The effects of temperature, mixed liquor suspended solids and velocity on the removal of organic pollutants and kinetic modelling in wastewater sewer systems

Amiri, Roonak; Ahmadi, Mehdi; Piri, Homeyra

doi:10.1080/03067319.2020.1730340

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…When there is high population of microorganisms in the biological treatment process, the probability of decomposing organic matter and oxidizing inorganic matter increases. Furthermore, the interaction between organic matter, surface carbon, oxygen, and silicon enhance removal at high temperatures [ 28 ].…”

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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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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Surface-templating of rough interface to efficiently recover aquaculture wastewater using membrane distillation

et al. 2022

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Modelling and optimization of energy consumption in the activated sludge biological aeration unit

Muloiwa

Dinka

Nyende‐Byakika

2022

Water Practice and Technology

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The biological aeration unit consumes the highest energy (67.3%) in wastewater treatment compared with physical (18.8%) and chemical (13.9%) treatment processes. The high energy consumption is caused by the supply of oxygen using air pumps/blowers and temperature that controls microorganisms' growth. The purpose of this study was to model and optimize energy consumption in the biological aeration unit. The multilayer perceptron (MLP) artificial neural network (ANN) algorithm was used to model energy consumption. The particle swarm optimization (PSO) algorithm was used to optimize the energy consumption model. Sensitivity analysis was performed to determine the percentage contribution of input variables towards energy consumption. The MLP ANN algorithm modelled energy consumption successfully and produced R², RMSE, and MSE of 0.89, 0.0265, and 0.00070, respectively, during the testing phase. The PSO algorithm optimized energy consumption successfully and produced a global solution of 0.993 kWh/m³. The percentage reduction between the lowest measured and optimized energy consumption was 38.4%. Aeration period (81%) and temperature (10.7%) contributed the highest towards energy consumption. In conclusion, the temperature played a significant role in energy consumption compared with the airflow rate (4.2%). When the temperature is conducive to allowing the growth of microorganisms, the removal of COD and ammonia will be rapid resulting in low energy consumption.

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The effects of temperature, mixed liquor suspended solids and velocity on the removal of organic pollutants and kinetic modelling in wastewater sewer systems

Cited by 4 publications

References 20 publications

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

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

Surface-templating of rough interface to efficiently recover aquaculture wastewater using membrane distillation

Modelling and optimization of energy consumption in the activated sludge biological aeration unit

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