., Yadav, D. & Verma, O.P. Synergetic fusion of energy optimization and waste heat reutilization using nature-inspired algorithms: a case study of Kraft recovery process. Neural Comput & Applic 33, 10751–10770 (2021). https://doi.org/10.1007/s00521-020-04828-4

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