Modelling waste stabilisation ponds with an extended version of ASM3

Gehring, Tito; Silva, J. D.; Kehl, O.; Castilhos, Armando Borges de; Costa, Rejane Helena Ribeiro da; Uhlenhut, Frank; Alex, J.; Horn, Harald; Wichern, Marc

doi:10.2166/wst.2010.954

Cited by 16 publications

(13 citation statements)

References 18 publications

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“…Since Buhr and Miller (1983), other more sophisticated models have been developed mostly based on parameters and processes similar to those defined by the River Water Quality Model 1 (RWQM1) of the International Water Association (Reichert et al, 2011). However, while RWQM1 includes expressions for growth of microalgae on N (ammonium and nitrate) and P (orthophosphate), it does not include expressions for essential C limitations (carbon dioxide and bicarbonate) which can occur in algae growing in wastewater systems (Gehring et al, 2010). In the model by Sah et al (2011) algal growth was described as a function of either ammonium or nitrate, with preference for ammonium.…”

Section: Introductionmentioning

confidence: 99%

Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds

Solimeno

Parker

Lundquist

et al. 2017

Science of The Total Environment

111

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An integral mechanistic model describing the complex interactions in mixed algal-bacterial systems was developed. The model includes crucial physical, chemical and biokinetic processes of microalgae as well as bacteria in wastewater. Carbon-limited microalgae and autotrophic bacteria growth, light attenuation, photorespiration, temperature and pH dependency are some of the new features included. The model named BIO_ALGAE was built using the general formulation and structure of activated sludge models (ASM), and it was implemented in COMSOL Multiphysics™ platform. Calibration and validation were conducted with experimental data from two identical pilot HRAPs receiving real wastewater. The model was able to simulate the dynamics of different components in the ponds, and to predict the relative proportion of microalgae (58-68% in average of total suspended solids (TSS) and bacteria (30-20% in average of TSS). Microalgae growth resulted strongly influenced by the light factor f(I), decreasing microalgae concentrations from 40 to 60%. Furthermore, reducing the influent organic matter concentration of 50% and 70%, model predictions indicated that microalgae production increased from (8.7gTSSmd to 13.5gTSSmd) due to the new distribution of particulate components. The proposed model could be an efficient tool for industry to predict the production of microalgae, as well as to design and optimize HRAPs.

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Section: Introductionmentioning

confidence: 99%

Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds

Solimeno

Parker

Lundquist

et al. 2017

Science of The Total Environment

111

View full text Add to dashboard Cite

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“…The River Water Quality Model 1 (RWQM1 (Reichert et al., 2001)) introduced a set of differential equations to model the acid-base equilibria for inorganic carbon, ammonia, and phosphate, as well as the water acid-base equilibrium. This approach was later modified to a system of differential (for biological process rates) and algebraic (for fast chemical reactions) equations (DAE), where pH is numerically estimated by closing the charge balance (i.e., mono-dimensional numerical methods) in the system (Broekhuizen et al., 2012; Gehring et al., 2010; Wolf et al., 2007). The application of DAE systems to predict pH is commonly used to model other wastewater systems, as it reduces the stiffness of the model compared to differential equation systems (Batstone et al., 2012, 2002; Hellinga et al., 1999).…”

Section: Mechanistic Modeling Of Phytoplanktonmentioning

confidence: 99%

Microalgae and cyanobacteria modeling in water resource recovery facilities: A critical review

Shoener

Schramm

Béline³

et al. 2019

Water Research X

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Microalgal and cyanobacterial resource recovery systems could significantly advance nutrient recovery from wastewater by achieving effluent nitrogen (N) and phosphorus (P) levels below the current limit of technology. The successful implementation of phytoplankton, however, requires the formulation of process models that balance fidelity and simplicity to accurately simulate dynamic performance in response to environmental conditions. This work synthesizes the range of model structures that have been leveraged for algae and cyanobacteria modeling and core model features that are required to enable reliable process modeling in the context of water resource recovery facilities. Results from an extensive literature review of over 300 published phytoplankton models are presented, with particular attention to similarities with and differences from existing strategies to model chemotrophic wastewater treatment processes (e.g., via the Activated Sludge Models, ASMs). Building on published process models, the core requirements of a model structure for algal and cyanobacterial processes are presented, including detailed recommendations for the prediction of growth (under phototrophic, heterotrophic, and mixotrophic conditions), nutrient uptake, carbon uptake and storage, and respiration.

