Eintrag weiter verarbeiten
Intelligent system for improving dosage control
Gespeichert in:
| Zeitschriftentitel: | Acta Scientiarum. Technology |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Acta Scientiarum. Technology, 39, 2017, 1, S. 33 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Universidade Estadual de Maringa
|
| Schlagwörter: |
| author_facet |
Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo |
|---|---|
| author |
Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo |
| spellingShingle |
Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo Acta Scientiarum. Technology Intelligent system for improving dosage control General Earth and Planetary Sciences General Physics and Astronomy General Engineering General Mathematics General Chemistry General Computer Science |
| author_sort |
santos, fabio cosme rodrigues dos |
| spelling |
Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo 1807-8664 1806-2563 Universidade Estadual de Maringa General Earth and Planetary Sciences General Physics and Astronomy General Engineering General Mathematics General Chemistry General Computer Science http://dx.doi.org/10.4025/actascitechnol.v39i1.29353 <jats:p>Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. </jats:p> <b>Intelligent system for improving dosage control Acta Scientiarum. Technology |
| doi_str_mv |
10.4025/actascitechnol.v39i1.29353 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNDAyNS9hY3Rhc2NpdGVjaG5vbC52MzlpMS4yOTM1Mw |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNDAyNS9hY3Rhc2NpdGVjaG5vbC52MzlpMS4yOTM1Mw |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
Universidade Estadual de Maringa, 2017 |
| imprint_str_mv |
Universidade Estadual de Maringa, 2017 |
| issn |
1806-2563 1807-8664 |
| issn_str_mv |
1806-2563 1807-8664 |
| language |
Undetermined |
| mega_collection |
Universidade Estadual de Maringa (CrossRef) |
| match_str |
santos2017intelligentsystemforimprovingdosagecontrol |
| publishDateSort |
2017 |
| publisher |
Universidade Estadual de Maringa |
| recordtype |
ai |
| record_format |
ai |
| series |
Acta Scientiarum. Technology |
| source_id |
49 |
| title |
Intelligent system for improving dosage control |
| title_unstemmed |
Intelligent system for improving dosage control |
| title_full |
Intelligent system for improving dosage control |
| title_fullStr |
Intelligent system for improving dosage control |
| title_full_unstemmed |
Intelligent system for improving dosage control |
| title_short |
Intelligent system for improving dosage control |
| title_sort |
<b>intelligent system for improving dosage control |
| topic |
General Earth and Planetary Sciences General Physics and Astronomy General Engineering General Mathematics General Chemistry General Computer Science |
| url |
http://dx.doi.org/10.4025/actascitechnol.v39i1.29353 |
| publishDate |
2017 |
| physical |
33 |
| description |
<jats:p>Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. </jats:p> |
| container_issue |
1 |
| container_start_page |
0 |
| container_title |
Acta Scientiarum. Technology |
| container_volume |
39 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792339941324226567 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T15:56:02.039Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Intelligent+system+for+improving+dosage+control&rft.date=2017-02-24&genre=article&issn=1806-2563&volume=39&issue=1&pages=33&jtitle=Acta+Scientiarum.+Technology&atitle=%3Cb%3EIntelligent+system+for+improving+dosage+control&aulast=Rodrigues&aufirst=Sheila+Gozzo&rft_id=info%3Adoi%2F10.4025%2Factascitechnol.v39i1.29353&rft.language%5B0%5D=und |
| SOLR | |
| _version_ | 1792339941324226567 |
| author | Santos, Fabio Cosme Rodrigues dos, Librantz, André Felipe Henriques, Dias, Cleber Gustavo, Rodrigues, Sheila Gozzo |
| author_facet | Santos, Fabio Cosme Rodrigues dos, Librantz, André Felipe Henriques, Dias, Cleber Gustavo, Rodrigues, Sheila Gozzo, Santos, Fabio Cosme Rodrigues dos, Librantz, André Felipe Henriques, Dias, Cleber Gustavo, Rodrigues, Sheila Gozzo |
| author_sort | santos, fabio cosme rodrigues dos |
| container_issue | 1 |
| container_start_page | 0 |
| container_title | Acta Scientiarum. Technology |
| container_volume | 39 |
| description | <jats:p>Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. </jats:p> |
| doi_str_mv | 10.4025/actascitechnol.v39i1.29353 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNDAyNS9hY3Rhc2NpdGVjaG5vbC52MzlpMS4yOTM1Mw |
| imprint | Universidade Estadual de Maringa, 2017 |
| imprint_str_mv | Universidade Estadual de Maringa, 2017 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 1806-2563, 1807-8664 |
| issn_str_mv | 1806-2563, 1807-8664 |
| language | Undetermined |
| last_indexed | 2024-03-01T15:56:02.039Z |
| match_str | santos2017intelligentsystemforimprovingdosagecontrol |
| mega_collection | Universidade Estadual de Maringa (CrossRef) |
| physical | 33 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | Universidade Estadual de Maringa |
| record_format | ai |
| recordtype | ai |
| series | Acta Scientiarum. Technology |
| source_id | 49 |
| spelling | Santos, Fabio Cosme Rodrigues dos Librantz, André Felipe Henriques Dias, Cleber Gustavo Rodrigues, Sheila Gozzo 1807-8664 1806-2563 Universidade Estadual de Maringa General Earth and Planetary Sciences General Physics and Astronomy General Engineering General Mathematics General Chemistry General Computer Science http://dx.doi.org/10.4025/actascitechnol.v39i1.29353 <jats:p>Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. </jats:p> <b>Intelligent system for improving dosage control Acta Scientiarum. Technology |
| spellingShingle | Santos, Fabio Cosme Rodrigues dos, Librantz, André Felipe Henriques, Dias, Cleber Gustavo, Rodrigues, Sheila Gozzo, Acta Scientiarum. Technology, Intelligent system for improving dosage control, General Earth and Planetary Sciences, General Physics and Astronomy, General Engineering, General Mathematics, General Chemistry, General Computer Science |
| title | Intelligent system for improving dosage control |
| title_full | Intelligent system for improving dosage control |
| title_fullStr | Intelligent system for improving dosage control |
| title_full_unstemmed | Intelligent system for improving dosage control |
| title_short | Intelligent system for improving dosage control |
| title_sort | <b>intelligent system for improving dosage control |
| title_unstemmed | Intelligent system for improving dosage control |
| topic | General Earth and Planetary Sciences, General Physics and Astronomy, General Engineering, General Mathematics, General Chemistry, General Computer Science |
| url | http://dx.doi.org/10.4025/actascitechnol.v39i1.29353 |