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

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

doi:10.1016/j.apenergy.2016.04.047

Cited by 63 publications

(14 citation statements)

References 55 publications

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“…An appropriate cross-section dimension of the Laval nozzle reduced the consumption of the motive steam and improved the efficiency of the steam ejector pump system. Strusnik et al [11] innovatively used the supervised machine learning modelling to evaluate the energy efficiency of a steam ejector and an electric vacuum pump for a turbine condenser air extraction system. Their study proposed basic guidelines for the selection of an appropriate condenser vacuum pumps system of a steam turbine.…”

Section: Introductionmentioning

confidence: 99%

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

et al. 2019

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Supersonic ejectors are of great interest for various industries as they can improve the quality of the low-grade heat source in an eco-friendly and sustainable way. However, the impact of steam condensation on the supersonic ejector performances is not fully understood and is usually neglected by using the dry gas assumptions. The non-equilibrium condensation occurs during the expansion and mixing process and is tightly coupled with the high turbulence, oblique and expansion waves in supersonic flows. In this paper, we develop a wet steam model based on the computational fluid dynamics to understand the intricate feature of the steam condensation in the supersonic ejector. The numerical results show that the dry gas model exaggerates the expansion characteristics of the primary nozzle by 21.95%, which predicts the Mach number of 2.00 at the nozzle exit compared to 1.64 for the wet steam model. The dry gas model computes the static temperature lower to 196 K, whereas the wet steam model predicts the static temperature should above the triple point due to the phase change process. The liquid fraction can reach 7.2% of the total mass based on the prediction of the wet steam model. The performance analysis indicates that the dry gas model overestimates a higher entrainment ratio by 11.71% than the wet steam model for the steam ejector.

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Section: Introductionmentioning

confidence: 99%

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

et al. 2019

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“…seawater desalination system provides a solution for freshwater production, which achieves energy saving by upgrading the low-grade energy [1,2]. The steam ejector, as the main part of the TVC unit, recovers the low-pressure steam from the last effect of evaporators, which accomplish high efficient utilisation of the low-grade energy [3,4].…”

Section: Introductionmentioning

confidence: 99%

Performance of steam ejector with nonequilibrium condensation for multi-effect distillation with thermal vapour compression (MED-TVC) seawater desalination system

2020

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The single-phase and two-phase flow models are developed and compared for the performance evaluation of a steam ejector for the multi-effect distillation with thermal vapour compression (MED-TVC) seawater desalination system. The results show that a single-phase flow model with ignoring the phase change predicts an unphysical temperature of the steam in the supersonic flow with the minimum value of approximately 122 K, which raises the query of the formation of the ice. The two-phase wet steam model corrects the distribution of the flow parameter by predicting the heat and mass transfer during the phase change. The steam achieves the first nonequilibrium condensation process inside the primary nozzle and another four alternating condensation and re-evaporation processes. The single-phase flow model underpredicts the entropy loss coefficient by approximately 15% than the two-phase wet steam model. The performance comparison is achieved against the single-phase model

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“…An alternative strategy to PMS is Conditioned-based Maintenance (CM), wherein the maintenance is carried out based on the condition of the machinery, which is detected by measuring several parameters during the vessel operation [ 4 ]. Fortunately, the recent development of IT technology has enabled real-time access to the machinery’s condition and energy efficiency using data collected from onboard sensors [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Such sensor-based monitoring can be used to detect abnormal behavior of the system that may indicate the degradation or fault of the system.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis

Kim¹,

Lee²,

Lee

2020

Sensors

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This study proposes an unsupervised anomaly detection method using sensor streams from the marine engine to detect the anomalous system behavior, which may be a possible sign of system failure. Previous works on marine engine anomaly detection proposed a clustering-based or statistical control chart-based approach that is unstable according to the choice of hyperparameters, or cannot fit well to the high-dimensional dataset. As a remedy to this limitation, this study adopts an ensemble-based approach to anomaly detection. The idea is to train several anomaly detectors with varying hyperparameters in parallel and then combine its result in the anomaly detection phase. Because the anomaly is detected by the combination of different detectors, it is robust to the choice of hyperparameters without loss of accuracy. To demonstrate our methodology, an actual dataset obtained from a 200,000-ton cargo vessel from a Korean shipping company that uses two-stroke diesel engine is analyzed. As a result, anomalies were successfully detected from the high-dimensional and large-scale dataset. After detecting the anomaly, clustering analysis was conducted to the anomalous observation to examine anomaly patterns. By investigating each cluster’s feature distribution, several common patterns of abnormal behavior were successfully visualized. Although we analyzed the data from two-stroke diesel engine, our method can be applied to various types of marine engine.

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

Cited by 63 publications

References 55 publications

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

Performance of steam ejector with nonequilibrium condensation for multi-effect distillation with thermal vapour compression (MED-TVC) seawater desalination system

An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis

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