Modelling gas-liquid mass transfer in wastewater treatment : when current knowledge needs to encounter engineering practice and vice versa

Abstract Gas–liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas–liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid–base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas–liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas–liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas–liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas–liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners

Long-term simulation of a full-scale EBPR plant with a novel metabolic-ASM model and its use as a diagnostic tool

This study evaluates the predictive capacity of the META-ASM model, a new integrated metabolic activated sludge model, in describing the long-term performance of a full-scale enhanced biological phosphorus removal (EBPR) system that suffers from inconsistent performance. In order to elucidate the causes of EBPR upsets and troubleshoot the process accordingly, the META-ASM model was tested as an operational diagnostic tool in a 1336-day long-term dynamic simulation, while its performance was compared with the ASM-inCTRL model, a version based on the Barker & Dold model. Overall, the predictions obtained with the META-ASM without changing default parameters were more reliable and effective at describing the active biomass of polyphosphate accumulating organisms (PAOs) and the dynamics of their storage polymers. The primary causes of the EBPR upsets were the high aerobic hydraulic retention times (HRTs) and low organic loading rates (OLRs) of the plant, which led to periods of starvation. The impact of these factors on EBPR performance were only identified with the META-ASM model. Furthermore, the first signs of process upsets were predicted by variations in the aerobic PAO maintenance rates, suggesting that the META-ASM model has potential to provide an early warning of process upset. The simulation of a new viable operational strategy indicated that troubleshooting the process could be achieved by reducing the aerated volume by switching off air in the first half of the aeration tank. In this new strategy, the META-ASM model predicted a simultaneous improvement in the biological phosphorus (P) and nitrogen (N) removal due to the enhancement of the hydrolysis and fermentation of the mixed liquor sludge in the new unaerated zone, which increased the availability of volatile fatty acids (VFAs) for PAOs. This study demonstrates that the META-ASM model is a powerful operational diagnostic tool for EBPR systems, capable of predicting and mitigating upsets, optimising performance and evaluating new process designs

Performance Evaluation of Fault Detection Methods for Wastewater Treatment Processes

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods. © 2010 Wiley Periodicals, Inc.This research is supported by the Canada Research Chair in Water Quality Modeling and a NSERC Special Research Opportunities grant as part of the Canadian contribution to the European Union 6th framework project NEPTUNE. Lluis Corominas benefits from the postdoctoral fellowship "Beatriu de Pinos" of the Government of Catalonia. The authors would like to thank Ulf Jeppsson for his contribution to the development of the BSM1_LT platform and the evaluation index.Corominas, L.; Villez, K.; Aguado García, D.; Rieger, L.; Rosén, C.; Vanrolleghem, PA. (2011). Performance Evaluation of Fault Detection Methods for Wastewater Treatment Processes. Biotechnology and Bioengineering. 108(2):333-344. doi:10.1002/bit.22953S3333441082Aguado, D., & Rosen, C. (2008). Multivariate statistical monitoring of continuous wastewater treatment plants. Engineering Applications of Artificial Intelligence, 21(7), 1080-1091. doi:10.1016/j.engappai.2007.08.004Aguado, D., Ferrer, A., Ferrer, J., & Seco, A. (2007). Multivariate SPC of a sequencing batch reactor for wastewater treatment. Chemometrics and Intelligent Laboratory Systems, 85(1), 82-93. doi:10.1016/j.chemolab.2006.05.003BSM 2009 http://www.benchmarkwwtp.orgGenovesi, A., Harmand, J., & Steyer, J.-P. (1999). A fuzzy logic based diagnosis system for the on-line supervision of an anaerobic digestor pilot-plant. Biochemical Engineering Journal, 3(3), 171-183. doi:10.1016/s1369-703x(99)00015-7Lee, D. S., & Vanrolleghem, P. A. (2003). Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. Biotechnology and Bioengineering, 82(4), 489-497. doi:10.1002/bit.10589Lee, D. S., Park, J. M., & Vanrolleghem, P. A. (2005). Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor. Journal of Biotechnology, 116(2), 195-210. doi:10.1016/j.jbiotec.2004.10.012Lennox, J., & Rosen, C. (2002). Adaptive multiscale principal components analysis for online monitoring of wastewater treatment. Water Science and Technology, 45(4-5), 227-235. doi:10.2166/wst.2002.0593Rieger, L., Alex, J., Winkler, S., Boehler, M., Thomann, M., & Siegrist, H. (2003). Progress in sensor technology - progress in process control? Part I: Sensor property investigation and classification. Water Science and Technology, 47(2), 103-112. doi:10.2166/wst.2003.0096Rieger, L., Alex, J., Gujer, W., & Siegrist, H. (2006). Modelling of aeration systems at wastewater treatment plants. Water Science and Technology, 53(4-5), 439-447. doi:10.2166/wst.2006.100Rosen, C., & Lennox, J. A. (2001). Multivariate and multiscale monitoring of wastewater treatment operation. Water Research, 35(14), 3402-3410. doi:10.1016/s0043-1354(01)00069-0Rosen, C., Jeppsson, U., & Vanrolleghem, P. A. (2004). Towards a common benchmark for long-term process control and monitoring performance evaluation. Water Science and Technology, 50(11), 41-49. doi:10.2166/wst.2004.0669Rosen, C., Rieger, L., Jeppsson, U., & Vanrolleghem, P. A. (2008). Adding realism to simulated sensors and actuators. 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Computers & Chemical Engineering, 27(3), 327-346. doi:10.1016/s0098-1354(02)00162-xVillez, K., Ruiz, M., Sin, G., Colomer, J., Rosén, C., & Vanrolleghem, P. A. (2008). Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes. Water Science and Technology, 57(10), 1659-1666. doi:10.2166/wst.2008.143Yoo, C. K., Villez, K., Lee, I.-B., Rosén, C., & Vanrolleghem, P. A. (2007). Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor. Biotechnology and Bioengineering, 96(4), 687-701. doi:10.1002/bit.2122

A novel metabolic-ASM model for full-scale biological nutrient removal systems

This study demonstrates that META-ASM, a new integrated metabolic activated sludge model, provides an overall platform to describe the activity of the key organisms and processes relevant to biological nutrient removal (BNR) systems with a robust single-set of default parameters. This model overcomes various shortcomings of existing enhanced biological phosphorous removal (EBPR) models studied over the last twenty years. The model has been tested against 34 data sets from enriched lab polyphosphate accumulating organism (PAO)-glycogen accumulating organism (GAO) cultures and experiments with full-scale sludge from five water resource recovery facilities (WRRFs) with two different process configurations: three stage Phoredox (A2/O) and adapted Biodenitro™ combined with a return sludge sidestream hydrolysis tank (RSS). Special attention is given to the operational conditions affecting the competition between PAOs and GAOs, capability of PAOs and GAOs to denitrify, metabolic shifts as a function of storage polymer concentrations, as well as the role of these polymers in endogenous processes and fermentation. The overall good correlations obtained between the predicted versus measured EBPR profiles from different data sets support that this new model, which is based on in-depth understanding of EBPR, reduces calibration efforts. On the other hand, the performance comparison between META-ASM and literature models demonstrates that existing literature models require extensive parameter changes and have limited predictive power, especially in the prediction of long-term EBPR performance. The development of such a model able to describe in detail the microbial and chemical transformations of BNR systems with minimal adjustment to parameters suggests that the META-ASM model is a powerful tool to predict and mitigate EBPR upsets, optimise EBPR performance and to evaluate new process designs