Dynamical Analysis and Robust Control Synthesis for Water Treatment Processes

Nowadays, water demand and water scarcity are very urgent issues due to population growth, drought and poor water quality all over the world. Therefore, water treatment plants are playing a vital role for good living condition of human. Water area needs more concentration study to increase water productivity and decrease water cost. This dissertation presents the analysis and control of water treatment plants using robust control techniques. The applied control algorithms include H∞, gain scheduled and observer-based loop-shaping control technique. They are modern control algorithms and very powerful in robust controlling of systems with uncertainties and disturbances. The water treatment plants include a desalination system and a wastewater process. Since fresh water scarcity is getting more serious, the desalination plants are to produce drinking water and wastewater treatment plants give the reusable water. The desalination system is a RO one used to produce drinking water from seawater and brackish water. The nonlinear behaviors of this system is carefully analyzed before the linearization. Due to the uncertainty caused by concentration polarization, the system is linearized using linear state-space parametric uncertainty framework. The system also suffer from many disturbances which water hammer is one of the most influential one. The mixed robust H∞ and μ-synthesis control algorithm is applied to control the RO system coping with large uncertainties, disturbances and noises. The wastewater treatment process is an activated sludge process. This biological process use microorganisms to convert organic and certain inorganic matter from wastewater into cell mass. The process is very complex with many coupled biological and chemical reactions. Due to the large variation in the influent flow, the system is modelized and linearized as a linear parametric varying system using affine parameter-dependent representation. Since the influent flow is quickly variable and easily to be measured, the robust gain scheduled robust controller is applied to deal with the large uncertainty caused by the scheduled parameter. This control algorithm often gives better performances than those of general robust H∞ one. In the wastewater treatment plant, there exist an anaerobic digestion, which is controlled by the observer-based loop-shaping algorithm. The simulations show that all the controllers can effectively deal with large uncertainties, disturbances and noises in water treatment plants. They help improve the system performances and safeties, save energy and reduce product water costs. The studies contribute some potential control approaches for water treatment plants, which is currently a very active research area in the world.Contents ······················································································· iv List of Tables ··············································································· viii List of Figures ··············································································· ix Chapter 1. Introduction ···································································· 1 1.1 Reverse osmosis process ···································································· 2 1.2 Activated sludge process ···································································· 6 1.3 Robust H∞ and gain scheduling control ··················································· 10 Chapter 2. Robust H∞ controller ······················································· 13 2.1 Introduction ·················································································· 13 2.2 Uncertainty modelling ······································································ 13 2.2.1 Unstructured uncertainties ···························································· 14 2.2.2 Parametric uncertainties ······························································· 15 2.2.3 Structured uncertainties ································································ 16 2.2.4 Linear fractional transformation ······················································ 16 2.3 Stability criterion ············································································ 17 2.3.1 Small gain theorem ····································································· 17 2.3.2 Structured singular value (muy) synthesis brief definition ·························· 19 2.4 Robustness analysis and controller design ··············································· 20 2.4.1 Forming generalised plant and N-delta structure ····································· 20 2.4.2 Robustness analysis ···································································· 24 2.5 Reduced controller ·········································································· 26 2.5.1 Truncation ··············································································· 27 2.5.2 Residualization ········································································· 29 2.5.3 Balanced realization···································································· 29 2.5.4 Optimal Hankel norm approximation ················································ 31 Chapter 3. Robust gain scheduling controller ······································· 37 3.1 Introduction ·················································································· 37 3.2 Linear parameter varying (LPV) system ·················································· 39 3.3 Matrix Polytope ·············································································· 40 3.4 Polytope and affine parameter-dependent representation ······························· 41 3.4.1 Polytope representation ································································ 41 3.4.2 Affine parameter-dependent representation ········································· 42 3.5 Quadratic stability of LPV systems and quadratic (robust) H∞ performance ········· 43 3.6 Robust gain scheduling ····································································· 44 3.6.1 LPV system linearization ······························································ 44 3.6.2 Polytope-based gain scheduling ······················································ 45 3.6.3 LFT-based gain scheduling ··························································· 48 Chapter 4. Mixed robust H∞ and μ-synthesis controller applied for a reverse osmosis desalination system ····························································· 52 4.1 RO principles ················································································ 52 4.1.1 Osmosis and reverse osmosis ························································· 52 4.1.2 Dead-end filtration and cross-flow filtration ········································ 53 4.2 Membranes ··················································································· 54 4.2.1 Structure and material ································································· 54 4.2.2 Hollow fine fiber membrane module ················································ 55 4.2.3 Spiral wound membrane module ····················································· 57 4.3 Nonlinear RO modelling and analysis ···················································· 58 4.3.1 RO system introduction ······························································· 58 4.3.2 Modelling ··············································································· 60 4.3.3 Nonlinear analysis ······································································ 62 4.3.4 Concentration polarization ···························································· 64 4.4 Water hammer phenomenon ······························································· 66 4.4.1 Water hammer, column separation and vaporous cavitation ······················ 66 4.4.2 Water hammer analysis and simulation ·············································· 69 4.4.3 Prevention of water hammer effect··················································· 78 4.5 RO linearization ············································································· 79 4.5.1 Nominal linearization ·································································· 79 4.5.2 Uncertainty modeling ·································································· 81 4.5.3 Parametric uncertainty