چكيده به لاتين
Global warming due to greenhouse effect has been considered as a serious problem for many years around the world. Among the different gases which cause greenhouse gas effect, carbon dioxide is of great difficulty by entering into the surrounding atmosphere. So CO2 capturing and separation especially by adsorption is one of the most interesting approaches because of the low equipment cost, ease of operation, simplicity of design, and low energy consumption. In addition to time consuming and costly adsorption experiments in operational conditions, the inability of traditional computational methods to solve some engineering problems has led researchers to study soft computing methods in preparing a simple, accurate and broad model. For this purpose, artificial neural networks with regard to accuracy, high speed, ability to perform nonlinear computations, diagnosis and learning are highly regarded in prediction of carbon dioxide adsorption. Neural networks are similar to the nervous system of the human brain. Neural network is a mathematical model that aims to simulate the executive characteristics of biological neural networks. Artificial neural networks are used to predict complex and nonlinear relations. It also reduces the computation time by constructing linkages between input and output data. Optimization of the carbon dioxide (CO2) adsorption process by activated carbon adsorbents is investigated. Adsorption of CO2 by activated carbon solid dsorbents was optimized by use of ANNs (Artificial Neuron Networks). With the experimental data as training data using ANNs approach, the resulting model can provide acceptable results in an effect of independent variables and the interaction between them by the impact on the objective function, to optimize the process of CO2 capture by carbon - based adsorbent prepared. Data are divided into three groups: 1) training set (70 % of the experimental data points), 2) testing set (15% of the experimental data points) and 3) 15% of the experimental data points are used as the validation set. The MLP and RBF models used by ANN method were effective in optimizing. The results showed that the amount of R2 in the multi layer perceptron network for active carbon, modified activated carbon with NaOH and the modified activated carbon with KOH were 0.998, 0.995, 0.9879 respectively and the results showed that the amount of R2 in the RBF network for active carbon, modified activated carbon with NaOH and the modified activated carbon with KOH were 0.958, 0.966, 0.970 respectively. Therefore, the multi layer perceptron network compared with radial basis function has better conformity with experimental results. The models obtained from the ANN methods have an acceptable compliance with the experimental outcomes and due to the minimum error obtained from the simulation, the ANN is recommended for the development of adsorption simulation models.
