How elevation dictates technology selection in biological wastewater treatment

Baquero-Rodríguez, Gustavo Andrés; Martínez, Sandra Patricia; Acuña, Julián; Nolasco, Daniel; Rosso, Diego

doi:10.1016/j.jenvman.2022.114588

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The results of the tests developed in different locations (Table 1) were analyzed to assess the effect of environmental conditions on oxygen transfer efficiency to water. The results are consistent with previously depicted relationships between environmental conditions and diffused aeration into water [27]. As dissolved oxygen saturation is a function of atmospheric pressure and water temperature, the driving force for oxygen transfer into water is exclusively dependent on local environmental conditions.…”

Section: Influence Of Environmental Conditionssupporting

confidence: 92%

“…As dissolved oxygen saturation is a function of atmospheric pressure and water temperature, the driving force for oxygen transfer into water is exclusively dependent on local environmental conditions. The larger the driving force, the greater the oxygen transfer [27]. The results presented herein are limited to gravel beds as recommended for aerated constructed wetlands (Table 2).…”

Section: Influence Of Environmental Conditionsmentioning

confidence: 99%

“…Oxygen solubility was inversely correlated with water temperature and directly correlated with atmospheric pressure [28,29]; therefore, location 1 had higher values for oxygen solubility (Table 1). Baquero et al [27] presented a detailed review of environmental factors, including atmospheric pressure, that affect oxygen solubility in a wastewater treatment context.…”

Section: Influence Of Environmental Conditionsmentioning

confidence: 99%

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Effects of Environmental Conditions and Bed Configuration on Oxygen Transfer Efficiency in Aerated Constructed Wetlands

Vera-Puerto

Campal

Martínez

et al. 2022

Water

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This research evaluated the oxygen transfer efficiency in beds to be used as aerated constructed wetlands. The research methods included oxygen transfer efficiency evaluations in several bed configurations using diffused aeration systems. Experiments were conducted at two locations with different environmental conditions: a) Talca (Chile), 120 m above sea level (m.a.s.l.), 0.99 Atm and b) Cajicá (Colombia), 2550 m.a.s.l., 0.76 Atm. A column with only clean water and three bed configurations representing aerated constructed wetlands were evaluated. These configurations included: (a) coarse gravel, (b) coarse gravel with an empty core in the middle (inner container), and (c) fine gravel. Three airflow rates were evaluated: (a) low, 0.7 L/min; (b) medium, 2.5 L/min; and (c) high, 3.6 L/min. The overall oxygen mass transfer coefficient, standard oxygen transfer rate, and standard oxygen transfer efficiency were the variables calculated from the oxygen transfer evaluation tests. The research results indicated that in diffused aeration systems, oxygen transfer efficiency was negatively influenced by environmental conditions, particularly altitude, which limits the driving force for oxygen transfer into water. Furthermore, the results showed that the size of the gravel used in the bed is related to the oxygen transfer efficiency: the larger the gravel size, the higher the oxygen transfer, regardless of the altitude. Finally, research regarding oxygen transfer in aerated constructed wetlands has signaled the need for a standard procedure for aeration testing, and this work suggests a new methodology.

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Section: Influence Of Environmental Conditionssupporting

confidence: 92%

Section: Influence Of Environmental Conditionsmentioning

confidence: 99%

Section: Influence Of Environmental Conditionsmentioning

confidence: 99%

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Effects of Environmental Conditions and Bed Configuration on Oxygen Transfer Efficiency in Aerated Constructed Wetlands

Vera-Puerto

Campal

Martínez

et al. 2022

Water

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“…High airflow rates produce large bubbles that have high buoyancy force which splits and destroys the microorganisms resulting in a slow grow and possible death of microorganisms. Therefore, high airflow rates will only result in high energy consumption in the biological treatment process which is a global concern [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Muloiwa

Dinka

Nyende‐Byakika

2023

Engineering in Life Sciences

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The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination ( R 2 ), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R 2 values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.

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Patterns of forest community diversity, regeneration potential and carbon storages along an altitudinal gradient in Eastern Himalaya, India

Ao,

Changkija,

Tripathi

2024

Environmental and Sustainability Indicators

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How elevation dictates technology selection in biological wastewater treatment

Cited by 11 publications

References 18 publications

Effects of Environmental Conditions and Bed Configuration on Oxygen Transfer Efficiency in Aerated Constructed Wetlands

Effects of Environmental Conditions and Bed Configuration on Oxygen Transfer Efficiency in Aerated Constructed Wetlands

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Patterns of forest community diversity, regeneration potential and carbon storages along an altitudinal gradient in Eastern Himalaya, India

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