linearization ················································· 83 4.6 Robust H∞ controller design for RO system ·············································· 85 4.6.1 Control of uncertain RO system ······················································ 85 4.6.2 Robustness analysis and H∞ controller design ······································ 86 4.7 Simulation result and discussion··························································· 90 4.8 Conclusion ··················································································· 95 Chapter 5. Robust gain scheduling control of activated sludge process ······· 96 5.1 Introduction about activated sludge process ············································· 96 5.1.1 State variables ·········································································· 98 5.1.2 ASM1 processes ······································································ 100 5.1.3 The control problem of activated sludge process ································· 102 5.2 System modelling ········································································· 104 5.3 Model linearization ········································································ 107 5.4 Robust gain-schedule controller design for activated sludge process ··············· 109 5.5 Simulation result and discussion························································· 115 5.6 Conclusion ················································································· 120 Chapter 6. Observer-based loop-shaping control of anaerobic digestion ···· 121 6.1 Introduction ················································································ 121 6.1.1 Control problem in anaerobic digestion ··········································· 122 6.2 System modelling ········································································· 123 6.3 Controller design ·········································································· 124 6.3.1 H∞ loop-shaping controller ························································· 125 6.3.2 Coprime factor uncertainty ·························································· 126 6.3.3 Control synthesis ····································································· 127 6.4 Simulation result ··········································································· 131 6.5 Conclusion ················································································· 133 Chapter 7. Conclusion ··································································· 134 References ·················································································· 136 Appendices ················································································· 144Docto

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Contribution à l’amélioration des performances d’un système de dessalement d’eau alimenté par une source photovoltaïque

Cette thèse présente une étude visant à améliorer l'efficacité d'un système de dessalement d’eau en utilisant une source photovoltaïque. Cette amélioration a été réalisée en appliquant l’ESC côté PV, la DTC 12 sectors côté motopompe et l'optimisation côté membrane dans des conditions climatiques saines et ombragées. L'étude a impliqué la conception et la mise en oeuvre d’un prototype de système de dessalement d'eau qui utilise une source d'énergie photovoltaïque. Les performances du système ont été évaluées en mesurant son taux de rejet de sel et sa consommation d'énergie spécifique. Les résultats ont montré que l'augmentation du débit d'eau saumâtre et l'amélioration des contrôles sur le côté PV et motopompe a considérablement amélioré les performances du système. L'étude a conclu que l'utilisation d'une source d'énergie photovoltaïque pour un système de dessalement de l'eau est une approche prometteuse pour répondre aux défis de la pénurie d'eau, en particulier dans les zones où l'électricité n'est pas facilement disponible. Cette approche peut réduire l'empreinte carbone associée aux méthodes traditionnelles de dessalement de l'eau et avoir un impact positif sur l'environnement. La recherche fournit des indications précieuses pour la conception et l'optimisation des systèmes de dessalement d'eau alimentés par l'énergie photovoltaïque, contribuant à la gestion durable des ressources en eau

