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Preparation of solids balances for municipal wastewater treatment facilities

2015· dissertation· tr· W7033317953 sur OpenAlex

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Notice bibliographique

RevueDSpace Repository · 2015
Typedissertation
Languetr
DomaineEnvironmental Science
ThématiqueFish biology, ecology, and behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRaw material
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

K?tle dengesi; reakt?r tasar?m?, proses de?erlendirilmesi gibi pek ?ok m?hendislik uygulamalar?nda kullan?lan ve iyi bilinen bir y?ntemdir. Bu metot, k?tle ak???n?n oldu?u bir sistemde giri? ve ??k?? maddelerinin korundu?unu varsayan bir hesaplama y?netimidir. ?evre M?hendisli?i'nde, at?ksu ar?tma tesislerinde (AAT), giri?, ??k?? at?ksu debileri ve at?ksu ?zelliklerine g?re bile?en ak?lar?n?n hesaplanmas?, farkl? i?letim ko?ullar?n?n kar??la?t?r?lmas? ve tesisinin genel durumunun de?erlendirilmesinde, kat? k?tle dengelerinin haz?rlanmas? ?nemli bir ara? olarak g?z ?n?nde bulundurulmaktad?r. AAT'lerde bu hesaplaman?n yap?lmas?nda uygulanan i?lem ad?mlar? do?rudan uygulanan ad?mlar olmakla birlikte, AAT i?letim verilerine bu dengenin uygulanmas?, ar?t?m proseslerinin ?ok dinamik sistemler olmas? ve tesise giri? y?klerindeki sal?n?mlar nedeniyle ?ok kolay de?ildir (Puig vd., 2008). Her ne kadar k?tle dengesi hesaplamalar? AAT i?letim kalitesini artt?rmak ?zere tercih edilse de (Meijer vd. 2002), AAT'ye ait ham veriden g?venli bir bilginin elde edilmesi genellikle m?mk?n olmamaktad?r. ?rne?in, aktif ?amur sistemine sahip bir biyolojik at?ksu ar?tma tesisinde proses entegrasyonuna y?nelik olarak t?m giren ve ??kan at?ksu debileri, aktif ?amurun ?zellikleri ve ?amur ?retiminin bilinmesi gerekmektedir (Puig vd., 2008). K?tle dengesini i?eren proses tasar?mlar?nda, at?ksu ar?t?m proseslerinin matematiksel modellemesinin m?hendislik ?l?e?inde uygulanmas?, 1990'l? y?llar ve 2000'li y?llar?n ba?lar?ndan beri olduk?a dikkat ?ekmektedir. Proses konfig?rasyonlar?na karar verilmesi ve de?erlendirilmesinde sadece ?zel ?nitelerin tasar?m?nda de?il ayn? zamanda tesis genelinde etkilerin de?erlendirilmesinde, baz? firma ve kurulu?lar taraf?ndan BioWin (EnviroSim Associates Ltd., Flamborough, Ontario, Kanada), GPS-X (Hydromantis Inc., Hamilton, Ontario, Kanada); ATV-131 E (DWA, Almanya) sim?lasyon programlar? geli?tirilmi?tir (WERF, 2010). Bu modeller, giri?-??k?? at?ksu debileri ve at?ksu ?zellikleri, kinetik ve stokiyometrik parametreler ve tesis i?i yan ak?mlar g?z ?n?nde bulundurularak t?m tesis i?in entegre bir k?tle dengesinin geli?tirilmesinde olduk?a yararl?d?r. Bu tez ?al??mas?nda Microsoft Excel program kullan?larak biyolojik at?ksu ar?tma tesislerinde k?tle denkli?inin ??kar?lmas?na y?nelik olarak bir hesaplama program? olu?turulmu?tur. Bu hesaplama y?ntemi, farkl? at?ksu ar?tma ak?m ?emalar?nda uygulanan proseslerin ve onlar?n olu?turdu?u yan ak?mlar?n k?tle dengesi sonu?lar?n? nas?l etkiledi?ini ortaya koymaktad?r. Bu ara?t?rmada ?? farkl? proses t?r? ( klasik aktif ?amur, uzun havaland?rmal? aktif ?amur ve A2/O prosesi) ve ?? farkl? debi (1000 metrek?p/g?n, 10000 metrek?p/g?n, 100000 metrek?p/g?n) ile ?al???lm??t?r. Her bir proses i?in ?? iterasyon yap?lm?? olup, d?rd?nc? bir iterasyona gerek kalmam??t?r. Hesaplamalarda kullan?lan sabitler, katsay?lar ve kabul edilen yakla??mlar, k?yaslama yap?labilmesi a??s?ndan her bir proses i?in ayn? se?ilmi?tir. Klasik ve uzun havaland?rmal? aktif ?amur sistemlerinde yan ak?mlarda debi, BO?, TKM; biyolojik n?trient giderimini yapan A2/O prosesinde, yan ak?mlarda debi, BO?, TKM, organik azot, amonyak azotu, nitrat azotu, toplam azot ve toplam fosfor hesaplamalarda dikkate al?nm??t?r. T?m hesaplamalar, orta derecede kirlili?e sahip evsel at?ksu karakterizasyonu dikkate al?narak yap?lm??t?r. Sonu?larda, klasik aktif ?amur sisteminin di?er