Power plant condenser performance forecasting using a non-fully connected artificial neural network

Prieto, M.M.; Montañés, Elena; Menéndez, Omar

doi:10.1016/s0360-5442(00)00046-3

Cited by 41 publications

(12 citation statements)

References 5 publications

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“…The ANN approach has been applied to predict the performance of various thermal systems [15][16][17][18][19][20][21][22][23].An artificial neural network (ANN) was used for the long-term performance prediction of thermosyphonic type solar domestic water heating (SDWH) systems. Results indicated that the proposed method can successfully be used for the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank or for the solar energy output of the system and the average quantity of the hot water per month for the two demand water temperatures considered [24].…”

Section: Neural Network Designmentioning

confidence: 99%

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

et al. 2009

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This study deals with artificial neural network (ANN) modeling a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of engine. To acquire data for training and testing the proposed ANN, two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results reveal that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model based on standard Back-Propagation algorithm for the engine was developed. Multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) were 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values by the model was obtained as 0.0004. Keywords

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Section: Neural Network Designmentioning

confidence: 99%

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

et al. 2009

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“…Estimation of NO x emissions of coal-fired [25], oil and methanefuelled [26] systems, development of neural-based software sensors for monitoring pollutant emissions [27] and emission modelling for comparison with continuous emission monitoring systems [28]. Performance prediction of specific equipment such as steam turbines [29], utility boilers [30], and condensers [31]. Assessment of ANN prediction performance for modelling of electric output of a coal-fired unit and comparison with other statistical approaches such as Autoregressive Integrated Moving Average and Multiple Linear Regression [32].…”

Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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“…Czarnigowski [7] used neural network model-based observer for idle speed control of ignition in SI engine. References [8][9][10][11][12][13][14][15] investigated the performance of various thermal systems with the aid of ANN. The ANN approach was used to predict the performance and exhaust emissions of internal combustion engines [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

Kiani

Ghobadian

Ommi

et al. 2012

Lecture Notes in Computer Science

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Abstract. This study deals with artificial neural network (ANN) modeling of a spark ignition engine to predict engine thermal balance. To acquire data for training and testing of ANN, a four-cylinder, four-stroke test engine was fuelled with ethanol-gasoline blended fuels with various percentages of ethanol and operated at different engine speeds and loads. The performance of the ANN was validated by comparing the prediction data set with the experimental results. Results showed that the ANN provided the best accuracy in modeling the thermal balance with correlation coefficient equal to 0.997, 0.998, 0.996 and 0.992 for useful work, heat lost through exhaust, heat lost to the cooling water and unaccounted losses respectively. The experimental results showed as the percentage of ethanol in the ethanol-gasoline blends is increased, the percentage of useful work is increased, while the heat lost to cooling water and exhaust are decreased compared to neat gasoline fuel operation.

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Power plant condenser performance forecasting using a non-fully connected artificial neural network

Cited by 41 publications

References 5 publications

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

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