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“…Gehring et al (2010) observaram, estudando lagoas de estabilização em série tratando lixiviado de aterro sanitário, remoção de amônia na ordem de 89,5% e encontraram taxas insignificantes de nitrificação e intensiva volatilização de amônia, tal como no trabalho de Rockne & Brezonik (2006), que estudaram lagoas de estabilização tratando esgoto doméstico e verificaram 80% de volatilização da amônia não ionizada em pH superior a 8,0, mesmo sob condições de baixas temperaturas.…”

Section: Resultsunclassified

Avaliação de sistema de tratamento de dejetos suínos instalado no estado de Santa Catarina

Araujo¹,

Oliveira²,

Alves³

et al. 2012

Rev. bras. eng. agríc. ambient.

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R ESU M OOs dejetos suínos contribuem de forma significativa em prol da degradação dos recursos naturais e para a diminuição da qualidade de vida na região sul do estado de Santa Catarina, Brasil. O excesso de dejetos gerados pelo grande número dos suínos por unidade de área dificulta a solução do problema. Este trabalho teve como objetivo avaliar o comportamento de um sistema de tratamento de dejetos suínos, em série e em escala real, composto de unidades anaeróbias, unidades aeróbias e pós-tratamento. Os parâmetros analisados foram: DBO, DQO, PT, P-PO 4 , N-NH 3 , NTK, pH, clorofila a, biomassa algal, coliformes totais e coliformes fecais (Escherichia coli). O sistema mostrou que após um ano de funcionamento as eficiências de remoção foram satisfatórias para matéria orgânica e nutrientes, alcançando valores de 97% para DBO, 95% para DQO, 88% para N-NH 3 e 74% para PT/P-PO 4 , mesmo com as variações de vazão e de carga orgânica aplicada. Entretanto, a remoção de coliformes fecais nas unidades aeróbias foi baixa, reduzindo somente 0,86 unidades log na lagoa facultativa aerada e 0,80 unidades log na lagoa de maturação. Palavras-chave: reatores anaeróbios, lagoas de estabilização, filtro de pedrasThe evaluation of swine waste treatment system installed in Santa Catarina State A B ST R A C T The swine manure contributes significantly to the degradation of natural resources and to decrease the quality of life in the southern state of Santa Catarina, Brazil. The excess of manure produced by the large number of pigs per unit area hinders the solution of the problem. This study aimed to evaluate the behavior of a system developed to treat swine manure, in series and in real scale, composed of anaerobic units, aerobic units and post-treatment. The parameters analyzed were: BOD, COD, TP, PO 4 -P, NH 3 -N, TKN, pH, chlorophyll a, algal biomass, total coliforms and fecal coliforms (Escherichia coli). The system showed that, after one year of operation, the removal efficiencies were satisfactory for organic matter and nutrients, reaching values of 97% for BOD, 95% for COD, 88% for N-NH 3 and 74% for PT/P-PO 4 , even with variations in flow and organic load applied. However the removal of fecal coliforms in the aerobic units was low, decreasing only 0.86 log units in the aerated facultative pond and 0.80 log units in the maturation pond.

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Modelling waste stabilisation ponds with an extended version of ASM3

Cited by 16 publications

References 18 publications

Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds

Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds

Microalgae and cyanobacteria modeling in water resource recovery facilities: A critical review

Avaliação de sistema de tratamento de dejetos suínos instalado no estado de Santa Catarina

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