Energy and exergy analysis of LiBr-H<sub>2</sub>O-operated vapour absorption refrigeration system using the ANN approach

Singh, Dheerendra Vikram; Verma, Tikendra Nath

doi:10.1080/01430750.2019.1670727

Cited by 12 publications

(2 citation statements)

References 22 publications

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“…ANN is feasible across all scientific domains [17]. From healthcare [18] to automotive [19], football [20] to energy systems [21][22], artificial neural networks have revolutionized the way we approach complex problems. Its versatility and wide range of applications make artificial neural networks an indispensable tool for scientists and researchers in various fields [23].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

BAYIR,

KILIÇ,

DİRİ

et al. 2024

Preprint

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The G7 countries, consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States, have important collaborations in energy production to achieve energy security. One of the main systems of Artificial intelligence's, artificial neural networks (ANN), is crucial to this area of study, comparatively using Multiple Linear Regression (MLR) comparatively. ANN and MLR are feasible to use across the G7 countries' total energy production numbers, and these numbers were determined using ANN and MLR forecasting techniques. The data included the years 1990–2020, focusing on GDP intensity, refined oil product production, electricity production, and renewable energy proportion. In ANN modeling, the best regression results at 10*10 have been obtained with two hidden layers. All regression values were 0.99947, with the training regression value being 0.99912, the validation regression value being 0.99997, and the test regression value being 0.99997. The results showed high accuracy, with regression scores exceeding 99% and smaller prediction error values. A paired sample t test has been applied to see whether the distinction between the average values is significant or not. The results of the test between the actual and predicted values (p = 0.7462 > 0.05) revealed that the forecasted values have been quite close to the actual values. Total energy production Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 4.364, 34.072, 5.837, and 0.162, respectively. The research proved that ANNs are effective in forecasting total energy output. And, with MLR, error values for MAD, MSE, RMSE, and MAPE were also found to be 5.364, 34.352, 5.861, and 1.609, respectively, using MLR modeling. By 2035, the G7 nations are expected to produce 50,652.746 Mtoe of energy collectively. The research proved that ANNs are effective in forecasting total energy output.

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Section: Introductionmentioning

confidence: 99%

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

BAYIR,

KILIÇ,

DİRİ

et al. 2024

Preprint

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“…The difference between the reversible and irreversible work transfers is the rate of exergy destruction of the system. The term T a S gen has been introduced by Gouy‐Stodola, which is the rate of exergy destruction/losses and which depends on the rate of entropy generation and ambient temperature 26,27 . A new performance analyzing parameter has been introduced to various types of HExs, called entransy.…”

Section: Introductionmentioning

confidence: 99%

Experimental exergy and entransy analyses on designed and fabricated crossflow heat exchanger

et al. 2020

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A crossflow heat exchanger (CFHEx) is designed and fabricated in a workshop. For designing this heat exchanger (HEx), the number of passes, frontal areas, HEx volumes, heat transfer areas, free‐flow areas, ratios of minimum free‐flow area to frontal area, densities, mass flow rates of flowing fluids, maximum/minimum heat capacities, heat capacity ratio, outlet temperatures of hot/cold fluids, average temperatures, mass velocities, Reynolds numbers, and convective heat transfer coefficients are evaluated by considering Colburn/friction factors. After fabrication of the HEx, effectiveness, exergy destruction, entransy dissipation, entransy dissipation‐based thermal resistance, entransy dissipation number, and entransy effectiveness for hot/cold fluids sides are found at different flow rates and inlet temperatures of fluids. By experimental results, optimum operating conditions are found, which gives maximum effectiveness and entransy effectiveness but minimum rates of exergy destruction, entransy dissipation, entransy dissipation‐based thermal resistance, and entransy dissipation number for the fabricated CFHEx. This study is concluded as follows: minimum exergy destruction and entransy dissipation rates (ie, 3.061 kJ/s·K and 1125.44 kJ·K/s, respectively) are found during experiment 2. Maximum entransy effectiveness of hot/cold fluids (ie, 0.689/0.21) is achieved in experiment 1. Moderate values of entransy dissipation number (ie, 4.689), entransy dissipation‐based thermal resistance (ie, 0.04 s·K/J), exergy destruction (ie, 3.845 kJ/s·K), and entransy dissipation (ie, 1374.04 kJ·K/s) rates are found during experiment 1. Maximum effectiveness (ie, 0.4) for the fabricated HEx is also obtained through experiment 1. After comparative analyses, it is found that experiment 1 provides optimum results, which shows the best performance of the fabricated HEx.

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Comparative Exergetic Analysis of Vapour Absorption Refrigeration System Using Three Different Absorbent Refrigerant Fluids: LiBr–H2O, LiCl–H2O, and NH3–H2O

Kumar,

Ambikesh

2024

Lecture Notes in Mechanical Engineering

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Energy and exergy analysis of LiBr-H₂O-operated vapour absorption refrigeration system using the ANN approach

Cited by 12 publications

References 22 publications

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

Experimental exergy and entransy analyses on designed and fabricated crossflow heat exchanger

Comparative Exergetic Analysis of Vapour Absorption Refrigeration System Using Three Different Absorbent Refrigerant Fluids: LiBr–H2O, LiCl–H2O, and NH3–H2O

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Energy and exergy analysis of LiBr-H2O-operated vapour absorption refrigeration system using the ANN approach

Cited by 12 publications

References 22 publications

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

Experimental exergy and entransy analyses on designed and fabricated crossflow heat exchanger

Comparative Exergetic Analysis of Vapour Absorption Refrigeration System Using Three Different Absorbent Refrigerant Fluids: LiBr–H2O, LiCl–H2O, and NH3–H2O

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Energy and exergy analysis of LiBr-H₂O-operated vapour absorption refrigeration system using the ANN approach