Schwarz, Maximilian (2023)
Measurement and Prediction of Oxygen Transfer in Activated Sludge based on Ex Situ Off-gas Monitoring.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00023287
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
This dissertation examines oxygen transfer dynamics of activated sludge aeration systems in wastewater treatment plants (WWTP). The method of ex situ off-gas testing and its measurement uncertainty when determining the α-factor were studied. The variation of the α-factor was measured in conventional activated sludge (CAS) and two-stage systems with pilot-scale long-term ex situ off-gas testing. A data-driven approach to predict oxygen transfer based on supervised machine learning is presented. The key results of this cumulative dissertation and its three papers (P1-P3) are as follows:
- ASCE 18-18 describes the ex situ off-gas method as an alternative to in situ off-gas testing with off-gas hoods on the activated sludge surface. P1 showed that results from ex situ and in situ tests cannot be compared because sludge inflow into an ex situ bubble column systematically increased the α-factor. Still, ex situ off-gas testing offers unique advantages for piloting and research of oxygen transfer because operation of an external bubble column is more flexible than in situ off-gas testing.
- By comparing ex situ off-gas measurements under the same conditions, P1 demonstrated that α-factors can be quantified at a relative standard deviation of about ± 2.8 %. This is significantly more accurate than previously reported uncertainties between ± 5 to 15 %. A sensitivity analysis in P1 revealed that recording the oxygen concentration in the off-gas was the most important parameter to conduct reliable oxygen transfer tests, exceeding the relevance of dissolved oxygen (DO) and airflow rate measurement by far.
- α-factors are generally higher in the second stage of a two-stage WWTP because oxygen transfer inhibiting substances, e.g., surfactants and TOC, are partially removed in the first stage. In P2, α-factors for design load cases were determined as 0.45 for αmean and 0.33/0.54 for αmin/αmax in the first stage (HRAS), and as 0.80 for αmean and 0.69/0.91 for αmin/αmax in the second stage. α-factors in situ would be lower because these values were recorded with ex situ off-gas tests.
- The α0-factor was introduced in P3 to compare oxygen transfer in activated sludge from aerated and non-aerated zones. It considers differences of in situ and ex situ DO under non-steady state DO conditions. An increase of the α0-factor along an upstream anoxic tank of a CAS process was observed, thus suggesting biosorption and/or biodegradation of oxygen transfer inhibiting substances.
- The α0-factor was predicted by Random Forest models for different activated sludge stages within an RMSE (root-mean-square error) of 0.024 and 0.033 (R2 between 0.84 and 0.92). Models were trained with 17 predictor variables based on WWTP operating data. The data-driven approach can consider potential interactions of influences on oxygen transfer, but the final models are typically unable to generalize for conditions not included in training data.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2023 | ||||
Autor(en): | Schwarz, Maximilian | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Measurement and Prediction of Oxygen Transfer in Activated Sludge based on Ex Situ Off-gas Monitoring | ||||
Sprache: | Englisch | ||||
Referenten: | Wagner, Prof. Dr. Martin ; Engelhart, Prof. Dr. Markus ; Jardin, Prof. Dr. Norbert | ||||
Publikationsjahr: | 2023 | ||||
Ort: | Darmstadt | ||||
Kollation: | XII, 138 Seiten | ||||
Datum der mündlichen Prüfung: | 9 Februar 2023 | ||||
DOI: | 10.26083/tuprints-00023287 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23287 | ||||
Kurzbeschreibung (Abstract): | This dissertation examines oxygen transfer dynamics of activated sludge aeration systems in wastewater treatment plants (WWTP). The method of ex situ off-gas testing and its measurement uncertainty when determining the α-factor were studied. The variation of the α-factor was measured in conventional activated sludge (CAS) and two-stage systems with pilot-scale long-term ex situ off-gas testing. A data-driven approach to predict oxygen transfer based on supervised machine learning is presented. The key results of this cumulative dissertation and its three papers (P1-P3) are as follows: - ASCE 18-18 describes the ex situ off-gas method as an alternative to in situ off-gas testing with off-gas hoods on the activated sludge surface. P1 showed that results from ex situ and in situ tests cannot be compared because sludge inflow into an ex situ bubble column systematically increased the α-factor. Still, ex situ off-gas testing offers unique advantages for piloting and research of oxygen transfer because operation of an external bubble column is more flexible than in situ off-gas testing. - By comparing ex situ off-gas measurements under the same conditions, P1 demonstrated that α-factors can be quantified at a relative standard deviation of about ± 2.8 %. This is significantly more accurate than previously reported uncertainties between ± 5 to 15 %. A sensitivity analysis in P1 revealed that recording the oxygen concentration in the off-gas was the most important parameter to conduct reliable oxygen transfer tests, exceeding the relevance of dissolved oxygen (DO) and airflow rate measurement by far. - α-factors are generally higher in the second stage of a two-stage WWTP because oxygen transfer inhibiting substances, e.g., surfactants and TOC, are partially removed in the first stage. In P2, α-factors for design load cases were determined as 0.45 for αmean and 0.33/0.54 for αmin/αmax in the first stage (HRAS), and as 0.80 for αmean and 0.69/0.91 for αmin/αmax in the second stage. α-factors in situ would be lower because these values were recorded with ex situ off-gas tests. - The α0-factor was introduced in P3 to compare oxygen transfer in activated sludge from aerated and non-aerated zones. It considers differences of in situ and ex situ DO under non-steady state DO conditions. An increase of the α0-factor along an upstream anoxic tank of a CAS process was observed, thus suggesting biosorption and/or biodegradation of oxygen transfer inhibiting substances. - The α0-factor was predicted by Random Forest models for different activated sludge stages within an RMSE (root-mean-square error) of 0.024 and 0.033 (R2 between 0.84 and 0.92). Models were trained with 17 predictor variables based on WWTP operating data. The data-driven approach can consider potential interactions of influences on oxygen transfer, but the final models are typically unable to generalize for conditions not included in training data. |
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Status: | Verlagsversion | ||||
URN: | urn:nbn:de:tuda-tuprints-232879 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau | ||||
Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut IWAR - Wasser- und Abfalltechnik, Umwelt- und Raumplanung 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut IWAR - Wasser- und Abfalltechnik, Umwelt- und Raumplanung > Fachgebiet Abwassertechnik |
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TU-Projekte: | Bund/BMBF|02WA1461|WOBeS | ||||
Hinterlegungsdatum: | 01 Mär 2023 09:53 | ||||
Letzte Änderung: | 02 Mär 2023 06:57 | ||||
PPN: | |||||
Referenten: | Wagner, Prof. Dr. Martin ; Engelhart, Prof. Dr. Markus ; Jardin, Prof. Dr. Norbert | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 9 Februar 2023 | ||||
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