Machine learning methods for precise calculation of temperature drop during a throttling process

Farzaneh–Gord, Mahmood; Rahbari, Hamid Reza; Mohseni-Gharyehsafa, Behnam; Тойкка, А. М.; Zvereva, Irina A.

doi:10.1007/s10973-019-09029-3

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…ML is an application of artificial intelligence (AI) where systems automatically learn, develop knowledge, and improve the ability to predict with more accuracy with experience [62][63][64][65][66][67][68]. Shankar et al [65] developed an ANN approach to predict interface temperature.…”

Section: Ann-based Heat Transfer Predictionmentioning

confidence: 99%

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Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Bhattacharyya

Sarkar²,

Roy

et al. 2021

Sustainability

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In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

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Section: Ann-based Heat Transfer Predictionmentioning

confidence: 99%

“…Shankar et al [65] developed an ANN approach to predict interface temperature. Farzaneh-Gord et al [66] employed machine learning for predicting the temperature in the throttle process. Jin et al [67] also employed the same approach for determining the auto-ignition temperature.…”

Section: Ann-based Heat Transfer Predictionmentioning

confidence: 99%

Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Bhattacharyya

Sarkar²,

Roy

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…The results indicate that the developed machine learning methods exhibit high accuracy in calculations over a wide range of gas mixtures and input properties. [6] Englart et al [7], [8] propose an auxiliary system based on renewable energy and heat pumps to support or completely replace traditional gas boilers. Results show that it is possible to reduce consumption by up to about one third, for the applications considered in the case study.…”

Section: Introductionmentioning

confidence: 99%

Data-driven modelling for gas consumption prediction at City Gate Stations

Cheli

Meazzini²,

Busi³

et al. 2022

J. Phys.: Conf. Ser.

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City Gate Stations (CGS) are critical elements of Natural Gas (NG) distribution systems, as they connect national high-pressure transmission networks with local low-pressure networks. One of their main tasks is to pre-heat the gas to avoid dangerous sub-cooling due to the Joule-Thompson effect after the pressure reduction stage. For this process, significant amounts of thermal energy are required, usually obtained by burning part of the gas flow rate. This work aims to develop a data-driven model that will serve as a tool to predict and monitor the thermal consumption of the CGS. The plant chosen as a case study for this activity is in a region of central Italy. A Multiple Linear Regression (MLR) model is developed and trained, and its predictive performance is evaluated. The model results achieved an accuracy of over 95% for the coefficient of determination. The method makes it possible to create a baseline consumption model and evaluate the performance of the CGS over time using the CUSUM technique to find variations between actual and modelled gas consumption, being an essential tool for monitoring the effectiveness of the NG preheating system.

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Optimal design of microphone array in a planar circular configuration by genetic algorithm enhanced beamforming

Khatami

Jamalabadi

2020

J Therm Anal Calorim

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Machine learning methods for precise calculation of temperature drop during a throttling process

Cited by 12 publications

References 26 publications

Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Data-driven modelling for gas consumption prediction at City Gate Stations

Optimal design of microphone array in a planar circular configuration by genetic algorithm enhanced beamforming

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