Empirical data-driven multi-layer perceptron and radial basis function techniques in predicting the performance of nanofluid-based modified tubular solar collectors

Sadeghi, Gholamabbas; Pisello, Anna Laura; Nazari, Saeed; Jowzi, Mohammad; Shama, Farzin

doi:10.1016/j.jclepro.2021.126409

Cited by 35 publications

(10 citation statements)

References 41 publications

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“…Some reassuring types of ANN techniques are applicable in engineering problems, such as multi-layer perceptron and radial basis function (Sadeghi et al, 2021), both of which shall be briefly expounded in the following sections.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A comparative study of forecasting methods using real-life econometric series data

Matta

Bianchesi

Oliveira

et al. 2021

Prod.

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Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings:The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice:This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A comparative study of forecasting methods using real-life econometric series data

Matta

Bianchesi

Oliveira

et al. 2021

Prod.

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“…Nanoparticles are another technique for improving the collectors' efficiency (Qu et al, 2019;Mahmoudi et al, 2020;Alawi et al, 2021;Sadeghi et al, 2021). These materials have widely been examined by many researchers (Osman et al, 2019;Aghakhani et al, 2020;Eshgarf et al, 2021;Mahdavi et al, 2020;Nguyen et al, 2020;.…”

Section: Second Scenariomentioning

confidence: 99%

Transient Thermal Analysis of a Solar-Assisted AHU by Focusing on Heat Recovery and Nanoparticles: Jeddah Climate Zone

Khetib

2021

Front. Energy Res.

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In the Jeddah climate region, a lot of energy is assigned to the air handling unit (AHU) sector, which should be reduced by using energy-efficient solutions. As the air passes through the cooling coil, a lot of energy is consumed to reduce the temperature along with humidity so that if the air is precooled in the previous stages, energy consumption in this energy-intensive section will be diminished. Using the coldness of the return air in the heat recovery unit (HRU), the incoming air is precooled. Based on the thermodynamic calculations, in June, July, and August, the cooling coil power demand reduces by 11.6, 13.3, and 12%, respectively. In summer, owing to using HRU, an energy-saving by 76.08 MWh is achieved (12.34% reduction in energy demand). By the incorporation of the solar collectors in the AHU, heating coil demand diminishes by 1,206, 1,399, and 1,367 kWh in June, July, and August, respectively. To improve the solar-assisted AHU effectiveness, the MWCNT nanoparticles are injected into the collectors, and it is found that the saving-energy capability improves by 17.7% using MWCNT-water at 0.1 vol.%.

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“…However, for fluids with a higher Prandtl number, the thickness of the thermal boundary layer is smaller than the momentum boundary layer, and measurement of the temperature profile is practically impossible. Hence for such systems, the overall HTC is measured experimentally and then the process side HTC is estimated by measuring or estimating the other resistances as listed in Equations ( 1) and (2).…”

Section: Introductionmentioning

confidence: 99%

“…Such efforts are also finding applications in the field of chemical engineering in developing correlations as well as advanced process control activities. 1,2 The artificial intelligence and machine learning tools have been proven to be useful in terms of improvement in data correlation for many complex systems such as bubble column, 3 packed bed systems, 4 stirred tank systems. 5 The classical design procedures in chemical engineering have been more of art and experience than the exact science since inception.…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of advanced mathematical tools and computing power, machine learning and big data analysis are now routinely being used in the field of engineering, finance, climate change, social sciences for data correlation. Such efforts are also finding applications in the field of chemical engineering in developing correlations as well as advanced process control activities 1,2 . The artificial intelligence and machine learning tools have been proven to be useful in terms of improvement in data correlation for many complex systems such as bubble column, 3 packed bed systems, 4 stirred tank systems 5 …”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence‐based correlation: Process side heat transfer coefficient for helical coils in stirred tank reactors

Moholkar

Vala²,

Mathpati

et al. 2021

Heat Trans

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Stirred tank reactor systems often employ jackets/ limpets, single/multiple internal coils for heat transfer. Internal coils are used for heat removal in the case of exothermic reaction systems. The desired heat transfer area can be accurately estimated based on the overall heat transfer coefficient (HTC) considering the process side, utility side, and wall conduction resistance. For estimation of process side HTC, a large number of empirical/semi-empirical correlations are available in the published literature. These correlations account for impeller Reynolds number, fluid Prandtl number, viscosity ratio, dimensionless geometric factors, and so forth. However, all the factors over the entire range of industrial operation do not get covered in a single study, hence the predictions may involve significant errors for industrial cases. The advanced artificial intelligence-based techniques can provide unified correlation based on the experimental data set extracted from various investigations and the predictions can be accurate and

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Empirical data-driven multi-layer perceptron and radial basis function techniques in predicting the performance of nanofluid-based modified tubular solar collectors

Cited by 35 publications

References 41 publications

A comparative study of forecasting methods using real-life econometric series data

A comparative study of forecasting methods using real-life econometric series data

Transient Thermal Analysis of a Solar-Assisted AHU by Focusing on Heat Recovery and Nanoparticles: Jeddah Climate Zone

Artificial intelligence‐based correlation: Process side heat transfer coefficient for helical coils in stirred tank reactors

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