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231225s2018 xx |||||o 00| ||eng c |
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|a 10.2166/wst.2018.021
|2 doi
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|a pubmed24n0939.xml
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|a (DE-627)NLM281847959
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|a (NLM)29528303
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Rittmann, Bruce E
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A framework for good biofilm reactor modeling practice (GBRMP)
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| 264 |
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|c 2018
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 23.07.2018
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| 500 |
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|a Date Revised 07.12.2022
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|a published: Print
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|a Citation Status MEDLINE
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|a A researcher or practitioner can employ a biofilm model to gain insight into what controls the performance of a biofilm process and for optimizing its performance. While a wide range of biofilm-modeling platforms is available, a good strategy is to choose the simplest model that includes sufficient components and processes to address the modeling goal. In most cases, a one-dimensional biofilm model provides the best balance, and good choices can range from hand-calculation analytical solutions, simple spreadsheets, and numerical-method platforms. What is missing today is clear guidance on how to apply a biofilm model to obtain accurate and meaningful results. Here, we present a five-step framework for good biofilm reactor modeling practice (GBRMP). The first four steps are (1) obtain information on the biofilm reactor system, (2) characterize the influent, (3) choose the plant and biofilm model, and (4) define the conversion processes. Each step demands that the model user understands the important components and processes in the system, one of the main benefits of doing biofilm modeling. The fifth step is to calibrate and validate the model: System-specific model parameters are adjusted within reasonable ranges so that model outputs match actual system performance. Calibration is not a simple 'by the numbers' process, and it requires that the modeler follows a logical hierarchy of steps. Calibration requires that the adjusted parameters remain within realistic ranges and that the calibration process be carried out in an iterative manner. Once each of steps 1 through 5 is completed satisfactorily, the calibrated model can be used for its intended purpose, such as optimizing performance, trouble-shooting poor performance, or gaining deeper understanding of what controls process performance
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| 650 |
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|a Journal Article
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| 650 |
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7 |
|a Waste Water
|2 NLM
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| 700 |
1 |
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|a Boltz, Joshua P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Brockmann, Doris
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Daigger, Glen T
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Morgenroth, Eberhard
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sørensen, Kim Helleshøj
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Takács, Imre
|e verfasserin
|4 aut
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| 700 |
1 |
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|a van Loosdrecht, Mark
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Vanrolleghem, Peter A
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 77(2018), 5-6 vom: 25. März, Seite 1149-1164
|w (DE-627)NLM098149431
|x 0273-1223
|7 nnns
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| 773 |
1 |
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|g volume:77
|g year:2018
|g number:5-6
|g day:25
|g month:03
|g pages:1149-1164
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|u http://dx.doi.org/10.2166/wst.2018.021
|3 Volltext
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