Modeling Chemical Engineering Processes Using Artificial Neural Networks
| dc.contributor.advisor | Assefa, Berhanu (PhD) | |
| dc.contributor.advisor | Tefera, Nurelegne (PhD) | |
| dc.contributor.author | Ambaw, Alemayehu | |
| dc.date.accessioned | 2018-07-11T12:27:42Z | |
| dc.date.accessioned | 2023-11-10T14:54:58Z | |
| dc.date.available | 2018-07-11T12:27:42Z | |
| dc.date.available | 2023-11-10T14:54:58Z | |
| dc.date.issued | 2005-01 | |
| dc.description.abstract | In this thesis the application of feed forward type artificial neural networks to model chemical engineering processes are demonstrated with reference to five different problems. Neural network models are constructed and employed to predict vapor-liquid equilibrium (VLE) data of twelve different binary systems having different chemical structures and solution types (azeotrope-nonazeotrope) in various conditions (isothermal or isobaric). It is observed that the data found by neural network model gives an excellent agreement with the experimental data. In fact the neural network model can be treated as a powerful means for VLE data prediction in a fast and reliable way. This study has confirmed the feasibility of using a neural network to capture the nonlinear and interacting relationships between the moisture content and different drying conditions of potato. Simulating time series temperature profiles of adiabatic batch reactor has also investigated. Neural network trained with a limited number of experimental data were capable of predicting fresh data that were not used to train the network. The results obtained in using the developed models are physically sound as expected from experience. Simulating a human operator controlling a chemical plant is also a good instance where the advantage of using artificial neural networks is demonstrated in the thesis. This thesis also describes the use of multilayer feed forward neural networks as a CO2 analyzer. It was proved that MLP-type network of a relatively simple structure made it possible to predict the CO2 effluent from a furnace. Taking in to account the difficulties in experimental conditions, complicated measurements and unavoidable errors of devices used, limit the precision of laboratory measurement results. The accuracy of the results generated by the developed neural network models may be considered satisfactory for engineering calculations. | en_US |
| dc.description.sponsorship | Addis Ababa University | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/8075 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Chemical Engineering | en_US |
| dc.title | Modeling Chemical Engineering Processes Using Artificial Neural Networks | en_US |
| dc.type | Thesis | en_US |