Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter
This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assess...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mohanty, S. - BTech, MTech, PhD [verfasserIn] Scholz, M. - BEng, MSc, PhD Graduate Member [verfasserIn] Slater, M. J. - BSc, DIC, PhD [verfasserIn] |
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| Format: |
E-Artikel |
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| Erschienen: |
Oxford, UK: Blackwell Publishing Ltd ; 2002 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
2007 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Water and environment journal - Oxford [u.a.] : Wiley-Blackwell, 1987, 16(2002), 1, Seite 0 |
| Übergeordnetes Werk: |
volume:16 ; year:2002 ; number:1 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1111/j.1747-6593.2002.tb00369.x |
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NLEJ243629079 |
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| 520 | |a This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. | ||
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10.1111/j.1747-6593.2002.tb00369.x doi (DE-627)NLEJ243629079 DE-627 ger DE-627 rakwb Mohanty, S. BTech, MTech, PhD verfasserin aut Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter Oxford, UK Blackwell Publishing Ltd 2002 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| Biological activated carbon Scholz, M. BEng, MSc, PhD Graduate Member verfasserin aut Slater, M. J. BSc, DIC, PhD verfasserin aut In Water and environment journal Oxford [u.a.] : Wiley-Blackwell, 1987 16(2002), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926480 (DE-600)2218235-4 1747-6593 nnns volume:16 year:2002 number:1 pages:0 http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 16 2002 1 0 |
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10.1111/j.1747-6593.2002.tb00369.x doi (DE-627)NLEJ243629079 DE-627 ger DE-627 rakwb Mohanty, S. BTech, MTech, PhD verfasserin aut Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter Oxford, UK Blackwell Publishing Ltd 2002 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| Biological activated carbon Scholz, M. BEng, MSc, PhD Graduate Member verfasserin aut Slater, M. J. BSc, DIC, PhD verfasserin aut In Water and environment journal Oxford [u.a.] : Wiley-Blackwell, 1987 16(2002), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926480 (DE-600)2218235-4 1747-6593 nnns volume:16 year:2002 number:1 pages:0 http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 16 2002 1 0 |
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10.1111/j.1747-6593.2002.tb00369.x doi (DE-627)NLEJ243629079 DE-627 ger DE-627 rakwb Mohanty, S. BTech, MTech, PhD verfasserin aut Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter Oxford, UK Blackwell Publishing Ltd 2002 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| Biological activated carbon Scholz, M. BEng, MSc, PhD Graduate Member verfasserin aut Slater, M. J. BSc, DIC, PhD verfasserin aut In Water and environment journal Oxford [u.a.] : Wiley-Blackwell, 1987 16(2002), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926480 (DE-600)2218235-4 1747-6593 nnns volume:16 year:2002 number:1 pages:0 http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 16 2002 1 0 |
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10.1111/j.1747-6593.2002.tb00369.x doi (DE-627)NLEJ243629079 DE-627 ger DE-627 rakwb Mohanty, S. BTech, MTech, PhD verfasserin aut Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter Oxford, UK Blackwell Publishing Ltd 2002 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| Biological activated carbon Scholz, M. BEng, MSc, PhD Graduate Member verfasserin aut Slater, M. J. BSc, DIC, PhD verfasserin aut In Water and environment journal Oxford [u.a.] : Wiley-Blackwell, 1987 16(2002), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926480 (DE-600)2218235-4 1747-6593 nnns volume:16 year:2002 number:1 pages:0 http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 16 2002 1 0 |
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10.1111/j.1747-6593.2002.tb00369.x doi (DE-627)NLEJ243629079 DE-627 ger DE-627 rakwb Mohanty, S. BTech, MTech, PhD verfasserin aut Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter Oxford, UK Blackwell Publishing Ltd 2002 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| Biological activated carbon Scholz, M. BEng, MSc, PhD Graduate Member verfasserin aut Slater, M. J. BSc, DIC, PhD verfasserin aut In Water and environment journal Oxford [u.a.] : Wiley-Blackwell, 1987 16(2002), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926480 (DE-600)2218235-4 1747-6593 nnns volume:16 year:2002 number:1 pages:0 http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 16 2002 1 0 |
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Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter |
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This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. |
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This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. |
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This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ243629079</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707183635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s2002 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/j.1747-6593.2002.tb00369.x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ243629079</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mohanty, S.</subfield><subfield code="c">BTech, MTech, PhD</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural Network Simulation of the Chemical Oxygen Demand Reduction in a Biological Activated-Carbon Filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford, UK</subfield><subfield code="b">Blackwell Publishing Ltd</subfield><subfield code="c">2002</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper is primarily aimed at encouraging further use of neural networks by the water- and wastewater treatment industry. The study demonstrates the principle of using a network method of simulating the performance of a biological activated-carbon filter based on a biological water-quality assessment and measurements of pH and dissolved oxygen during the bio-regeneration mode with untreated river water. Protozoa, worms, rotifers, bacteria, fungi and algae were used as biological parameters. The neural network model could reasonably estimate the chemical oxygen demand reduction in an exhausted filter. The neural network model gave much better results than a second-order polynomial regression model; however, a much larger database is required than is currently available.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2007</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2007||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biological activated carbon</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scholz, M.</subfield><subfield code="c">BEng, MSc, PhD Graduate Member</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Slater, M. J.</subfield><subfield code="c">BSc, DIC, PhD</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Water and environment journal</subfield><subfield code="d">Oxford [u.a.] : Wiley-Blackwell, 1987</subfield><subfield code="g">16(2002), 1, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243926480</subfield><subfield code="w">(DE-600)2218235-4</subfield><subfield code="x">1747-6593</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2002</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/j.1747-6593.2002.tb00369.x</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2002</subfield><subfield code="e">1</subfield><subfield code="h">0</subfield></datafield></record></collection>
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7.40096 |