Entropy and exergy analysis of steam passing through an inlet steam turbine control valve assembly using artificial neural networks

Agrež, Marko; Avsec, Jurij; Strušnik, Dušan

doi:10.1016/j.ijheatmasstransfer.2020.119897

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…In comparison to the previous work in the field, refs [10][11][12] it can be seen that GP implementation in this paper achieves comparable results to other works using it. The same can be said for other researchers with similar goals, such as [36] in which the used methods achieve results that are comparable to the ones achieved by GP. In comparison to the performance of the existing work in AI-based CODLAG system modeling, which used other ML algorithms it is seen that results achieved by GP are comparable or better, with the benefit of clearer models.…”

Section: Discussionmentioning

confidence: 52%

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Anđelić

Šegota

Lorencin

et al. 2021

JMSE

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In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11,934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefficient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefficients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefficients while symbolic expressions with decay state coefficients have slightly lower estimation performance.

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Section: Discussionmentioning

confidence: 52%

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Anđelić

Šegota

Lorencin

et al. 2021

JMSE

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“…Firstly, influences will be investigated of several losses on the CO 2 closed-cycle gas turbine process as for example losses through the gland seals [22], mechanical losses and additional losses related to compression or expansion processes [23], as well as other losses which can be detected inside the observed CO 2 closed-cycle gas turbine.…”

Section: Resultsmentioning

confidence: 99%

Thermodynamic Analysis of CO2 Closed-Cycle Gas Turbine for Marine Applications at Various Pressure Ratios

et al. 2022

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A thermodynamic analysis of CO2 closed-cycle gas turbine is presented in this paper. Two processes are investigated -base process and the same process upgraded with a heat regenerator. Maximum specific useful work is 159.94 kJ/kg for both observed processes. Involving of heat regenerator inside base CO2 closed-cycle gas turbine process requires attention due to required temperature differences -high pressure ratios cannot be obtained with a high efficiency heat regenerator. Base CO2 closed-cycle gas turbine process did not reach cycle efficiency higher than 25%, while for the upgraded process the cycle efficiency can reach 40% at high pressure ratio and for high regenerator efficiency. Additionally, multilayer perceptron is trained in order to achieve high quality models for estimating specific useful work and efficiency for both, base and upgraded process. As a result, MLP with three hidden layers achieved high values of R 2 score.

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“…This means that the increased vacuum in the TC has positive effects as the ST low-pressure cylinder does more work, the ST generates more power and the useful efficiency increases. The useful efficiencies of the power of the ST low-pressure cylinder, together with the TC, taking into account the heat dissipated into the environment, are calculated using Equation (21). The results of the ST low-pressure part power efficiency provided by the SMIV under optimal and actual circumstances are shown in Figure 20.…”

Section: Presentation Of Smiv Resultsmentioning

confidence: 99%

“…Similarly, some other authors have obtained the filtered data from the SCADA system. [19][20][21][22] Such methods, however, are not used only in technological but also in other processes and systems. 23 All groups of data used in the machine learning and presented in Section 3.1 were filtered in the filtering process.…”

Section: Data Preparation Filtering and Smiv Architecturementioning

confidence: 99%

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Strušnik

2021

Intl J of Energy Research

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In large thermal power facilities, thermal machines mainly fitted with steam turbines are used in thermodynamic process of converting thermal energy into mechanical work. In order to improve the efficiency of heat energy conversion into mechanical work, the increase of vacuum level in the condenser of a steam turbine was analyzed using the machine learning method. The vacuum in the steam turbine condenser increases after replacing the labyrinth gaskets with honeycomb ones, resealing the vacuum system and increasing the amount of cooling water through the condenser. The latter is controlled by algorithm, which is integrated into the steam turbine distribution control system. As a result of the increased vacuum in the steam turbine condenser, the enthalpy difference of the steam turbine low-pressure cylinder increases, and the steam turbine performs more mechanical work resulting in higher conversion efficiency. The drawback of the increased vacuum in the turbine condenser is that the flow of cooling water through the steam turbine condenser increases as well as heat dissipation from the condenser into the environment to be replaced by regenerative condensate heating. The results of the analysis show that the overall balance of the vacuum increase in the steam turbine condenser is positive, the steam turbine generates more power, and consequently the process efficiency increases by as much as 2.48 %.

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Entropy and exergy analysis of steam passing through an inlet steam turbine control valve assembly using artificial neural networks

Cited by 17 publications

References 38 publications

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Thermodynamic Analysis of CO2 Closed-Cycle Gas Turbine for Marine Applications at Various Pressure Ratios

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

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