scholarly journals ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2910
Author(s):  
Adhirath Mandal ◽  
Haengmuk Cho ◽  
Bhupendra Singh Chauhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Regression Coefficient ◽  
Experimental Results ◽  
Transport Sector ◽  
Dual Fuel ◽  
Alternate Fuel ◽  
Ci Engine ◽  
Artificial Neural

Compression ignition (CI) engines are popular in the transport sector because of their high compression ratio. However, in recent years, it has become a major concern from an environmental point of view because of the emission and depleting fossil fuel. The advanced combustion concept has been a popular research topic in the CI engine. Low-temperature combustion with alternate fuel has helped in reducing the oxides of nitrogen (NOx) and soot emission of the engine. Biogas is a popular substitute of energy especially deduced from biomass because of its clean combustion properties, as well it being a renewable energy source compared to non-renewable diesel resources. In experiments with dual fuel, i.e., conventional diesel and alternate fuel (biogas) were carried out through them. In the present study, an artificial neural network model was used to estimate emissions and check the attributes of performance. Different algorithms and training functions were used to train the models. However, the best training algorithm was Levenberge Marquardt and the training function was Tansig (Hyperbolic tangent sigmoid) and Logsig (logarithmic sigmoid), which showed the best result with regression coefficient (R > 0.98) and Mean square error (MSE < 0.001). The best model was trained by evaluating MSE and regression coefficient. Experimental results and artificial neural network (ANN) prediction showed that the experimental results were similar to each other and lie at the same intervals. The ANN model helped in predicting experimental data that were earlier difficult to experimentally perform using interpolation and extrapolations. It was observed that there was an increase in Brake Specific Energy Consumption (BSEC) and a decrease in Brake thermal efficiency (BTE) with improved biogas flow rate and reduced NOx emission in the combustion chamber. Carbon monoxide (CO) and hydrocarbon (HC) emissions increase linearly with the increase in biogas flow rate, whereas smoke opacity decreases. It could be concluded that this study helps in understanding the effect of dual fuel (diesel-biogas) combustion under different load conditions of the engine with the help of ANN, which could be a substitute fuel and help to protect the environment.

scholarly journals Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3294
Author(s):  
Carla Delmarre ◽  
Marie-Anne Resmond ◽  
Frédéric Kuznik ◽  
Christian Obrecht ◽  
Bao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Thermal Energy ◽  
Temperature Difference ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Experimental Results ◽  
Physical Models ◽  
Artificial Neural

Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

2013 ◽  
Vol 13 (2) ◽  
pp. 1085-1098 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Mohammad Ebadi ◽  
Amin Shokrollahi ◽  
Seyed Mohammad Javad Majidi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm ◽  
Oil Flow ◽  
Artificial Neural

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Abhishek Paul ◽  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Durbadal Debroy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Natural Gas ◽  
Goodness Of Fit ◽  
Specific Energy Consumption ◽  
Dual Fuel ◽  
Mean Square ◽  
Compressed Natural Gas ◽  
Artificial Neural

The present study surveys the effects on performance and emission parameters of a partially modified single cylinder direct injection (DI) diesel engine fueled with diesohol blends under varying compressed natural gas (CNG) flowrates in dual fuel mode. Based on experimental data, an artificial intelligence (AI) specialized artificial neural network (ANN) model have been developed for predicting the output parameters, viz. brake thermal efficiency (Bth), brake-specific energy consumption (BSEC) along with emission characteristics such as oxides of nitrogen (NOx), unburned hydrocarbon (UBHC), carbon dioxide (CO2), and carbon monoxide (CO) emissions. Engine load, Ethanol share, and CNG strategies have been used as input parameters for the model. Among the tested models, the Levenberg–Marquardt feed-forward back propagation with three input neurons or nodes, two hidden layers with ten neurons in each layer and six output neurons, and tansig-purelin activation function have been found to the optimal model topology for the diesohol–CNG platforms. The statistical results acquired from the optimal network topology such as correlation coefficient (0.992–0.999), mean square error (MSE) (0.0001–0.0009), and mean absolute percentage error (MAPE) (0.09–2.41%) along with Nash–Sutcliffe coefficient of efficiency (NSE), Kling–Gupta efficiency (KGE), mean square relative error, and model uncertainty established itself as a real-time robust type machine learning tool under diesohol–CNG paradigms. The study also incorporated a special type of measure, namely Pearson's Chi-square test or goodness of fit, which brings up the model validation to a higher level.

scholarly journals Artificial Neural Network (ANN) Modeling for Prediction of Pesticide Wastewater Degradation by FeGAC/H2O2 Process

2018 ◽  
Vol 65 ◽  
pp. 05004
Author(s):  
Augustine Chioma Affam ◽  
Malay Chaudhuri ◽  
Chee Chung Wong ◽  
Chee Swee Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Close Agreement ◽  
Transfer Functions ◽  
Cod Removal ◽  
Experimental Results ◽  
Ann Model ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Mahnaz Yasemi ◽  
Masoud Rahimi ◽  
Amir Heydarinasab ◽  
Mehdi Ardjmand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Maximum Yield ◽  
Extraction Yield ◽  
Experimental Results ◽  
Flow Rate Ratio ◽  
Acid Extraction ◽  
Ann Model ◽  
Design Expert ◽  
Artificial Neural

Abstract: The current study presents the outcomes of modeling and optimizing extraction of gallotannic acid from Quercus leaves using a microfluidic system. In this study, the effects of various experimental parameters were investigated using the method of design expert. Number of experiments suggested is 31 by central composite design of Design Expert. The experimental results of design expert were analyzed by artificial neural network (ANN). Based on the results of ANN, independent variables experiment: temperature (T), flow rate ratio (FR) and pH have shown a negative effect on extraction yield (dependent variable), while the residence time (RT) has shown a positive effect. In trained network, ${R^2} = 0.9805$ and RMSE = 0.0166 shows good agreement between the predicted values of ANN and experimental results. Optimum extraction conditions, to reach maximum yield by genetic algorithms (GA), were FR = 0.53, RT = 26.4, pH = 2.06 and T = 21.44 ${R^2} = 0.9805$ . The extraction yield under the optimum predicated conditions was 96.4 %, which was well matched with the experimental value 95.01 % $\pm 0.63$ . Based on the obtained results, it was found that the ANN model could be employed successfully in estimating the gallotannic acid extraction efficiency using microfluidic extraction method.

scholarly journals Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Pradyut Kundu ◽  
Anupam Debsarkar ◽  
Somnath Mukherjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Slaughterhouse Wastewater ◽  
Ann Models ◽  
Artificial Neural

The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.

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