Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models.
Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models.A ctivated sludge is the most widely used biological technology for treating domestic and industrial wastewater. After its development with 100 years of history, many novel and modified processes have been developed to meet the more and more stringent discharge and emission limits. However, most of operating systems are suffering some drawbacks, such as substantial energy consumption, excessive greenhouse gas emission, and labour-intensive industry. As a powerful tool, Activated Sludge Models (ASMs) have proven to be very useful in process design, operation and optimization 1,2 . To date, ASMs for the simulation of biological nutrients removal processes have been updated from the first version of ASM1 to more complicated extensions, including ASM2, ASM2d, and ASM3 3 , and further to the extended ASM3s 4-10 in order to satisfy various requirements.However, ASMs are large and overparameterized models in terms of having many stoichiometric and kinetic parameters. Some of the model parameters as well as the model structure have to be adjusted, since microbial community structure and dominant species can vary in different wastewater treatment systems with different influent characteristics or operation schemes 6,7,[11][12][13][14] . In addition, the collected data from full-scale plants as well as pilot-or lab-scale reactors can hardly provide reliable estimations of all the parameters simultaneously due to the well-known problem of poorly identifiable parameters [15][16][17][18][19][20] . Thus, the approach to properly select the subsets of parameters for model calibration plays a crucial role on simulation results and model applications [21][22][23][24][25] . Until now, substantial studies have been conducted to develop effective model calibration approaches, which can be distinguished into two m...
Filamentous bulking is a complicated problem in wastewater treatment plants treating various wastewaters, leading to the deterioration of the settling properties and the effluent quality. This study systematically investigated long-term effects of various carbon sources and feeding patterns on the growth of filamentous bacteria, in order to reveal the mechanism of filamentous bulking. Sludge volume index (SVI), microscopic observations, staining (Gram and Neisser staining), scan electron microscopic, and fluorescent in situ hybridization (FISH) were used to monitor the bulking and track the changes of microbial morphology and community structure of activated sludge in six lab-scale sequencing batch reactors (SBRs) fed with different carbon sources. Filamentous bulking was not observed in all SBRs under anoxic feeding pattern with a short fill time, in which SVI remained below 150 mL/g. In contrast, serious bulking (SVI > 500 mL/g) occurred under aerobic feeding pattern when fed with ethanol, propionate, acetate, and glucose, in which Thiothrix and Sphaerotilus natans proliferated as dominant filaments. Compared to glucose-fed reactor, relatively light bulking was caused in starch-fed reactor with the growth of Nostocoida limicola II. In addition, flocs in starch-fed reactor were more open and fluffy than flocs formed on readily biodegradable substrates. Finally, a framework integrating kinetic selection, diffusion selection, storage selection, and protozoa capture mechanism was proposed to explain filamentous bulking.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.