An Evaluation of the Effect of Discharging a High Quality Effluent into a Small Ozark Mountain Stream

Recently the newly constructed Fayetteville wastewater treatment plant went on line and directed a portion of its discharge to a point in the Mud Creek drainage basin that had previously not received any effluent. Prior to the discharge, a background study had been performed to establish the water quality in the basin. The background data, when compared to the data collected by this study, allowed any alteration of the stream water quality to be evaluated. Also the modeling procedure used to set the effluent limits for the treatment plant was analyzed. All stream data were compared to the limits set forth for surface water quality by the Department of Pollution Control and Ecology. The new discharge had some effect on the receiving stream, however, the stream still meets Arkansas water quality standards for all parameters

Water Quality Modeling and Control in Recirculating Aquaculture Systems

Nowadays, modern aquaculture technologies are made in recirculating systems, which require the use of high-performance methods for the recirculated water treatment. The present chapter presents the results obtained by the authors in the field of modeling and control of wastewater treatment processes from intensive aquaculture systems. All the results were obtained on a pilot plant built for the fish intensive growth in recirculating regime located in “Dunarea de Jos” University from Galati. The pilot plant was designed to study the development of various fish species, starting with the less demanding species (e.g. carp, waller), or "difficult" species such as trout and sturgeons (beluga, sevruga, etc.)

Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model

[EN] Plant-wide modelling can be considered an appropriate approach to represent the current complexity in water resource recovery facilities, reproducing all known phenomena in the different process units. Nonetheless, novel processes and new treatment schemes are still being developed and need to be fully incorporated in these models. This work presents a short chronological overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model), illustrating the key role of general models (also known as supermodels) in the field of wastewater treatment, both for engineering and research.Seco, A.; Ruano, MV.; Ruiz-Martínez, A.; Robles Martínez, Á.; Barat, R.; Serralta Sevilla, J.; Ferrer, J. (2020). Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model. Water Science & Technology. 81(8):1700-1714. https://doi.org/10.2166/wst.2020.056S17001714818Barat, R., Montoya, T., Seco, A., & Ferrer, J. (2011). Modelling biological and chemically induced precipitation of calcium phosphate in enhanced biological phosphorus removal systems. Water Research, 45(12), 3744-3752. doi:10.1016/j.watres.2011.04.028Barat, R., Serralta, J., Ruano, M. V., Jiménez, E., Ribes, J., Seco, A., & Ferrer, J. (2013). Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants. Water Science and Technology, 67(7), 1481-1489. doi:10.2166/wst.2013.004Batstone, D. J., Hülsen, T., Mehta, C. M., & Keller, J. (2015). Platforms for energy and nutrient recovery from domestic wastewater: A review. Chemosphere, 140, 2-11. doi:10.1016/j.chemosphere.2014.10.021Borrás F. L. 2008 Técnicas microbiológicas aplicadas a la identificación y cuantificación de organismos presentes en sistemas EBPR (Microbiological Techniques Applied to Identification and Quantification of Organisms Present in EBPR Systems). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Claros, J., Jiménez, E., Aguado, D., Ferrer, J., Seco, A., & Serralta, J. (2013). Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor. Water Science and Technology, 67(11), 2587-2594. doi:10.2166/wst.2013.132Copp, J. B., Jeppsson, U., & Rosen, C. (2003). TOWARDS AN ASM1 – ADM1 STATE VARIABLE INTERFACE FOR PLANT-WIDE WASTEWATER TREATMENT MODELING. Proceedings of the Water Environment Federation, 2003(7), 498-510. doi:10.2175/193864703784641207Dorofeev, A. G., Nikolaev, Y. A., Kozlov, M. N., Kevbrina, M. V., Agarev, A. M., Kallistova, A. Y., & Pimenov, N. V. (2017). Modeling of anammox process with the biowin software suite. Applied Biochemistry and Microbiology, 53(1), 78-84. doi:10.1134/s0003683817010100Drewnowski, J., Zaborowska, E., & Hernandez De Vega, C. (2018). Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs. E3S Web of Conferences, 30, 03007. doi:10.1051/e3sconf/20183003007Durán F. 2013 Modelación matemática del tratamiento anaerobio de aguas residuales urbanas incluyendo las bacterias sulfatorreductoras. Aplicación a un biorreactor anaerobio de membranas (Mathematical Model of Urban Wastewater Anaerobic Treatment Including Sulphate Reducing Bacteria. Application to an Anaerobic Membrane Bioreactor). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Ekama, G. A. (2009). Using bioprocess stoichiometry to build a plant-wide mass balance based steady-state WWTP model. Water Research, 43(8), 2101-2120. doi:10.1016/j.watres.2009.01.036EPA 2006 User's manual version 4.03 2006. Available from: https://www.epa.gov/ceam/minteqa2-equilibrium-speciation-model (accessed July 2019).Fernández-Arévalo, T., Lizarralde, I., Fdz-Polanco, F., Pérez-Elvira, S. I., Garrido, J. M., Puig, S., … Ayesa, E. (2017). Quantitative assessment of energy and resource recovery in wastewater treatment plants based on plant-wide simulations. Water Research, 118, 272-288. doi:10.1016/j.watres.2017.04.001Ferrer, J., Seco, A., Serralta, J., Ribes, J., Manga, J., Asensi, E., … Llavador, F. (2008). DESASS: A software tool for designing, simulating and optimising WWTPs. Environmental Modelling & Software, 23(1), 19-26. doi:10.1016/j.envsoft.2007.04.005Ferrer J., Seco A., Ruano M. V., Ribes J., Serralta J., Gómez T., Robles A. 2011 LoDif BioControl® Control Software, Intellectual Property. Main Institution: Universitat de València; Universitat Politècnica de València.Flores-Alsina, X., Corominas, L., Snip, L., & Vanrolleghem, P. A. (2011). Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies. Water Research, 45(16), 4700-4710. doi:10.1016/j.watres.2011.04.040Flores-Alsina, X., Arnell, M., Amerlinck, Y., Corominas, L., Gernaey, K. V., Guo, L., … Jeppsson, U. (2014). Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of The Total Environment, 466-467, 616-624. doi:10.1016/j.scitotenv.2013.07.046Flores-Alsina, X., Kazadi Mbamba, C., Solon, K., Vrecko, D., Tait, S., Batstone, D. J., … Gernaey, K. V. (2015). A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models. Water Research, 85, 255-265. doi:10.1016/j.watres.2015.07.014Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems, 171, 16-25. doi:10.1016/j.chemolab.2017.09.021Grau, P., de Gracia, M., Vanrolleghem, P. A., & Ayesa, E. (2007). A new plant-wide modelling methodology for WWTPs. Water Research, 41(19), 4357-4372. doi:10.1016/j.watres.2007.06.019Grau, P., Copp, J., Vanrolleghem, P. A., Takács, I., & Ayesa, E. (2009). A comparative analysis of different approaches for integrated WWTP modelling. Water Science and Technology, 59(1), 141-147. doi:10.2166/wst.2009.589Henze M., Gujer W., Mino T., van Loosdrecht M. C. M. 2000 Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report No.9. IWA Publishing, London, UK.Jeppsson, U., & Pons, M.-N. (2004). The COST benchmark simulation model—current state and future perspective. Control Engineering Practice, 12(3), 299-304. doi:10.1016/j.conengprac.2003.07.001Jeppsson, U., Rosen, C., Alex, J., Copp, J., Gernaey, K. V., Pons, M.-N., & Vanrolleghem, P. A. (2006). Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science and Technology, 53(1), 287-295. doi:10.2166/wst.2006.031Ji, X., Liu, Y., Zhang, J., Huang, D., Zhou, P., & Zheng, Z. (2018). Development of model simulation based on BioWin and dynamic analyses on advanced nitrate nitrogen removal in deep bed denitrification filter. Bioprocess and Biosystems Engineering, 42(2), 199-212. doi:10.1007/s00449-018-2025-xJiménez, E., Giménez, J. B., Ruano, M. V., Ferrer, J., & Serralta, J. (2011). Effect of pH and nitrite concentration on nitrite oxidation rate. Bioresource Technology, 102(19), 8741-8747. doi:10.1016/j.biortech.2011.07.092Jiménez, E., Giménez, J. B., Seco, A., Ferrer, J., & Serralta, J. (2012). Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate. Bioresource Technology, 124, 478-484. doi:10.1016/j.biortech.2012.07.079Kazadi Mbamba, C., Flores-Alsina, X., John Batstone, D., & Tait, S. (2016). Validation of a plant-wide phosphorus modelling approach with minerals precipitation in a full-scale WWTP. Water Research, 100, 169-183. doi:10.1016/j.watres.2016.05.003Kazadi Mbamba, C., Lindblom, E., Flores-Alsina, X., Tait, S., Anderson, S., Saagi, R., … Jeppsson, U. (2019). Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems. Water Research, 155, 12-25. doi:10.1016/j.watres.2019.01.048Liu, Y., Peng, L., Ngo, H. H., Guo, W., Wang, D., Pan, Y., … Ni, B.-J. (2016). Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-Based Autotrophic Denitrification Processes. Environmental Science & Technology, 50(17), 9407-9415. doi:10.1021/acs.est.6b02202Lizarralde, I., Fernández-Arévalo, T., Brouckaert, C., Vanrolleghem, P., Ikumi, D. S., Ekama, G. A., … Grau, P. (2015). A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 74, 239-256. doi:10.1016/j.watres.2015.01.031Lizarralde, I., Fernández-Arévalo, T., Manas, A., Ayesa, E., & Grau, P. (2019). Model-based opti mization of phosphorus management strategies in Sur WWTP, Madrid. Water Research, 153, 39-52. doi:10.1016/j.watres.2018.12.056Maere, T., Verrecht, B., Moerenhout, S., Judd, S., & Nopens, I. (2011). BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 45(6), 2181-2190. doi:10.1016/j.watres.2011.01.006Mannina, G., Ekama, G., Caniani, D., Cosenza, A., Esposito, G., Gori, R., … Olsson, G. (2016). Greenhouse gases from wastewater treatment — A review of modelling tools. Science of The Total Environment, 551-552, 254-270. doi:10.1016/j.scitotenv.2016.01.163Martí, N., Barat, R., Seco, A., Pastor, L., & Bouzas, A. (2017). Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants. Journal of Environmental Management, 196, 340-346. doi:10.1016/j.jenvman.2016.12.074Moretti, P., Choubert, J.-M., Canler, J.-P., Buffière, P., Pétrimaux, O., & Lessard, P. (2017). Dynamic modeling of nitrogen removal for a three-stage integrated fixed-film activated sludge process treating municipal wastewater. Bioprocess and Biosystems Engineering, 41(2), 237-247. doi:10.1007/s00449-017-1862-3Nagy, J., Kaljunen, J., & Toth, A. J. (2019). Nitrogen recovery from wastewater and human urine with hydrophobic gas separation membrane: experiments and modelling. Chemical Papers, 73(8), 1903-1915. doi:10.1007/s11696-019-00740-xNewhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 157, 498-513. doi:10.1016/j.watres.2019.03.030Nopens, I., Batstone, D. J., Copp, J. B., Jeppsson, U., Volcke, E., Alex, J., & Vanrolleghem, P. A. (2009). An ASM/ADM model interface for dynamic plant-wide simulation. Water Research, 43(7), 1913-1923. doi:10.1016/j.watres.2009.01.012Nopens, I., Benedetti, L., Jeppsson, U., Pons, M.-N., Alex, J., Copp, J. B., … Vanrolleghem, P. A. (2010). Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. Water Science and Technology, 62(9), 1967-1974. doi:10.2166/wst.2010.044Ontiveros, G. A., & Campanella, E. A. (2013). Environmental performance of biological nutrient removal processes from a life cycle perspective. Bioresource Technology, 150, 506-512. doi:10.1016/j.biortech.2013.08.059Penya-Roja, J. M., Seco, A., Ferrer, J., & Serralta, J. (2002). Calibration and Validation of Activated Sludge Model No.2d for Spanish Municipal Wastewater. Environmental Technology, 23(8), 849-862. doi:10.1080/09593332308618360Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). A plant-wide energy model for wastewater treatment plants: application to anaerobic membrane bioreactor technology. Environmental Technology, 37(18), 2298-2315. doi:10.1080/09593330.2016.1148903Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). Economic and environmental sustainability of submerged anaerobic MBR-based (AnMBR-based) technology as compared to aerobic-based technologies for moderate-/high-loaded urban wastewater treatment. Journal of Environmental Management, 166, 45-54. doi:10.1016/j.jenvman.2015.10.004Rehman, U., Audenaert, W., Amerlinck, Y., Maere, T., Arnaldos, M., & Nopens, I. (2017). How well-mixed is well mixed? Hydrodynamic-biokinetic model integration in an aerated tank of a full-scale water resource recovery facility. Water Science and Technology, 76(8), 1950-1965. doi:10.2166/wst.2017.330Rieger L., Gillot S., Langergraber G., Ohtsuki T., Shaw A., Takacs I., Winkler S. 2012 Guidelines for Using Activated Sludge Models Scientific and Technical report No. 21. EWA Task Group on Good Modelling Practice. IWA Publishing Volume 11.Robles, A., Ruano, M. V., Ribes, J., Seco, A., & Ferrer, J. (2014). Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR). Journal of Membrane Science, 465, 14-26. doi:10.1016/j.memsci.2014.04.012Robles, A., Capson-Tojo, G., Ruano, M. V., Seco, A., & Ferrer, J. (2018). Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste. Waste Management, 80, 299-309. doi:10.1016/j.wasman.2018.09.031Ruano, M. V., Serralta, J., Ribes, J., Garcia-Usach, F., Bouzas, A., Barat, R., … Ferrer, J. (2012). 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Control-Oriented Modeling and Experimental Validation of a Deoiling Hydrocyclone System

