Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

Dušan Strušnik, Milan Marčič, Marjan Golob, Aleš Hribernik, Marija Živić and Jurij Avsec

Applied Energy, 2016, vol. 173, issue C, 386-405

Abstract: This paper compares the vapour ejector and electric vacuum pump power consumptions with machine learning algorithms by using real process data and presents some novelty guideline for the selection of an appropriate condenser vacuum pump system of a steam turbine power plant. The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models. The proposed non-linear models are designed by using a wide range of real process operation data sets from the CHP system in the thermal power plant. The novelty guideline for the selection of an appropriate condenser vacuum pumps system is expressed in the comparative analysis of the energy consumption and use of specific energy capable of work. Furthermore, the novelty is expressed in the economic efficiency analysis of the investment taking into consideration the operating costs of the vacuum pump systems and may serve as basic guidelines for the selection of an appropriate condenser vacuum pump system of a steam turbine.

Keywords: Ejector; Machine learning; Mixing section; Operating principle; Thermodynamic analysis; Vacuum pump (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:173:y:2016:i:c:p:386-405

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DOI: 10.1016/j.apenergy.2016.04.047

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