Sensitivity of Effluent Variables in Activated Sludge Process
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B Vivekanandan
Abstract
The quality of a treated effluent changes when there is a sudden variation in the influent flow to the wastewater treatment plant during dry, rain, and storm weather conditions. In this study, various influent flow conditions in an activated sludge process are considered that affect the sensitivity of effluent variables such as chemical oxygen demand (COD), biological oxygen demand (BOD), nitrate nitrogen (SNO), ammonical nitrogen (SNH), and total nitrogen (TN) with respect to varying internal recycle flow rate (Qa), sludge recycle flow rate (Qr), sludge wastage flow rate (Qw) and oxygen transfer rate co-efficient of aerobic tanks (KLa(3,4,5)). The analysis has been carried out based on benchmark simulation model no.1 (BSM 1) plant layout which comprises of two models namely activated sludge model no.1 (ASM 1) and simple one dimensional (Simple 1-D) Takacs model. Based on the present analysis, it is observed that the changes in influent flow rate have larger impact on the effluent variables. This variation can be subdued by introducing additional tanks to smoothen the perturbations or using internal recycle rate from the fifth tank in order to maintain the flow around the optimal influent flow rate. The sludge wastage rate has a greater impact on all effluent variables except nitrogenous variables during maximum flow conditions.
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Artikel in diesem Heft
- Research Articles
- Sensitivity of Effluent Variables in Activated Sludge Process
- Optimization of Pressure-Swing Distillation by Evolutionary Techniques: Separation of Ethanol-Water and Acetonitrile-Water Mixtures
- Phase Split in T-Junction Mini Channel – A Numerical Study
- Simulation and Dynamic Optimization of an Industrial Naphtha Thermal Cracking Furnace Based on Time Variant Feeding Policy
- Mathematical Modeling and Optimization of Syngas Production Process: A Novel Axial Flow Spherical Packed Bed Tri-Reformer
- Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks