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. Int J Hydrogen Energ 35:10710–10718Aguado D, Montoya T, Ferrer J, Seco A (2006) Relating ions concentration variations to conductivity variations in a sequencing batch reactor operated for enhanced biological phosphorus removal. Environ Modell Softw 21:845–851Aguilar-Garnica E, Dochain D, Alcaraz-González V, González-Álvarez V (2009) A multivariable control scheme in a two-stage anaerobic digestion system described by partial differential equations. J Process Contr 19:1324–1332Ahring BK, Angelidaki I, Johansen K (1992) Anaerobic treatment of manure together with industrial waste. Water Sci Technol 25:311–318Ajeej A, Thanikal JV, Narayanan CM, Senthil Kumar R (2015) An overview of bio augmentation of methane by anaerobic co-digestion of municipal sludge along with microalgae and waste paper. Renew Sustain Energy Rev 50:270–276Alcaraz-González V, González-Álvarez V (2007) Selected topics in dynamics and control of chemical and biological processes. Springer, BerlinAlcaraz-González V, Harmand J, Rapaport A, Steyer JP, González-Álvarez V, Pelayo-Ortiz C (2005a) Robust interval-based regulation for anaerobic digestion processes. Water Sci Technol 52:449–456Alcaraz-González V, Salazar-Peña R, González-Alvarez V, Gouzé JL, Steyer JP (2005b) A tunable multivariable nonlinear robust observer for biological systems. C R Biol 328:317–325Alferes J, Irizar I (2010) Combination of extremum-seeking algorithms with effective hydraulic handling of equalization tanks to control anaerobic digesters. Water Sci Technol 61:2825–2834Alferes J, García-Heras JL, Roca E, García C, Irizar I (2008) Integration of equalisation tanks within control strategies for anaerobic reactors. Validation based on ADM1 simulations. Water Sci Technol 57:747–752Alimahmoodi M, Mulligan CN (2008) Anaerobic bioconversion of carbon dioxide to biogas in an upflow anaerobic sludge blanket reactor. J Air Waste Manage Assoc 58:95–103Alvarez JA, Otero L, Lema JM (2010) A methodology for optimising feed composition for anaerobic co-digestion of agro-industrial wastes. Bioresour Technol 101:1153–1158Alvarez-Ramirez J, Meraz M, Monroy O, Velasco A (2002) Feedback control design for an anaerobic digestion process. J Chem Technol Biotechnol 77:725–734Anderson GK, Yang G (1992) Determination of bicarbonate and total volatile acid concentration in anaerobic digesters using a simple titration. Water Environ Res 64:53–59Andrews JF, Graef SP (1971) Dynamic modelling and simulation of the AD process. Advances in chemistry series no. 105, Anaerobic Biological Treatment Processes. American Chemical Society, Washington, DC, p 126Andrews JF, Pearson EA (1965) Kinetics and characteristics of volatile acid production in anaerobic fermentation processes. Air Water Pollut 9:439–461Angelidaki I, Sanders W (2004) Assessment of the anaerobic biodegradability of macropllutants. Rev Environ Sci Biotechnol 3:117–129Antila J, Tuohiniemi M, Rissanen A, Kantojärvi U, Lahti M, Viherkanto K, Kaarre M, Malinen J (2014) MEMS- and MOEMS-based near-infrared spectrometers. Encycl Anal Chem 1–36. doi: 10.1002/9780470027318.a9376Antoniades CD, Christofides P (2001) Integrating nonlinear output feedback control and optimal actuator/sensor placement for transport-reaction processes. Chem Eng Sci 56:4517–4535APHA (2005) American Public Health Association/American Water Works Association/Water Environmental Federation, Standard methods for the Examination of Water and Wastewater, 21st edn. Washington, DC, USAAppels L, Baeyens J, Degrève J, Dewil R (2008) Principles and potential of the anaerobic digestion of waste-activated sludge. Prog Energ Combust 34:755–781Appels L, Lauwers J, Gins G, Degreve J, Van Impe J, Dewil R (2011) Parameter identification and modeling of the biochemical methane potential of waste activated sludge. Environ Sci Technol 45:4173–4178Aquino SF, Chernicharo CAL, Soares H, Takemoto SY, Vazoller RF (2008) Methodologies for determining the bioavailability and biodegradability of sludges. Environ Technol 29:855–862Astals S, Esteban-Gutiérrez M, Fernández-Arévalo