Numerical analysis of the circular settling tank

Chero, Elahe; Torabi, Mohammadamin; Zahabi, Hamidreza; ghafoorisadatieh, Anahita; Bina, Keivan

doi:10.5194/dwes-12-39-2019

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…The summary of the theoretical sedimentation tank geometry is presented in Table 5. The finding indicates a practical approach to determining the sizing for an economical treatment unit [47].…”

Section: Machine Learning Optimized Sedimentation Tank Specificationsmentioning

confidence: 89%

Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate

Ugonabo

Ovuoraye

Chowdhury³

et al. 2022

J. Eng. Appl. Sci.

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Small-scale preliminary studies are necessary to determine the feasibility of the machine learning (ML) algorithm and time-evolution kinetics to meet the design specification of the treatment unit. The train and test datasets were obtained from jar test experimentation on the petroleum industry effluent (PIE) sample using aluminum sulfate (AS) as the coagulant. The ML algorithm from scikit-learn was employed to determine the optimum operating condition for the removal of colloidal particles, causing turbidity in the PIE. The predictive capacity of four ML models was compared based on their statistical metrics for clean discharge. The predicted optimum condition corresponds to pH (10), dosage (0.1 g/L), and settling time (30 min) which transcends to residual turbidity ≤ 10 NTU and translates to 95% removal efficiency. The second-order AS-sweep flocculation kinetic showed that at the predicted optimum conditions, modeled rate constant of 1.33 × 10−3 L/g.min and flocculation period of 1.2 min reduced the combination of the monomer, dimmer, and trimmer class colloids from an initial 570 mg/L concentration to the residual counts of 24 mg/L corresponding to residual turbidity ≤ 10 NTU under the mixing regime 14 s−1 ≤ G ≤ 164 s−1 satisfied the EPA standard for clean effluent discharge. It incorporated the selected ML output with time-evolution and aggregation kinetics to define sedimentation tank geometry for cleaner discharge. The findings from the design-driven optimization recommended a flow rate (1000 m3s−1), coefficient of kinematic viscosity (0.841 mm/s), and the required detention time (30–60 min) to define the sedimentation tank geometry.

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Section: Machine Learning Optimized Sedimentation Tank Specificationsmentioning

confidence: 89%

Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate

Ugonabo

Ovuoraye

Chowdhury³

et al. 2022

J. Eng. Appl. Sci.

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“…where k is the turbulent kinetic energy per unit mass, ε is the turbulence dissipation rate per unit mass, G k is the production of turbulent kinetic energy due to velocity gradient, G b is turbulent kinetic energy production from buoyancy, and Y M is turbulence dilation oscillation distribution [39,40]. In the above equations, α k = α s = 1.39, C 1s = 1.42, and C 2ε = 1.6 are model constants.…”

Section: Numerical Modelmentioning

confidence: 99%

Energy Dissipation and Hydraulics of Flow over Trapezoidal–Triangular Labyrinth Weirs

Ghaderi

Daneshfaraz

Dasineh

et al. 2020

Water

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In this work experimental and numerical investigations were carried out to study the influence of the geometric parameters of trapezoidal–triangular labyrinth weirs (TTLW) on the discharge coefficient, energy dissipation, and downstream flow regime, considering two different orientations in labyrinth weir position respective to the reservoir discharge channel. To simulate the free flow surface, the volume of fluid (VOF) method, and the Renormalization Group (RNG) k-ε model turbulence were adopted in the FLOW-3D software. The flow over the labyrinth weir (in both orientations) is simulated as a steady-state flow, and the discharge coefficient is validated with experimental data. The results highlighted that the numerical model shows proper coordination with experimental results and also the discharge coefficient decreases by decreasing the sidewall angle due to the collision of the falling jets for the high value of H/P (H: the hydraulic head, P: the weir height). Hydraulics of flow over TTLW has free flow conditions in low discharge and submerged flow conditions in high discharge. TTLW approximately dissipates the maximum amount of energy due to the collision of nappes in the upstream apexes and to the circulating flow in the pool generated behind the nappes; moreover, an increase in sidewall angle and weir height leads to reduced energy. The energy dissipation of TTLW is largest compared to vertical drop and has the least possible value of residual energy as flow increases.

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“…Several experimental studies and three-dimensional (3D) computational fluid dynamics (CFD) simulations on PKWs with different geometries have been conducted to develop a better understanding of flow behavior and to identify the primary and secondary parameters influencing its discharge capacity and hydraulic performance [7]- [14]. The magnification ratio (L/W) and weir height (P) was reportedly the primary parameters that dominate the discharge capacity of a PKW, whereas overhang ratio (Bi/Bo) and key width ratio (Wi/Wo) were identified as secondary parameters [12] [13] [15] [16] [17].…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on Hydraulic Characteristics and Discharge Capacity of Modified Piano Key Weir with Various Inlet/Outlet Width Ratio

Ich¹,

Zhang²,

Li³

et al. 2023

WJET

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A modified piano key weir with a rounded nose and a parapet wall (MPKW) can improve the discharge capacity significantly compared to a standard piano key weir. However, the optimum of the inlet/outlet width ratio (Wi/Wo) on the discharge efficiency of MPKW is still not investigated numerically. The present work utilized the numerical modeling to investigate and analyze the effects of the inlet/outlet key width ratios on the hydraulic characteristics and discharge capacity of the MPKW. To validate the numerical model with the experimental data, the results indicate that the average relative error is 2.96%, which confirms that the numerical model is fairly well to predict the specifications of flow over on the MPKW. Numerical simulation results indicated that the discharge capacity of the MPKW can be improved up to 8.5% by optimizing the Wi/Wo ratio ranging from 1.53 to 1.67 even if the other parameters of the MPKW keep unchanged. A big Wi/Wo ratio generally leads to an increase in discharge capacity at low heads and a little effect on the discharge efficiency at high heads. The discharge efficiency of the inlet and outlet crests increases up to 9.6% for high heads, while discharge efficiency of the lateral crest decreases up to 23.5% compared with the reference model. The findings of the study revealed that the intrinsic influencing mechanism of the Wi/Wo ratio on the discharge performance of MPKWs.

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Numerical analysis of the circular settling tank

Cited by 9 publications

References 14 publications

Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate

Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate

Energy Dissipation and Hydraulics of Flow over Trapezoidal–Triangular Labyrinth Weirs

Numerical Study on Hydraulic Characteristics and Discharge Capacity of Modified Piano Key Weir with Various Inlet/Outlet Width Ratio

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