Optimizing screw machines using an evolution strategy with extended creation scheme

Berlik, Stefan; Helpertz, Markus

doi:10.1109/icsmc.2002.1176382

Cited by 2 publications

(1 citation statement)

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“…It makes use of the conjugacy conditions when solved explicitly and enables a variety of primary arcs to be defined that allows it to generate a higher efficiency profile [4]. A breakthrough in the optimisation of screw machines using evolution strategy (ES) has been carried out by Berlik et al (2002) [5]. The limitations of using the ES in optimisation of screw machines has been discussed along with how the unsuitability of the penalty function method led to the extended offspring generation scheme.…”

Section: Introductionmentioning

confidence: 99%

On performance optimisation for oil-injected screw compressors using different evolutionary algorithms

Kumar

Kovacevic

Ponnusami

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Energy consumed for pressurizing air makes a significant proportion of total electrical energy consumption worldwide. To reduce the carbon footprint, it is necessary to have air compressors, which can operate efficiently over a large range of pressures and flow including full load and part load conditions. Several studies have been performed in this area including some which monitor the performance of a large number of compressors to develop strategies for their designs. This paper focuses on the design optimisation of geometrical and oil parameters of oil-injected screw compressors using different evolutionary algorithms such as genetic algorithm (GA), covariance matrix adaptation evolution strategy (CMA-ES), and so on. A comparison of the performance of these algorithms is presented. SCORG and GT-SUITE (commercial software tools for screw compressor thermodynamic simulations and optimisation) are used in the integrated model producing promising results. Feasibility of the optimum outcomes generated by these algorithms is critically evaluated from machine and system design point of view. Finally, in the context of optimisation presented here, the simplex converges fastest as compared to other algorithms. In the future study, the system design limitations are to be incorporated as constraints for the optimisation along with the objective to improve energy efficiency.

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Section: Introductionmentioning

confidence: 99%