Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network

Heng, Shye Yunn; Asako, Yutaka; Suwa, Tohru; Nagasaka, Ken

doi:10.1016/j.renene.2018.07.037

Cited by 42 publications

(7 citation statements)

References 37 publications

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“…The outlet temperature of the fluid during one day is obtained with a mean absolute deviation of 2K with the ANN model, and its calculation lasted only 1 minute on a personal computer, which is short compared to the Finite Element Method achieving the same accuracy (Heng et al, 2019). This study confirms the rapidity of an ANN model to estimate the solar field performances compared to traditional models.…”

Section: Solar Thermal Plant Modelingsupporting

confidence: 62%

Analysis and future perspectives for the application of Dynamic Real-Time Optimization to solar thermal plants: A review

et al. 2022

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Section: Solar Thermal Plant Modelingsupporting

confidence: 62%

Analysis and future perspectives for the application of Dynamic Real-Time Optimization to solar thermal plants: A review

et al. 2022

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“…The ANN model accurately estimated 86.77% of hourly values within a deviation of less than 3 MW h, making it a reliable and readily usable tool for estimating energy production in a PTSTPP. Heng et al [24] utilized an ANN to predict the temperature rise at the exit caused by a single heat flux pulse in the first step of their methodology. Subsequently, they employed superposition to predict the cumulative effects resulting from multiple heat flux pulses in the second step.…”

Section: Rementioning

confidence: 99%

Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network

Li,

Xia,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.

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“…It was discovered that the improvements were monitored for low stream rates, higher temperature, and larger nanoparticle focuses. Heng et al(2019) presented a fast and accurate transient thermal prediction method to predict the parabolic trough collector tube exit temperature. The artificial neural network is combined with the principles of superposition.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal forecasting of thermal conductivity and storage in parabolic trough collector using phase change materials

Ponnusamy¹,

Balaji²

2021

JER is an international, peer-reviewed journal that publishes f

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Renewable energy is one of the cleaner energy generation strategies practiced all over the world to reduce environmental impacts and waste based on current sustainability in economic practices. Solar energy is one kind of renewable sources of energy practiced for different application. The thermal storage system in solar energy is one of the least practiced methods in research, and the utilization of solar energy in the thermal application is attaining higher responses and is quite possible. In this paper, solar heat generation is attained by solar parabolic trough collector using phase change materials. The ideology behind this research is to develop a thermal energy storage system using solar collectors and phase change materials. A composition mixture of MgCl2. 6H2O phase change materials used as the fluid medium in trough collector and thermal efficiency of the material is evaluated. For effective optimization, an imperialist competitive algorithm is used for optimizing the thermal efficiency of the solar collectors. The thermal efficiency of the collector is numerically experimented in the running platform of Mat Lab and executed in terms of heat gain, heat loss, and thermal efficiency of the parabolic trough collector, respectively. The efficiency of the proposed framework is 85%, and the current framework just has 80% efficiency. The heat loss in the proposed framework is lower than that of the current system, distinguished as 4200 W and 4520 W, respectively. It is shown from the research study that the proposed PCM composition is an optimal method for generating heat energy in solar parabolic trough collectors.

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Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network

Cited by 42 publications

References 37 publications

Analysis and future perspectives for the application of Dynamic Real-Time Optimization to solar thermal plants: A review

Analysis and future perspectives for the application of Dynamic Real-Time Optimization to solar thermal plants: A review

Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network

Optimal forecasting of thermal conductivity and storage in parabolic trough collector using phase change materials

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