T, Aymerich E, García-Heras JL, Mata-Alvarez J (2013a) Anaerobic digestion of seven different sewage sludges: a biodegradability and modelling study. Water Res 47:6033–6043Astals S, Nolla-Ardèvol V, Mata-Alvarez J (2013b) Thermophilic co-digestion of pig manure and crude glycerol: process performance and digestate stability. J Biotechnol 166:97–104Babary JP, Julien S, Nihtilä MT et al (1999) New boundary conditions and adaptive control of fixed-bed bioreactors. Chem Eng Process Process Intensif 38:35–44Barat R, Serralta J, Ruano MV, Jiménez E, Ribes J, Seco A, Ferrer J (2012) Biological nutrient removal model No 2 (BNRM2): a general model for wastewater treatment plants. Water Sci Technol 67:1481–1489Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier Science, AmsterdamBatstone DJ (2013) Modelling and control in anaerobic digestion: achievements and challenges. 13th IWA World Congress on Anaerobic Digestion (AD 13), pp 1–6Batstone DJ, Keller J, Angelidaki I et al (2002) Anaerobic digestion model No. 1. (ADM1). IWA Scientific and Technical Report No. 13. IWABatstone DJ, Tait S, Starrenburg D (2009) Estimation of hydrolysis parameters in full-scale anaerobic digesters. Biotechnol Bioeng 102:1513–1520Batstone DJ, Amerlinck Y, Ekama G et al (2012) Towards a generalized physicochemical framework. Water Sci Technol 66:1147–1161Baumann WT, Rugh WJ (1986) Feedback control of nonlinear systems by extended linearization. IEEE Trans Automat Contr AC-31:40–46Benyahia B, Campillo F, Cherki B, Harmand J (2012) Particle filtring for the chemostat. In: MED’12, Barcelone, SpainBernard O (2011) Hurdles and challenges for modelling and control of microalgae for CO2 mitigation and biofuel production. J Process Control 21:1378–1389Bernard O, Gouzé JL (2004) Closed loop observers bundle for uncertain biotechnological models. J Process Control 14:765–774Bernard O, Hadj-Sadok Z, Dochain D et al (2001a) Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnol Bioeng 75:424–438Bernard O, Polit M, Hadj-Sadok Z, Pengov M, Dochain D, Estaben M, Labat P (2001b) Advanced monitoring and control of anaerobic wastewater treatment plants: software sensors and controllers for an anaerobic digester. Water Sci Technol 43:175–182Bernard O, Chachuat B, Hélias A, Rodriguez J (2005a) Can we assess the model complexity for a bioprocess? Theory and example of the anaerobic digestion process. Water Sci Technol 53:85–92Bernard O, Chachuat B, Hélias A, Le Dantec B, Sialve B, Steyer JP, Lavigne JF (2005b) An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet. Water Sci Technol 52:457–464Björnsson L, Hörnsten EG, Mattiasson B (2001a) Utilization of a palladium–metal oxide semiconductor (Pd-MOS) sensor for on-line monitoring of dissolved hydrogen in anaerobic digestion. Biotechnol Bioeng 73:35–43Björnsson L, Murto M, Jantsch TG, Mattiasson B (2001b) Evaluation of new methods for the monitoring of alkalinity, dissolved hydrogen and the microbial community in anaerobic digestion. Water Res 35:2833–2840Boe K (2006) Online monitoring and control of the biogas process. Technical University of DenmarkBoe K, Batstone D, Angelidaki I (2007) An innovative online VFA monitoring system for the anerobic process, based on headspace gas chromatography. Biotechnol Bioeng 96:712–721Boe K, Steyer JP, Angelidaki I (2008) Monitoring and control of the biogas process based on propionate concentration using online VFA measurement. Water Sci Technol 57:661–766Boe K, Batstone DJ, Steyer JP, Angelidaki I (2010) State indicators for monitoring the anaerobic digestion process. Water Res 44:5973–5980Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248–254Brinkmann K, Blaschke L, Polle A (2002) Comparison of different methods for lignin determination as a basis for calibration of near-infrared reflectance spectroscopy and implications of lignoproteins. J Chem Ecol 28:2483–2501Buendía IM, Fernández FJ, Villaseñor J, Rodríguez L (2008) Biodegradability of meat industry wastes under anaerobic and aerobic conditions. Water Res 42:3767–3774Buffiere P, Loisel D, Bernet N, Delgenes JP (2006) Towards new indicators for the prediction of solid waste anaerobic digestion properties. Water Sci Technol 53:233–241Cao Y, Pawlowski A (2012) Sewage sludge-to-energy approaches based on anaerobic digestion and pyrolysis: brief overview and energy efficiency assessment. Renew Sust Energ Rev 16:1657–1665Carballa M, Regueiro L, Lema JM (2015) Microbial management of anaerobic digestion: exploiting the microbiome-functionality nexus. Curr Opin Biotechnol 33:103–111Carlos-Hernandez S, Beteau JF, Sanchez EN (2007) Intelligent control strategy for an anaerobic fluidized bed reactor. In: Michel P (ed) Computer applications in biotechnology, vol 1. Cancun, Mexico, pp 73–78Carlos-Hernandez S, Sanchez EN, Bueno JA (2010) Neurofuzzy control strategy for an abattoir wastewater treatment process. In: Banga JR, Bogaerts P, Van Impe J, Dochain D, Smets I (eds) 11th International symposium on computer applications in biotechnology. Leuven, Belgium, pp 84–89Chandler JA, Jewell WJ, Gossett JM (1980) Predicting methane fermentation biodegradability. Biotechnol Bioeng Symp 10:93–107Chen YH (1990) Adaptive robust observers for non-linear uncertain systems. Int J Syst Sci 21:803–814Chen Y, Cheng JJ, Creamer KS (2008) Inhibition of anaerobic digestion process: a review. Bioresour Technol 99:4044–4064Chynoweth DP, Turick CE, Owens JM, Jerger DE, Peck MW (1993) Biochemical methane potential of biomass and waste feedstocks. Biomass Bioenerg 5:95–111Cirne DG, van der Zee FP, Fernandez-Polanco M, Fernandez-Polanco F (2008) Control of sulphide during anaerobic treatment of S-containing wastewaters by adding limited amounts of oxygen or nitrate. Rev Environ Sci Biotechnol 7:93–105Colombié S, Latrille E, Sablayrolles JM (2007) Online estimation of assimilable nitrogen by electrical conductivity measurement during alcoholic fermentation in enological conditions. J Biosci Bioeng 103:229–235Cord-Ruwisch R, Mercz TI, Hoh CY, Strong GE (1997) Dissolved hydrogen concentration as an on-line control parameter for the automated operation and optimization of anaerobic digesters. Biotechnol Bioeng 56:626–634Cossu R, Raga R (2008) Test methods for assessing the biological stability of biodegradable waste. Waste Manage 28:381–388Cresson R, Pommier S, Béline F et al (2014) Etude interlaboratoires pour l’harmonisation des protocoles de mesure du potentiel bio-méthanogène des matrices solides hétérogènes—Final report (in French) ADEMEDalmau J, Comas J, Rodríguez-Roda I, Pagilla K, Steyer JP (2010) Model development and simulation for predicting risk of foaming in anaerobic digestion systems. Bioresour Technol 101:4306–4314Davidsson A, Gruvberger C, Christensen TH, Hansen TL, Jansen J (2007) Methane yield in source-sorted organic fraction of municipal solid waste. Waste Manage 27:406–414De Baere L (2000) Anaerobic digestion of solid waste: state-of-the-art. Water Sci Technol 41:283–290De Baere L (2008) Partial stream digestion of residual municipal solid waste. Water Sci Technol 57:1073–1077De Gracia M, Grau P, Huete E et al (2009) New generic mathematical model for WWTP sludge digesters operating under aerobic and anaerobic conditions: model building and experimental verification. Water Res 43:4626–4642De Vrieze J, Verstraete W, Boon N (2013) Repeated pulse feeding induces functional stability in anaerobic digestion. Microb Biotechnol 6:414–424Delattre C, Dochain D, Winkin J (2004) Observability analysis of nonlinear tubular (bio)reactor models: a case study. J Process Control 14:661–669Di Pinto AC, Limoni N, Passino R, Rozzi A, Tomei MC (1990) Instrumentation, control and automation of water and wastewater treatment and transport systems. In: Proceedings of the 5th IAWPRC workshop, pp 51–58Díaz I, Pérez C, Alfaro N, Fdz-Polanco F (2015) A feasibility study on the bioconversion of CO2 and H2 to biomethane by gas sparging through polymeric membranes. Bioresour Technol 185:246–253Dochain D (2003) State and parameter estimation in chemical and biochemical processes: a tutorial. J Process Control 13:801–818Dochain D, Tali-Maamar N, Babary JP (1997) On modelling, monitoring and control of fixed bed bioreactors. Comput Chem Eng 21:1255–1266Dochain D, Perrier M, Guay M (2011) Extremum seeking control and its application to process and reaction systems: a survey. Math Comput Simulat 82:369–380Donoso-Bravo A, Garcia G, Pérez-Elvira S, Fernandez-Polanco