Exergy analysis of marine waste heat recovery CO2 closed-cycle gas turbine system

Mrzljak, Vedran; Poljak, Igor; Prpić-Oršić, Jasna; Jelić, Maro

doi:10.31217/p.34.2.12

Cited by 11 publications

(9 citation statements)

References 63 publications

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“…The similar conclusion can be found in [6] for closed cycle gas turbines where the highest destruction rate occurs in main heater (or more of them). All of the combustion (or heat exchange) processes in gas or steam turbines can be improved and optimized by using various machine learning methods [7][8][9][10]. The advantage of these approaches is in very low relative error in comparison with the available data.…”

Section: Introductionsupporting

confidence: 83%

“…The combustion chambers are components which produce the highest destruction rate in open cycle gas turbines [4,5]. The similar conclusion can be found in [6] for closed cycle gas turbines where the highest destruction rate occurs in main heater (or more of them). All of the combustion (or heat exchange) processes in gas or steam turbines can be improved and optimized by using various machine learning methods [7][8][9][10].…”

Section: Introductionsupporting

confidence: 72%

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A gas turbine combustion chamber modeling by physical model

2021

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The validity of the gas turbine unit model largely depends on the accuracy of the flue gas temperature value calculation at the gas turbine inlet (TIT). This temperature is determined by the maximum combustion temperature. In variable running mode, the temperature value is regulated by changing the ratio of air and fuel at the inlet to the combustion chamber. The paper presents a model of a gas turbine combustion chamber using Modelica, an object-oriented language for modeling complex physical systems with the aim of determining the temperature of combustion flue gases, specific heat capacity, enthalpy, and flue gas composition at different gas turbine loads.

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Section: Introductionsupporting

confidence: 83%

Section: Introductionsupporting

confidence: 72%

A gas turbine combustion chamber modeling by physical model

2021

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“…All the equations related to exergy analysis of whole observed steam turbine, turbine cylinders and cylinder parts are developed in accordance to available literature [90][91][92][93][94], by using operating points presented in Fig. 1.…”

Section: Equations For the Exergy Analysis Of The Observed Steam Turbine Turbine Cylinders And Cylinder Partsmentioning

confidence: 99%

The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction

et al. 2021

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This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms – SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.

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“…These techniques have been applied in many areas of maritime research. Examples include optimization of exergy analysis of internal ship systems [2] for steam turbines [3], modeling of propulsion system parameters [4,5], ship modeling [6], vessel route optimization [7], and vessel detection [8].…”

Section: Introductionmentioning

confidence: 99%

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Šegota

Lorencin

Šercer³

et al. 2021

Pomorstvo (Online)

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Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression (SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.

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Exergy analysis of marine waste heat recovery CO2 closed-cycle gas turbine system

Cited by 11 publications

References 63 publications

A gas turbine combustion chamber modeling by physical model

A gas turbine combustion chamber modeling by physical model

The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

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