Neuro-Fuzzy Controller forMethanol Recovery Distillation Column
Distillation columns are widely used in chemical processes as separation systems in industries. In order to gain better product quality and lower the energy consumption of the distillation column, an effective control system is needed to allow the process to be operated over larger operating ranges....
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Unviversity of Technology- Iraq
2013-06-01
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| coleção: | Engineering and Technology Journal |
| Assuntos: | |
| Acesso em linha: | https://etj.uotechnology.edu.iq/article_82073_6df77898e7bdf2bae67098860305db75.pdf |
| _version_ | 1827358511144632320 |
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| author | Safa A. Al-Naimi Ghydaa M. Jaid |
| author_facet | Safa A. Al-Naimi Ghydaa M. Jaid |
| author_sort | Safa A. Al-Naimi |
| collection | DOAJ |
| description | Distillation columns are widely used in chemical processes as separation systems in industries. In order to gain better product quality and lower the energy consumption of the distillation column, an effective control system is needed to allow the process to be operated over larger operating ranges. In this study Different control strategies were used to control the distillate and bottom compositions of a packed distillation column to separate the mixture of methanol (CH3OH) and water (H2O). The tuning of control parameters were determined for PI and PID controllers using three different methods; Internal Model Control (IMC), Ziegler-Nichols (Z.N), and Cohen-Coon (PRC) to find the best values of proportional gain (KC), integral time (τI) and derivative time (τD). The Internal Model Control (IMC) method gave better results than that of the other two methods thus it was recommended to be the tuning method in this work. The low values of ITAE of 61.3 for distillate product composition and 54 for bottom composition were obtained which represent the adaptive neuro-fuzzy inference system (ANFIS) method and assure the feasibility of this method as a control strategy among other methods (conventional feedback controllers (PI, PID), artificial neural network (ANN) , adaptive fuzzy logic and PID fuzzy logic controllers). |
| first_indexed | 2024-03-08T06:11:51Z |
| format | Article |
| id | doaj.art-d0b7eb1e84ab4870b0fb7d8b51b5f79d |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:11:51Z |
| publishDate | 2013-06-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-d0b7eb1e84ab4870b0fb7d8b51b5f79d2024-02-04T17:35:42ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582013-06-0131A 61026104410.30684/etj.31.6A282073Neuro-Fuzzy Controller forMethanol Recovery Distillation ColumnSafa A. Al-NaimiGhydaa M. JaidDistillation columns are widely used in chemical processes as separation systems in industries. In order to gain better product quality and lower the energy consumption of the distillation column, an effective control system is needed to allow the process to be operated over larger operating ranges. In this study Different control strategies were used to control the distillate and bottom compositions of a packed distillation column to separate the mixture of methanol (CH3OH) and water (H2O). The tuning of control parameters were determined for PI and PID controllers using three different methods; Internal Model Control (IMC), Ziegler-Nichols (Z.N), and Cohen-Coon (PRC) to find the best values of proportional gain (KC), integral time (τI) and derivative time (τD). The Internal Model Control (IMC) method gave better results than that of the other two methods thus it was recommended to be the tuning method in this work. The low values of ITAE of 61.3 for distillate product composition and 54 for bottom composition were obtained which represent the adaptive neuro-fuzzy inference system (ANFIS) method and assure the feasibility of this method as a control strategy among other methods (conventional feedback controllers (PI, PID), artificial neural network (ANN) , adaptive fuzzy logic and PID fuzzy logic controllers).https://etj.uotechnology.edu.iq/article_82073_6df77898e7bdf2bae67098860305db75.pdfdistillationconventional feedbackartificial neural networkadaptive fuzzy logic and pid fuzzy logic |
| spellingShingle | Safa A. Al-Naimi Ghydaa M. Jaid Neuro-Fuzzy Controller forMethanol Recovery Distillation Column Engineering and Technology Journal distillation conventional feedback artificial neural network adaptive fuzzy logic and pid fuzzy logic |
| title | Neuro-Fuzzy Controller forMethanol Recovery Distillation Column |
| title_full | Neuro-Fuzzy Controller forMethanol Recovery Distillation Column |
| title_fullStr | Neuro-Fuzzy Controller forMethanol Recovery Distillation Column |
| title_full_unstemmed | Neuro-Fuzzy Controller forMethanol Recovery Distillation Column |
| title_short | Neuro-Fuzzy Controller forMethanol Recovery Distillation Column |
| title_sort | neuro fuzzy controller formethanol recovery distillation column |
| topic | distillation conventional feedback artificial neural network adaptive fuzzy logic and pid fuzzy logic |
| url | https://etj.uotechnology.edu.iq/article_82073_6df77898e7bdf2bae67098860305db75.pdf |
| work_keys_str_mv | AT safaaalnaimi neurofuzzycontrollerformethanolrecoverydistillationcolumn AT ghydaamjaid neurofuzzycontrollerformethanolrecoverydistillationcolumn |