sistemlere oranla, iterasyon farklar?n?n baz? hesaplarda y?zde 5'i a?mas?ndan dolay? daha az kararl? oldu?unu g?stermi?tir. Bu y?ksek lisans tezinde, t?m hesaplama ad?mlar?, yap?lan kabuller ve elde edilen sonu?lar proses ve debi farkl?l?klar?na g?re detayl? olarak verilmektedir. Mass balance is a well-known method in many engineering applications including reactor design, process evaluation, and benchmarking. This method assumes and calculates the remaining stable the outputs and inputs of substances in a mass flow system. In environmental engineering field, preparation of mass balances is considered as a very important tool to compute the fluxes of substances compare operational conditions and check the general validity in wastewater treatment facilities (WWTFs). It is a very current way to compute the influent and effluent flows and their characteristics at wastewater treatment plants. However, application of mass balances on WWTP data is mainly difficult since the treatment processes are dynamic systems and the variability of the influent loading is unknown (Puig et al., 2008). Although mass balance calculations have been preferred to improve the quality of WWTF information (Meijer et al. 2002), getting reliable information from raw WWTF data is mainly not possible. For example, to establish a mass balance in the biological WWTFs having activated sludge units for process integrity, all in- and outgoing flows including the activated sludge composition and sludge production should be known (Puig et al., 2008). For process design including mass balances, the use of mathematical modeling of wastewater treatment processes has taken great attention based on engineering scale applications since 1990s and early 2000s. To evaluate and refine process configurations not only in the design of a particular unit process, but also in terms of plant-wide effects, some companies have developed simulation software programs such as BioWin (EnviroSim Associates Ltd., Flam borough, Ontario, Canada); GPS-X (Hydromantis Inc., Hamilton, Ontario, Canada); ATV-131 E (DWA, Germany) (WERF, 2010). The models are very useful to develop the steady-state mass balances of the integrated plant processes regarding the influent and effluent characteristics, kinetic and stoichiometric parameters, and the effects of sidestream loads. In this MSc. Thesis, a spread-sheet was developed by using Microsoft Excel to calculate the mass balance in biological WWTFs. It is capable to present how different wastewater treatment processes and their recycled flows affect the mass balance results. This research has been studied with three kinds of biological wastewater treatment processes -conventional activated sludge process, extended aeration activated sludge process, and A2/O process (BNR)- and flows. It has been computed with three iterations for each processes since there is no need for further iteration. The flows are 1000 cubic meter, 10,000 cubic meter, and 100,000 cubic meter. The constants and coefficients were chosen the same for all processes so it was suitable to comparison. It computed flow, BOD, TSS loads and recycle streams for conventional and extended aeration active sludge systems while flow, BOD, TSS, Org-N, NH4+-N, NO3--N, TN and TP loads were taken into account for biological nutrient removal system (BNR). In the calculations, medium strength domestic/municipal wastewater characteristics were used. The results showed that the conventional active sludge system is less stable than the other processes since its iteration differences exceeding 5 percent are higher than those in the other processes. The computing steps and the results are given in details and compared them for the treatment processes and flows examined in the thesis

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,339
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,311
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle