Challenges and opportunities of Rankine cycle for waste heat recovery from internal combustion engine

Tian, Hua; Liu, Peng

doi:10.1016/j.pecs.2021.100906

Cited by 71 publications

(20 citation statements)

References 320 publications

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“…al., 2021;SILVA et. al., 2019) becomes a necessary method since it is possible to prevent the stoppage of machines in a production process through indicators offered by monitoring systems, identify the small irregularities that can evolve to large failures early and thus allow for correction (AYVAZ and ALPAY, 2021;TIAN, LIU and SHU, 2021). Some methods for performing equipment-monitoring diagnostics are used in the literature (SCHWENDEMANN, AMJAD AND SIKORA, 2021;MANHERTZ and BERECZKY, 2021;LUGHOFER and SAYED-MOUCHAWEH, 2019), such as:…”

Section: Predictive Maintenancementioning

confidence: 99%

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

Silva

Nascimento

Júnior

et al. 2021

Int J Innov Educ Res

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Predictive as well as preventive maintenance are tools of maintenance programs that aim to increase or maintain the life expectancy of an equipment through computational techniques and tools. Bearing in mind that the power generation industry has a high maintenance rate with machines and / or electric generators stopped, this research aims to develop a computational model for predicting the Reliability Key Performance Indicator (KPI) to identify how available the equipment will be in a time span of 22 days, for this the methodology to be used will be based on analyzes and tests of artificial neural network (ANN) architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the layers hidden to find the best state of convergence and the minimum Root Mean Square Error (RMSE) value calculated between the real and simulated outputs. According to the results obtained by the training, validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation between the real and simulated values, thus the model is able to identify which machine will have the greatest efficiency and less efficiency within the defined time span.

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Section: Predictive Maintenancementioning

confidence: 99%

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

Silva

Nascimento

Júnior

et al. 2021

Int J Innov Educ Res

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“…La producción de este tipo de gases es consecuencia de la quema de combustibles procedentes de materiales fósiles. Los estudios estadísticos muestran que el sector del transporte en los Estados Unidos representa aproximadamente el 29% de las emisiones totales de efecto invernadero (Tian et al, 2021). Dado el aumento en la producción y uso de motores de combustión interna, los investigadores han enfatizado en la necesidad de mejorar el rendimiento de estas máquinas térmicas.…”

Section: Introductionunclassified

Alternativas de recuperación residual, contribución desde la ingeniería mecánica.

2022

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En la actualidad la demanda de energía aumenta de forma progresiva debido al crecimiento económico a nivel global, dando como resultado un incremento en el consumo de recursos fósiles. Lo anterior, produce un aumento en los precios de los combustibles y preocupación ambiental. A lo largo de los años, los investigadores han propuesto diversos tipos de tecnologías para el aprovechamiento del calor residual de los gases de escape en los motores de combustión interna con el objetivo de mejorar la eficiencia de los procesos de combustión. Este tipo de estrategias son alternativas con un alto potencial para mejorar la eficiencia de los motores y reducir las emisiones contaminantes. Entre las principales tecnologías investigadas se encuentran: los turbocompresores, los ciclos orgánicos de Rankine, recirculación de gases de escape y generadores termoeléctricos. Cada una de estas tecnologías presenten ventajas y desventajas particulares que afectan su beneficio económico, ambiental y técnico. La intrusión de este tipo de tecnologías, generalmente produce una contrapresión en el sistema de escape, lo cual ocasiona una reducción en la potencia mecánica del motor. Entre las tecnologías existentes, se destaca los generadores termoeléctricos debido a sus diversas ventajas y capacidad de minimizar la contrapresión en el sistema de gases de escape mediante rediseños de los generadores.

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“…6 A rapid expansion in the production of ICE raised serious concern about waste fuel energy. [7][8][9] Moreover, waste heat energy is also a key contributor to global warming that alters the monsoon pattern, resulting in a decrease in the volume of freshwater. 10,11 In recent decades, the demand for freshwater has expanded substantially due to the rapid growth of the world population and the improvement of living standards.…”

Section: Introductionmentioning

confidence: 99%

Energy and exergy analysis of the thermal distillation system using diesel engine waste thermal energy and radial finned tube evaporator

Kumar

Prakash

2022

Heat Trans

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The performance of the waste heat-driven distillation system was investigated. The waste heat energy in the exhaust gas of the 7-hp diesel engine generator was utilized for distillation purposes. The thermal distillation system is comprised of a submerged copper tube evaporator and water-cooled U-tube condenser. Four copper tube evaporators were used, including one plain evaporator and three radial finned evaporators with pitch 30, 60, and 90 mm. The efficiency of the thermal distillation system was determined by varying engine load and evaporator type. The net recovered energy and exergy, overall first and second law efficiency, and distillate yield rate was determined relative to the applied load and evaporator type. The results revealed that the net recovered energy and exergy improved by 6.04%-23.22% and factor 1.07-1.23, respectively, at 75% engine load for radial finned evaporator with a pitch of 30 mm relative to plane tube evaporator. The distillate yield was also improved by 23.75% at the highest applied load 75% and the lowest radial fin pitch compared with the plane tube.

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Challenges and opportunities of Rankine cycle for waste heat recovery from internal combustion engine

Cited by 71 publications

References 320 publications

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

Alternativas de recuperación residual, contribución desde la ingeniería mecánica.

Energy and exergy analysis of the thermal distillation system using diesel engine waste thermal energy and radial finned tube evaporator

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