Modeling and optimization III: Reaction rate estimation using artificial neural network (ANN) without a kinetic model

2007 ◽  
Vol 79 (2) ◽  
pp. 622-628 ◽  
Author(s):  
Deniz Baş ◽  
Fahriye Ceyda Dudak ◽  
İsmail Hakkı Boyacı
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinetic Model ◽  
Reaction Rate ◽  
Modeling And Optimization ◽  
Rate Estimation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Modeling of Chlorine Decay using Artificial Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 174-176
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reaction Rate ◽  
Ann Model ◽  
Nitrogenous Compounds ◽  
Reaction Rate Constants ◽  
Single Output ◽  
Artificial Neural ◽  
Chlorine Decay ◽  
Artificial Neural Network Ann

In this paper an attempt is made to model chlorine decay using Artificial Neural Network (ANN). Initial chlorine concentration, fast and slow reacting organic and nitrogenous compounds and reaction rate constants of the compounds are used as inputs to the ANN model and the chlorine decay at different points in the decay curve are evaluated. ANN is trained by two different methods namely single output model and multi output models. Predicted data are compared with observed using correlation coefficient. Result indicates multi output model able to model more accurately than single output model.

Artificial Neural Network for Combined Steam-Carbon Dioxide Reforming of Methane

2020 ◽  
Vol 20 (9) ◽  
pp. 5730-5733 ◽  
Author(s):  
Sharon Jo ◽  
Byung Chol Ma ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reaction Temperature ◽  
Reaction Rate ◽  
Complex Reaction ◽  
Direct Influence ◽  
Independent Variables ◽  
Dependent Variables ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The CH4 conversion, CO2 conversion, and H2/CO ratio were set as dependent variables, as the feed rate, flow rate and reaction temperature as independent variables in the complex reaction of methane. We used the Artificial Neural Network (ANN) technique to build a model of the process. The ANN technique was able to predict the reforming process with higher accuracy due to the training capability. The reaction temperature has the greatest effect on the CO2–CH4 reforming reaction. This is because the catalytic reaction temperature has a direct influence on the thermodynamic value and the reaction rate and the equilibrium state.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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