F (2011) Initial rates technique as a procedure to predict the anaerobic digester operation. Biochem Eng J 53(3):275–280Doublet J, Boulanger A, Ponthieux A, Laroche C, Poitrenaud M, Cacho Rivero JA (2013) Predicting the biochemical methane potential of wide range of organic substrates by near infrared spectroscopy. Bioresour Technol 128:252–258Dreywood R (1946) Qualitative test for carbohydrate material. Industrial & Engineering Chemistry Analytical Edition. Am Chem Soc 18:499Dubois M, Gilles KA, Hamilton JK, Rebers PA, Smith F (1956) Colorimetric method for determination of sugars and related substances. Anal Chem 28:350–356Ekama GA, Sotemann SW, Wentzel MC (2007) Biodegradability of activated sludge organics under anaerobic conditions. Water Res 41:244–252Ellison WJ, Pedarros-Caubet F, Caubet R (2007) Automatic and rapid measurement of microbial suspension growth parameters: application to the evaluation of effector agents. J Rapid Meth Aut Mic 15:369–410Fang HHP (2012) Bioenergy production from waste and wastewater in China. In: Technical proceedings of the 2012 NSTI nanotechnology conference and expo, NSTI-nanotech 2012, pp 381–383Fannin KF, Chynoweth DP, Isaacson R (1987) Start-up, operation, stability, and control. Anaerob Dig Biomass 171–196Fdz-Polanco M, Díaz I, Pérez SI, Lopes AC, Fdz-Polanco F (2009a) Hydrogen sulphide removal in the anaerobic digestion of sludge by micro-aerobic processes: pilot plant experience. Water Sci Technol 60:3045–3050Fdz-Polanco M, Pérez-Elvira SI, Díaz I, García L, Torío R, Acevedo AF (2009b) Eliminación de H2S en digestión anaerobia de lodos por procesos microaerofílicos: experiencia en planta piloto. Tecnol del Agua 29:58–64Feitkenhauer H, von Sachs J, Meyer U (2002) On-line titration of volatile fatty acids for the process control of anaerobic digestion plants. Water Res 36:212–218Fernández YB, Soares A, Villa R, Vale P, Cartmell E (2014) Carbon capture and biogas enhancement by carbon dioxide enrichment of anaerobic digesters treating sewage sludge or food waste. Bioresour Technol 159:1–7Fountoulakis MS, Stamatelatou K, Lyberatos G (2008) The effect of pharmaceuticals on the kinetics of methanogenesis and acetogenesis. Bioresour Technol 99:7083–7090Francioso O, Rodriguez-Estrada MT, Montecchio D, Salomoni C, Caputo A, Palenzona D (2010) Chemical characterization of municipal wastewater sludges produced by two-phase anaerobic digestion for biogas production. J Hazard Mater 175:740–746Frigon JC, Roy C, Guiot SR (2012) Anaerobic co-digestion of dairy manure with mulched switchgrass for improvement of the methane yield. Bioprocess Biosyst Eng 35:341–349Frings CS, Dunn RT (1970) A colorimetric method for determination of total serum lipids based on the sulfo-phospho-vanillin reaction. Am J Clin Pathol 53:89–91Frølund B, Palmgren R, Keiding K, Nielsen PH (1996) Extraction of extracellular polymers from activated sludge using a cation exchange resin. Water Res 30:1749–1758Gaida D, Wolf C, Meyer C, Stuhlsatz A, Lippel J, Bäck T, Bongards M, McLoone S (2012) State estimation for anaerobic digesters using the ADM1. Water Sci Technol 66:1088–1095Ganesh R, Torrijos M, Sousbie P et al (2013) Anaerobic co-digestion of solid waste: effect of increasing organic loading rates and characterization of the solubilised organic matter. Bioresource Technol 130:559–569García-Diéguez C, Molina F, Roca E (2011) Multi-objective cascade controller for an anaerobic digester. Process Biochem 46:900–909García-Gen (2015) Modelling, optimisation and control of anaerobic co-digestion processes (2015), Ph.D. Thesis, Universidad de Santiago de Compostela, Departamento de Ingeniería QuímicaGarcía-Gen S, Sousbie P, Rangaraj G et al (2015) Kinetic modelling of anaerobic hydrolysis of solid wastes, including disintegration processes. Waste Manag 35:96–104Gauthier JP, Kupka IAK (1994) Observability and observers for nonlinear systems. SIAM J Control Optim 32:975–994Gauthier JP, Hammouri H, Othman S (1992) A simple observer for nonlinear systems applications to bioreactors. Autom Control IEEE Trans 37:875–880Ge H, Jensen PD, Batstone DJ (2011) Increased temperature in the thermophilic stage in temperature phased anaerobic digestion (TPAD) improves degradability