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Improving reciprocating compressor performance using a hybrid two‐level optimisation approach

Improving reciprocating compressor performance using a hybrid two‐level optimisation approach Purpose – This paper seeks to describe a design approach which can be used to manufacture better‐performing reciprocating compressors. This design approach relates the drive kinematic characteristics to the thermodynamic performance of the compressor. Design/methodology/approach – The presented approach is based on employing a stochastic optimisation algorithm to find the best piston trajectory within one cycle of operation and couple that with a gradient‐based technique to find the best dimensions of the mechanism which can realise this trajectory. Findings – The mathematical models presented to implement the proposed design approach have been coded in a computer program which has been employed for simulation purposes. A case study given at the end of the paper asserts the usefulness of the proposed method and proves that a few percentage points increase in a defined set of performance indices has been gained from the optimisation exercise. Research limitations/implications – The presented models are only relevant to reciprocating compressors. Practical implications – The promising results obtained in this paper will lead to the creation of better performing and more reliable compressor drives, designed to fulfil a set of desired performance criteria. Originality/value – The paper offers originality in two different aspects. The mechanism design process has been undertaken in full consideration to the thermodynamic performance of the compressor; and the coupling of the stochastic and the gradient‐based optimisation methods to produce the desired outcome. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations: International Journal for Computer-Aided Engineering and Software Emerald Publishing

Improving reciprocating compressor performance using a hybrid two‐level optimisation approach

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References (30)

Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644401111141046
Publisher site
See Article on Publisher Site

Abstract

Purpose – This paper seeks to describe a design approach which can be used to manufacture better‐performing reciprocating compressors. This design approach relates the drive kinematic characteristics to the thermodynamic performance of the compressor. Design/methodology/approach – The presented approach is based on employing a stochastic optimisation algorithm to find the best piston trajectory within one cycle of operation and couple that with a gradient‐based technique to find the best dimensions of the mechanism which can realise this trajectory. Findings – The mathematical models presented to implement the proposed design approach have been coded in a computer program which has been employed for simulation purposes. A case study given at the end of the paper asserts the usefulness of the proposed method and proves that a few percentage points increase in a defined set of performance indices has been gained from the optimisation exercise. Research limitations/implications – The presented models are only relevant to reciprocating compressors. Practical implications – The promising results obtained in this paper will lead to the creation of better performing and more reliable compressor drives, designed to fulfil a set of desired performance criteria. Originality/value – The paper offers originality in two different aspects. The mechanism design process has been undertaken in full consideration to the thermodynamic performance of the compressor; and the coupling of the stochastic and the gradient‐based optimisation methods to produce the desired outcome.

Journal

Engineering Computations: International Journal for Computer-Aided Engineering and Software – Emerald Publishing

Published: Jul 19, 2011

Keywords: Mechanisms; Gears; Mechanical drives; Efficiency; Optimization; Compressors; Kinematics; Stochastic process; Thermodynamics; Optimization techniques

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