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Research Papers: Energy Systems Analysis

Estimation of Operating Parameters of a Water–LiBr Vapor Absorption Refrigeration System Through Inverse Analysis

[+] Author and Article Information
T. K. Gogoi

Department of Mechanical Engineering,
Tezpur University,
Napaam,
Tezpur 784028, India
e-mail: tapan_g@tezu.ernet.in

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received December 10, 2014; final manuscript received October 9, 2015; published online November 12, 2015. Assoc. Editor: Mansour Zenouzi.

J. Energy Resour. Technol 138(2), 022002 (Nov 12, 2015) (16 pages) Paper No: JERT-14-1402; doi: 10.1115/1.4031833 History: Received December 10, 2014; Revised October 09, 2015

In this paper, an inverse problem is solved for estimating parameters of a steam-driven water–lithium bromide (LiBr) vapor absorption refrigeration system (VARS) using a differential evolution (DE)-based inverse approach. Initially, a forward model simulates the steady-state performance of the VARS at various operating temperatures and evaporator cooling loads (CLs). A DE-based inverse analysis is then performed to estimate the operating parameters taking VARS coefficient of performance (COP), CL, total irreversibility, and exergy efficiency as objective functions (one objective function at a time). DE-based inverse technique estimates the parameters within a very short period of elapsed time. Over 50 and 100 numbers of generations are sufficient for retrieval of COP and exergy efficiency, respectively, while it requires 150 generations for total irreversibility and CL. The study reveals that multiple combinations of parameters within a given range satisfy a particular objective function which serves as design guidelines in selecting appropriate operating parameters.

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Figures

Grahic Jump Location
Fig. 2

General evolutionary algorithm procedure

Grahic Jump Location
Fig. 1

Layout of the single-effect water–LiBr VARS

Grahic Jump Location
Fig. 3

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to COP of 0.738

Grahic Jump Location
Fig. 4

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to COP of 0.813

Grahic Jump Location
Fig. 5

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to COP of 0.841

Grahic Jump Location
Fig. 6

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to CL of 7000 kW

Grahic Jump Location
Fig. 7

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to CL of 10,500 kW

Grahic Jump Location
Fig. 8

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 50 iterations in parameter estimation corresponding to CL of 14,000 kW

Grahic Jump Location
Fig. 9

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 100 iterations in parameter estimation corresponding to total irreversibility of 6175.7 kW

Grahic Jump Location
Fig. 10

Variation of (a) objective function, (b) evaporator temperature, (c) generator temperature, (d) condenser temperature, (e) absorber temperature, and (f) refrigerant flow rate with 100 iterations in parameter estimation corresponding to VARS exergetic efficiency of 11.82%

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