Preliminary regression models for estimating first-order rate constants for removal of BOD and COD from landfill leachate

Bhatt, Arpita H.; Altouqi, Said; Karanjekar, Richa V.; Hossain, Md. Delwar; Chen, Victoria P.; Sattler, Melanie L.

doi:10.1016/j.eti.2016.02.002

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…The few attempts to display leachate quality/attributes utilizing measurable techniques or programming have likewise centered around a single landfill or couple of area landfills. This investigation utilizes a complete lab-scale reactor plan that covers wide scopes of contact time, pH, H 2 O 2 :Fe 2+ ratio and Fe 2+ concentration [15,17]. Table 1 shows the preliminary full-factorial design in four steps.…”

Section: Methodsmentioning

confidence: 99%

“…MLR equation is a weighted linear combination of the independent variables [11][12][13][14].Very few studies have explored the use of MLR model for landfill leachate treatment. Bhat et al [15] developed equations for estimating biological oxygen demand (BOD) and chemical oxygen demand (COD) removal from landfill leachate. Kamyab et al [16] reported that microalgae and macrophytes from agro-industrial waste were capable of removing nutrients only 4.4% of COD.…”

Section: Introductionmentioning

confidence: 99%

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Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

et al. 2019

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M UNICIPAL Solid Waste (MSW) landfill leachate contains highly concentrated organic substances which are hazardous to the environment. Therefore, it must be treated before discharged into water bodies and suitable techniques are essential for effective treatment. In this regard, multiple linear regression (MLR) model has shown to be a favorable technique for optimization of landfill leachate treatment. In this study, four operational variables, H 2 O 2 :Fe 2+ ratio, pH, reaction time, and Fe 2+ concentration, were assessed using the model. The chemical oxygen demand (COD) removal was 96.43% by experiment, while the predicted assessment was 100% under the optimized settings; time = 33.87 min, concentration of Fe 2+ = 749.64 mg/L, pH = 3, and ratio of H 2 O 2 :Fe 2+ = 2 during Fenton treatment. The high value of the coefficient of determination, R 2 = 0.896, designates a resilient relationship between the experimental and model values. The residual study (residual plot) specified that the points were randomly distributed, confirming the appropriateness of the model. The MLR model established may possibly be used for assessment of other landfill leachate treatment.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

et al. 2019

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“…Since the biochemical oxygen demand (BOD5) and ammonia as nitrogen (NH3-N) indicators in allowable wastewater are considered as a measure of water organic pollutants [30][31][32][33], their total amount is considered as indicators of the water quality variable (waste pollutants) in this study. According to the results from the report on the impact of cascade hydropower plants on the environment in the Lancang River [34], kBOD5 in Equation 7 For the hydraulic parameters in this study area for Equations (8) and (13), the water velocity in the river can be approximated by Equations (15).…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

“…The initial water storage V 0 in Equations (7) and (13) is set to be the storage at the normal water level according to the Jinghong reservoir operation rules. u = aQ b (15) Since the biochemical oxygen demand (BOD 5 ) and ammonia as nitrogen (NH 3 -N) indicators in allowable wastewater are considered as a measure of water organic pollutants [30][31][32][33], their total amount is considered as indicators of the water quality variable (waste pollutants) in this study. According to the results from the report on the impact of cascade hydropower plants on the environment in the Lancang River [34], k BOD5 in Equation 7was calibrated and validated as 0.042 day −1 in the wet season (from June to October), 0.054 day −1 in the dry season (from January to May), and 0.048 day −1 during the normal water season (November and December).…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Modeling the Physical Nexus across Water Supply, Wastewater Management and Hydropower Generation Sectors in River–Reservoir Systems

Liu

Zhang

Zeng

et al. 2019

Water

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Most water supply and hydropower generation is obtained from the river–reservoir system, and wastewater pollutants are also dumped into the system. Increasing water demand and consumption have caused the water supply, wastewater pollutant management and hydropower generation sectors to be interlinked and to reinforce each other in the system. A physical nexus across water supply, wastewater management and hydropower generation sectors for a river–reservoir system was developed based on the analytical water quality and hydropower generation equations. Considering the Jinghong hydropower reservoir, located in the middle and lower reaches of the Lancangjiang River Basin, as a case study, both the wastewater pollutant management target and water inflow from the upstream as the external and boundary conditions, were employed to establish the effects of the external and boundary conditions on the nexus. It was demonstrated that the nexus of water supply and hydropower generation sectors does not vary with the water quality indicators and its protection target, without the separation of environmental flow in hydropower generation flow. In addition, the amount of hydropower generation decreases with increasing water supply. However, the lapse rates of allowable wastewater pollutants–water supply differ based on the water inflow and the wastewater pollutant management sectors, while the efficiency of hydropower generation and the sensitivity of allowable wastewater pollutants per amount of water supply are considered to be unrelated to the water inflow and wastewater pollutant management target conditions. The quantitative nexus developed through the proposed equation not only contributes to a more complete understanding of the mechanism of cross-connections, but also in creation of specific water protection and utilization measures, which is also the focus of the water–energy nexus.

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“…De fato, para muitos estudos, os modelos determinísticos de primeira ordem representam um equilíbrio entre a simplicidade e a precisão, e, por esse motivo, a cinética de primeira ordem é a mais aceita para descrever a progressão de DBO exercida no tempo (BHATT et al, 2016;SIBIL;BERKUN;BEKIROGLU, 2014). Já alguns autores (ADRIAN;SANDERS, 1998;ZANONI, 1967) afirmam que o modelo de segunda ordem seria o mais adequado para descrição da DBO no tempo do que o modelo de primeira ordem, visto que a soma das progressões do consumo de cada composto presente na água residuária é algo de natureza complexa.…”

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Caracterização físico-química e determinação de coeficientes cinéticos aeróbios de remoção da matéria orgânica de águas residuárias agroindustriais

Morais

Coelho

Silva

et al. 2020

Eng. Sanit. Ambient.

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RESUMO Os objetivos do presente trabalho foram realizar uma caracterização físico-química de diferentes águas residuárias agroindustriais (ARA) e aplicar modelos cinéticos de primeira e de segunda ordem para verificar qual deles descreve melhor a progressão da demanda bioquímica de oxigênio (DBO) e para determinar coeficientes cinéticos aeróbios de remoção da matéria orgânica das ARA. Efetuou-se o ensaio de progressão da DBO pela incubação de ARA em um sistema respirométrico (Oxitop®). Os modelos cinéticos de primeira e de segunda ordem foram avaliados por erro quadrático médio (RMSE), erro quadrático médio normalizado (NRMSE) e critério de informação de Akaike (AIC). A cinética de biodegradação aeróbia das ARA avaliadas ajustou-se melhor ao modelo de primeira ordem em termos de DBO total e solúvel. Na progressão de DBO total, o maior coeficiente de desoxigenação de primeira ordem (k’) foi o da água residuária de abatedouro - ARB (0,56 d-1) - e o menor foi o da água residuária de suinocultura - ARS (0,16 d-1). Os coeficientes de desoxigenação determinados no presente trabalho mostraram-se representativos, podendo ser utilizados para a simulação de processos de degradação da matéria orgânica em condições aeróbias.

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Preliminary regression models for estimating first-order rate constants for removal of BOD and COD from landfill leachate

Cited by 21 publications

References 13 publications

Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

Modeling the Physical Nexus across Water Supply, Wastewater Management and Hydropower Generation Sectors in River–Reservoir Systems

Caracterização físico-química e determinação de coeficientes cinéticos aeróbios de remoção da matéria orgânica de águas residuárias agroindustriais

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