of waste activated sludge. J Hazard Mater 187:355–361Gendron S, Perrier M, Barrett J, Legault N (1993) Adaptive control of brightness: the model weighting approach. Annual meeting—technical section, Canadian Pulp and Paper Association, Preprints. Publ by Canadian Pulp & Paper AssocGhosh S, Conrad JR, Klass DL (1975) Anaerobic acidogenesis of waste activated sludge, WPCF 47Goffaux G, Van de Wouwer A (2005) Bioprocess state estimation: some classical and less classical approaches. Springer, BerlinGornall AG, Bardawill CJ, David MM (1949) Determination of serum proteins by means of the biuret reaction. J Biochem Chem 177:751–766Gouzé JL, Rapaport A, Hadj-Sadok MZ (2000) Interval observers for uncertain biological systems. Ecol Model 133:45–56Grau P, de Gracia M, Vanrolleghem PA, Ayesa E (2007) A new plant-wide modelling methodology for WWTPs. Water Res 41:4357–4372Gregersen KH (2003) Økonomien i biogasfællesanlæg, Udvikling og status medio (2002) Report no. 150. Institute of Food and Resource Economic, Rolighedsvej 25, DK 1958, Frederiksberg C, DenmarkGrepmeier M (2002) Experimentelle Untersuchungen an einer zweistufigen fuzzy-geregelten anaeroben Abwasserreinigungsanlage mit neuartigem Festbettmaterial. TU MunichGuay M, Dochain D, Perrier M (2004) Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics. Automatica 40:881–888Gunaseelan VN (2007) Regression models of ultimate methane yields of fruits and vegetable solid wastes, sorghum and napiergrass on chemical composition. Bioresour Technol 98:1270–1277Gunaseelan VN (2009) Predicting ultimate methane yields of Jatropha curcus and Morus indica from their chemical composition. Bioresour Technol 100:3426–3429Guwy AJ, Hawkes FR, Wilcox SJ, Hawkes DL (1997) Neural network and on-off control of bicarbonate alkalinity in a fluidised-bed anaerobic digester. Water Res 31:2019–2025Guwy AJ, Dinsdale RM, Kim JR et al (2011) Fermentative biohydrogen production systems integration. Bioresour Technol 102:8534–8542Hao OJ (2003) Sulphate-reducing bacteria. In: Mara D, Horan N (eds) Handbook of water and wastewater microbiology. Academic Press Inc, London, pp 459–468Harremoës P, Capodaglio AG, H

Simulating Behavioral Microcystin Impairment in Fish

Fish experiencing blooms of the cyanobacteria genera Microcystis and Anabaena acquire microcystin and saxitoxin through ingestion of contaminated food and absorption of dissolved toxin. Even low chronic doses induce sensory and motor impairment—the impact of which is unquantified in wild populations. Here, I introduce Lagrangian particle models for cyanobacteria and fish which test the hypotheses that impairment symptoms suppress movement and growth. This is implemented within the Finite-Volume Coastal Ocean Model (FVCOM). Cyanobacteria particles move vertically according to mixing and buoyancy (a function of cellular reservoirs). Fish navigate the horizontal domain, foraging in high growth areas, and fleeing when toxin increases. The framework is demonstrated here for the case of juvenile fish encountering Microcystis aeruginosa in an idealized Louisiana estuary. Self-shading reduces bloom growth, and causes algae to collect at the surface. Turbulent diffusivity is insufficient to break up this layer, so dissolved toxin becomes surface-intensified. Fish seek high growth areas in this environment, and dietary uptake increases. This triggers flight and swimming impairment. As cyanobacteria excrete microcystin, absorption forces fish to become intoxicated even in areas of lower toxic risk. Repeated flight means fish spend more time in suboptimal areas, with final growth reduced up to 6.6%. In vivo, this would be exacerbated by physiological stress and the metabolic cost of toxin removal. Collective movement (group diffusivity) is suppressed nearly 50% during wide-spread intoxication. Simulations show that within a certain parameter space, both movement and growth are suppressed relative to the control case as expected. However, additional experiments resulted in higher growth, indicating the methods are sensitive to model parameterization. Ultimately, these are sandbox cases, which will require carefully-designed lab and field experiments before predictive capability can be assumed