scholarly journals Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Water ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Co2 Emissions ◽  
Artificial Neural ◽  
Carbon Dioxide Co2

Development of artificial neural networks based confidence intervals and response surfaces for the optimization of coagulation performance

2015 ◽  
Vol 15 (5) ◽  
pp. 1079-1087 ◽  
Author(s):  
Robert H. McArthur ◽  
Robert C. Andrews
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Confidence Intervals ◽  
Response Surfaces ◽  
Average Interval ◽  
Squared Error ◽  
Interval Width ◽  
Artificial Neural

Effective coagulation is essential to achieving drinking water treatment objectives when considering surface water. To minimize settled water turbidity, artificial neural networks (ANNs) have been adopted to predict optimum alum and carbon dioxide dosages at the Elgin Area Water Treatment Plant. ANNs were applied to predict both optimum carbon dioxide and alum dosages with correlation (R2) values of 0.68 and 0.90, respectively. ANNs were also used to developed surface response plots to ease optimum selection of dosage. Trained ANNs were used to predict turbidity outcomes for a range of alum and carbon dioxide dosages and these were compared to historical data. Point-wise confidence intervals were obtained based on error and squared error values during the training process. The probability of the true value falling within the predicted interval ranged from 0.25 to 0.81 and the average interval width ranged from 0.15 to 0.62 NTU. Training an ANN using the squared error produced a larger average interval width, but better probability of a true prediction interval.

scholarly journals Process Configuration Studies of Methanol Production via Carbon Dioxide Hydrogenation: Process Simulation-Based Optimization Using Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6608
Author(s):  
Prapatsorn Borisut ◽  
Aroonsri Nuchitprasittichai
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Production Cost ◽  
Operating Conditions ◽  
Price Sensitivity ◽  
Methanol Production ◽  
Artificial Neural

Methanol production via carbon dioxide (CO2) hydrogenation is a green chemical process, which can reduce CO2 emission. The operating conditions for minimum methanol production cost of three configurations were investigated in this work. An artificial neural network with Latin hypercube sampling technique was applied to construct model-represented methanol production. Price sensitivity was performed to study the impacts of the raw materials price on methanol production cost. Price sensitivity results showed that the hydrogen price has a large impact on the methanol production cost. In mathematical modeling using feedforward artificial neural networks, four different numbers of nodes were used to train artificial neural networks. The artificial neural network with eight numbers of nodes showed the most suitable configuration, which yielded the lowest percent error between the actual and predicted methanol production cost. The optimization results showed that the recommended process design among the three studied configurations was the process of methanol production with two reactors in series. The minimum methanol production cost obtained from this configuration was $888.85 per ton produced methanol, which was the lowest methanol production cost among all configurations.

Use of artificial neural networks for prediction of phase equilibria in the binary system containing carbon dioxide

2013 ◽  
Vol 75 ◽  
pp. 144-151 ◽  
Author(s):  
Mostafa Lashkarbolooki ◽  
Zeinab Sadat Shafipour ◽  
Ali Zeinolabedini Hezave ◽  
Hamid Farmani
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Binary System ◽  
Phase Equilibria ◽  
Artificial Neural
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