As the treated water from offshore oil and gas production is discharged to the surrounding sea, there is an incentive to improve the performance of the offshore produced water treatment, to reduce the environmental pollutants to the sea. Regulations determine both the maximum allowed oil concentration and the total annual quantity. It is reasonable to assume that when better separation equipment or methods are developed, the regulation will become more strict, and force other producers to follow the trend towards zero harmful discharge. This paper develops and validates a hydrocyclone model to be used as a test-bed for improved control designs. The modeling methodology uses a combination of first-principles to define model structure and data-driven parameter identification. To evaluate and validate the separation performance, real-time fluorescence-based oil-in-water (OiW) concentration monitors, with dual redundancy, are installed and used on sidestreams of a modified pilot plant. The installed monitors measure the inlet and outlet OiW concentration of the tested hydrocyclone. The proposed control-oriented hydrocyclone model proved to be a reasonable candidate for predicting the hydrocyclone separation performance

An Investigation into Bromate Formation in Ozone Disinfection Systems

Ozonation is used as an alternative disinfection process to chlorination but unfortunately has a potential of oxidizing bromide, a natural component of water sources, to bromate. Bromate is a possible carcinogen with a maximum contaminant level of 10 ppb. To understand bromate formation in full-scale systems, a comprehensive study was conducted at the Moorhead Water Treatment Plant (WTP). Bromide concentrations in source waters were monitored. Water samples from locations in the ozonation chambers were collected and analyzed for bromate and other parameters. Results showed that bromate formation was increased through increases in pH, bromide, and ozone dose during high temperatures and was decreased by increases in organics. The impact of the bromate influential parameters was minimized at low temperatures. To assist Moorhead WTP on developing bromate control strategies, a modeling approach was adopted to predict bromate formation at various operational conditions using temperature, pH, ozone dose, bromide, and TOC.MWH Global, AWWA ScholarshipAmerican Water Works Association (AWWA), Minnesota and North Dakota sectionsNorth Dakota Water Resources Research InstituteDepartment of Civil Engineering, North Dakota State Universit

Development of Fuzzy Logic pH controller for pH Neutralization in Waste Water Treatment Plant (WWTP)

pH neutralization is one of the important processes in Wastewater Treatment Plant (WWTP). As WWTP used to treat eftluent and discharged water to public sewage, the right pH is important to save the enviromnent, safety, fulfill the rules and regulations and keep the plant equipment in safe condition. pH neutralization is well known fur highly non-linear characteristic with high sensitivity at the neutral region. Thus, pH neutralization is of the hardest parameter to be controlled in WWTP. From the literature review, current and most popular control strategy like PI controller produces certain range of errors. This is due to PI controller unable to compensate the non-linear characteristic of pH neutralization process. Advanced control strategy is believed to be the solution to control pH neutralization process in WWTP. Therefore, the objective of this project is to investigate and design advanced control strategy. A mathematical modeling is used to develop a plant model based on McAvoy Model. The plant model is obtained from previous researcher. From the validation of the mode~ the plant is accepted to be used throughout the project. Both conventional and advanced control strategy is developed. The development of controller is carried out in SIMULINK enviromnent. Evaluation of the controller performance is based on few parameters such as settling time, rise time and integral of absolute error. Based on the simulation results, Fuzzy Logic has given a excellent control performance compared to PI controller. Fuzzy logic is able to compensate any changes in high sensitivity in neutral region. In conclusion, advanced control strategy of Fuzzy Logic able to deal with high nonlinearity of pH neutralization process compared to conventional control strategy which is PI controller. The findings in this project will encourage other researcher to implement it fur other non-linear processes and being implemented in real time industry

A simple and efficient feedback control strategy for wastewater denitrification

Due to severe mathematical modeling and calibration difficulties open-loop feedforward control is mainly employed today for wastewater denitrification, which is a key ecological issue. In order to improve the resulting poor performances a new model-free control setting and its corresponding "intelligent" controller are introduced. The pitfall of regulating two output variables via a single input variable is overcome by introducing also an open-loop knowledge-based control deduced from the plant behavior. Several convincing computer simulations are presented and discussed.Comment: IFAC 2017 World Congress, Toulouse, Franc

Evaluating Cascading Impact of Attacks on Resilience of Industrial Control Systems: A Design-Centric Modeling Approach

A design-centric modeling approach was proposed to model the behaviour of the physical processes controlled by Industrial Control Systems (ICS) and study the cascading impact of data-oriented attacks. A threat model was used as input to guide the construction of the CPS model where control components which are within the adversary's intent and capabilities are extracted. The relevant control components are subsequently modeled together with their control dependencies and operational design specifications. The approach was demonstrated and validated on a water treatment testbed. Attacks were simulated on the testbed model where its resilience to attacks was evaluated using proposed metrics such as Impact Ratio and Time-to-Critical-State. From the analysis of the attacks, design strengths and weaknesses were identified and design improvements were recommended to increase the testbed's resilience to attacks