Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters
The disposal of sewage with acceptable qualitative characteristics to different acceptor resources is an environmental issue that today's societies face (with). Using the MatLab software, a neural network model, and an adaptive neuro-fuzzy inference system (ANFIS), this study has predicted the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
K. N. Toosi University of Technology
2021-05-01
|
| Series: | Numerical Methods in Civil Engineering |
| Subjects: | |
| Online Access: | https://nmce.kntu.ac.ir/article_160538_6cb2758d11fb1a17b1f12aa77b3cf883.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850123373435158528 |
|---|---|
| author | Nasser Mehrdadi Mehrdad Ghasemi |
| author_facet | Nasser Mehrdadi Mehrdad Ghasemi |
| author_sort | Nasser Mehrdadi |
| collection | DOAJ |
| description | The disposal of sewage with acceptable qualitative characteristics to different acceptor resources is an environmental issue that today's societies face (with). Using the MatLab software, a neural network model, and an adaptive neuro-fuzzy inference system (ANFIS), this study has predicted the qualitative parameters (COD, BOD5, and TSS of the wastewater, along with TS, VS, and SOUR of the sludge) for the south Tehran sewage treatment plant and finally chosen the best models by validating the model and using the defined criteria. Moreover, using these developed models and comparing their results with the available standard values provides a suitable classification to reuse the wastewater and sludge of the south Tehran wastewater treatment plant. The results indicated acceptable errors of both systems, the adaptive neuro-fuzzy inference system and the artificial neural network, in predicting the qualitative characteristics of the sludge and wastewater of the south Tehran sewage treatment plant and the priority of the adaptive neuro-fuzzy inference system over the artificial neural network in estimating the quality of the treated wastewater and sludge. |
| format | Article |
| id | doaj-art-7f227baf47a44535b9fb50163a2e2a03 |
| institution | OA Journals |
| issn | 2345-4296 2783-3941 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | K. N. Toosi University of Technology |
| record_format | Article |
| series | Numerical Methods in Civil Engineering |
| spelling | doaj-art-7f227baf47a44535b9fb50163a2e2a032025-08-20T02:34:36ZengK. N. Toosi University of TechnologyNumerical Methods in Civil Engineering2345-42962783-39412021-05-0161637610.52547/nmce.6.1.63160538Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge ParametersNasser Mehrdadi0Mehrdad Ghasemi1Professor, College of Engineering, Faculty of Environment, University of Tehran, Tehran, Iran.Ph.D. Candidate, College of Engineering, Faculty of Environment, University of Tehran, Tehran, Iran.The disposal of sewage with acceptable qualitative characteristics to different acceptor resources is an environmental issue that today's societies face (with). Using the MatLab software, a neural network model, and an adaptive neuro-fuzzy inference system (ANFIS), this study has predicted the qualitative parameters (COD, BOD5, and TSS of the wastewater, along with TS, VS, and SOUR of the sludge) for the south Tehran sewage treatment plant and finally chosen the best models by validating the model and using the defined criteria. Moreover, using these developed models and comparing their results with the available standard values provides a suitable classification to reuse the wastewater and sludge of the south Tehran wastewater treatment plant. The results indicated acceptable errors of both systems, the adaptive neuro-fuzzy inference system and the artificial neural network, in predicting the qualitative characteristics of the sludge and wastewater of the south Tehran sewage treatment plant and the priority of the adaptive neuro-fuzzy inference system over the artificial neural network in estimating the quality of the treated wastewater and sludge.https://nmce.kntu.ac.ir/article_160538_6cb2758d11fb1a17b1f12aa77b3cf883.pdfartificial neural networkadaptive neuro-fuzzy inference system (anfis)sewage treatment plantwastewatersludge |
| spellingShingle | Nasser Mehrdadi Mehrdad Ghasemi Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters Numerical Methods in Civil Engineering artificial neural network adaptive neuro-fuzzy inference system (anfis) sewage treatment plant wastewater sludge |
| title | Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters |
| title_full | Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters |
| title_fullStr | Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters |
| title_full_unstemmed | Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters |
| title_short | Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters |
| title_sort | modeling of tehran south water treatment plant using neural network and fuzzy logic considering effluent and sludge parameters |
| topic | artificial neural network adaptive neuro-fuzzy inference system (anfis) sewage treatment plant wastewater sludge |
| url | https://nmce.kntu.ac.ir/article_160538_6cb2758d11fb1a17b1f12aa77b3cf883.pdf |
| work_keys_str_mv | AT nassermehrdadi modelingoftehransouthwatertreatmentplantusingneuralnetworkandfuzzylogicconsideringeffluentandsludgeparameters AT mehrdadghasemi modelingoftehransouthwatertreatmentplantusingneuralnetworkandfuzzylogicconsideringeffluentandsludgeparameters |