An analysis of a thermal power plant working on a Rankine cycle: A theoretical investigation

Kapooria, R.K.; Kumar, S. T. A.; Kasana, K. S.

doi:10.17159/2413-3051/2008/v19i1a3314

Cited by 39 publications

(18 citation statements)

References 2 publications

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“…Well-designed heat exchangers, hot and cold liquids flow with little pressure loss. Thermal energy from steam into mechanical energy through turbine turns, which runs a generator to produce electrical energy obtained from the Rankine Cycle as a system ( Figure 1) [9]. Efficiency thermal of ORC will be determined by equation 1 as follow :…”

Section: Organic Rankine Cyclementioning

confidence: 99%

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Design and Manufacturing of Organic Rankine Cycle (ORC) System Using R-134a as Working Fluid with Solar Collector as Source Energy

Martin¹,

Naibaho²,

Kurniawan³

et al. 2019

Proceedings of the International Conference of CELSciTech 2019 - Science and Technology Track (ICCELST-ST 2019)

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Renewable energy sources such as solar heat, wind energy, geothermal, biomass and exhaust heat is one way to overcome the crisis of energy and environment in the world. Organic Rankine Cycle (ORC) is a power generation system modified from Rankine Cycle by using organic liquid with low boiling point as working fluids such as R-134a, therefore various types of heat sources can be use. This research was design and manufacture organic Rankine cycle by using R-134a as working fluid with solar collector as the source of energy. The design of this research resulted in the length of the evaporator tube is 14.5 m, length of condenser is 13.9 m and the length of solar collector tube is 9.4 m. The helical Diameter of evaporator and condenser are 250 mm, and the high of helical evaporator 290 mm and condenser 280 mm. Organic Rankine cycle system is designed to produced 534.4 Watt, but when the experiment is carried out the system generates energy of 305 Watt with the efficiency of 4.30%

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Section: Organic Rankine Cyclementioning

confidence: 99%

“…To found the velocity value of fluid flow (9) Then the value of Reynold Numbers (10) To found the Nusselt Number value (11) To calculate convection coefficient inner and outer (12) To determine Thermal Resistance (R) and Overall heat transfer coeficient (U) (13) (14)…”

Section: Parameter Designmentioning

confidence: 99%

Design and Manufacturing of Organic Rankine Cycle (ORC) System Using R-134a as Working Fluid with Solar Collector as Source Energy

Martin¹,

Naibaho²,

Kurniawan³

et al. 2019

Proceedings of the International Conference of CELSciTech 2019 - Science and Technology Track (ICCELST-ST 2019)

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“…Steam is produced by heating the water with the help of heating sources, such as fossil fuel, nuclear heat energy, and solar heat energy. The steam generated spins a steam turbine that drives an electrical generator, thus producing electrical power that is then added to the grid through the main transformer [1][2][3]. Thermal power plants are becoming more successful due to technological advancements, and demand for them is increasing [4].…”

Section: Introductionmentioning

confidence: 99%

Data Driven Leakage Detection and Classification of a Boiler Tube

Sohaib

Kim

2019

Applied Sciences

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Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

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“…When applied to an entire Rankine cycle-based power plant, exergy analysis has revealed that the boiler is responsible for the greatest destruction of power generation capacity, rather than the condenser (which discharges the greatest amount of energy to the environment) [27]. Analysis of a Rankine cycle-based thermal power plant was presented by Kapooria et al [14], and an extensive overview of various energyand exergy-based efficiencies used in the analysis of power cycles was provided by Kanoglu et al [13].…”

Section: Introductionmentioning

confidence: 99%