The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions

2020 ◽  
Author(s):  
Erdem Küçüktopcu ◽  
Bilal Cemek
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Dioxide Emissions ◽  
Energy Savings ◽  
Insulation Thickness ◽  
Artificial Neural ◽  
Optimum Insulation Thickness

AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS

2014 ◽  
Vol 13 (03) ◽  
pp. 1450014
Author(s):  
LAZIM ABDULLAH ◽  
HERRINI MOHD PAUZI
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Dioxide Emissions ◽  
Learning Algorithms ◽  
Impact Factors ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Lm Algorithm

This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide ( CO 2) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO 2 intensity were employed in developing all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the LM algorithm outperformed both BFGS and GD algorithms.

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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