Comparisons of nonlinear estimators for wastewater treatment plants

This paper deals with five existing nonlinear estimators (filters), which include Extended Kalman Filter (EKF), Extended H-infinity Filter (EHF), State Dependent Filter (SDF), State Dependent H-Infinity Filter (SDHF) and Unscented Kalman Filter (UKF) that are formulated and implemented to estimate unmeasured states of a typical biological wastewater system. The performance of these five estimators of different complexities, behaviour and advantages are demonstrated and compared via nonlinear simulations. This study shows promising application of UKF for monitoring and control of the process variables, which are not directly measurable

Innovative Surveillance and Process Control in Water Resource Recovery Facilities

Water Resource Recovery Facilities (WRRF), previously known as Wastewater Treatment Plants (WWTP), are getting increasingly complex, with the incorporation of sludge processing and resource recovery technologies. Along with maintaining a stringent effluent water quality standard, the focus is gradually shifting towards energy-efficient operations and recovery of resources. The new objectives of the WRRF demand an economically optimal operation of processes that are subjected to extreme variations in flowrate and composition at the influent. The application of online monitoring, process control, and automation in WRRF has already shown a steady increase in the past decade. However, the advanced model-based optimal control strategies, implemented in most process industries, are less common in WRRF. The complex nature of biological processes, the unavailability of simplified process models, and a lack of cost-effective surveillance infrastructure have often hindered the implementation of advanced control strategies in WRRF. The ambition of this research is to implement and validate cost-efficient monitoring alternatives and advanced control strategies for WRRF by fully utilizing the powerful Internet of Things (IoT) and data science tools. The first step towards implementing an advanced control strategy is to ensure the availability of surveillance infrastructure for monitoring nutrient compositions in WRRF processes. In Paper A, a soft sensor, based on Extended Kalman Filter, is developed for estimating water-quality parameters in a Sequential Batch MBBR process using reliable and inexpensive online sensors. The model used in the soft sensor combines the mechanistic understanding of the nutrient removal process with a statistical correlation between nutrient composition and easy-to-measure parameters. Paper B demonstrates the universality of the soft sensor through validation tests conducted in a Continuous Multistage MBBR pilot plant. The drift in soft-sensor estimation caused by a mismatch between the mathematical model and process behavior is studied in Paper B. The robustness of the soft sensor is assessed by observing estimated nutrient composition values for a period of three months. A systematic method to calibrate the measurement model and update model parameters using data from periodic lab measurements are discussed in Paper B. The term SCADA has been ubiquitous while mentioning online monitoring and control strategy deployment in WRRFs. The present digital world of affordable communication hardware, compact single board processors, and high computational power presents several options for remote monitoring and deployment of soft sensors. In Paper C, a cost-effective IoT strategy is developed by using an open-source programming language and inexpensive hardware. The functionalities of the IoT infrastructure are demonstrated by using it to deploy a soft sensor script in the ContinuousMultistage MBBR pilot plant. A cost-comparison between the commercially available alternatives presented in Paper A and the open-source IoT strategy in Paper B and Paper C highlights the benefits of the new monitoring infrastructure. Lack of reliable control models have often been the cause for the poor performance of advanced control strategies, such as Model Predictive Controls (MPC) when implemented to complex biological nutrient removal processes. Paper D attempts to overcome the inadequacies of the linear prediction model by combining a recursive model parameter estimator with the linear MPC. The new MPC variant, called the adaptive MPC (AMPC), reduces the dependency of MPC on the accuracy of its prediction model. The performance of the AMPC is compared with that of a linear MPC, nonlinear MPC, and the traditional proportional-integral cascade control through simulator-based evaluations conducted on the Benchmark Simulator platform(BSM2). The advantages of AMPC compared to its counterparts, in terms of reducing the aeration energy, curtailing the number of effluent ammonia violations, and the use of computational resources, are highlighted in Paper D. The complex interdependencies between different processes in a WRRF pose a significant challenge in defining constant reference points for WRRFs operations. A strategy that decides control outputs based on economic parameters rather than maintaining a fixed reference set-point is introduced in Paper E. The model-based control strategy presented in Paper D is further improved by including economic parameters in the MPC’s objective function. The control strategy known as Economic MPC (EMPC) is implemented for optimal dosage of magnesium hydroxide in a struvite recovery unit installed in a WRRF. A comparative study performed on the BSM2 platform demonstrates a significant improvement in overall profitability for the EMPC when compared to a constant or a feed-forward flow proportional control strategy. The resilience of the EMPC strategy to variations in the market price of struvite is also presented in Paper E. A combination of cost-effective monitoring infrastructure and advanced control strategies using advanced IoTs and data science tools have been documented to overcome some of the critical problems encountered in WRRFs. The overall improvement in process efficiency, reduction in operating costs, an increase in resource recovery, and a substantial reduction in the price of online monitoring infrastructure contribute to the overall aim of transitioning WRRFs to a self-sustaining facility capable of generating value-added products.Water Resource Recovery Facilities (WRRF), tidligere kjent som avløpsrenseanlegg (WWTP), blir stadig mer komplekse ettersom flere prosess steg tillegges anleggene i form av slambehandling og ressursgjenvinningsteknologi. Foruten hovedmålet om å imøtekomme strenge avløpsvannskvalitetskrav, har anleggenes fokus gradvis skiftet mot energieffektiv drift og gjenvinning av ressurser. Slike nye mål krever økonomisk optimal drift av prosesser som er utsatt for ekstreme variasjoner i volum og sammensetning av tilløp. Bruk av online overvåking, prosesskontroll og automatisering i WRRF har jevnt økt det siste tiåret. Likevel er avanserte modellbaserte kontrollstrategier for optimalisering ikke vanlig i WRRF, i motsetning til de fleste prosessindustrier. Komplekse forhold i biologiske prosesser, mangel på tilgang til pålitelige prosessmodeller og mangel på kostnadseffektiv overvåkingsinfrastruktur har ofte hindret implementeringen av avanserte kontrollstrategier i WRRF. Ambisjonen med denne avhandlingen er å implementere og validere kostnadseffektive overvåkingsalternativer og avanserte kontrollstrategier somutnytter kraftige Internet of Things (IoT) og datavitenskapelige verktøy i WRRF sammenheng. Det første steget mot implementering av en avansert kontrollstrategi er å sørge for tilgjengelighet av overvåkingsinfrastruktur for måling av næringsstoffer i WRRF-prosesser. Paper A demonstrerer en virtuell sensor basert på et utvidet Kalman filter, utviklet for å estimere vannkvalitetsparametere i en sekvensiell batch MBBR prosess ved hjelp av pålitelige og rimelige online sensorer. Modellen som brukes i den virtuelle sensoren kombinerer en mekanistisk forståelse av prosessen for fjerning av næringsstoffer fra avløpsvann med et statistisk sammenheng mellom næringsstoffsammensetning i avløpsvann og parametere som er enkle å måle. Paper B demonstrerer det universale bruksaspektet til den virtuelle sensoren gjennom valideringstester utført i et kontinuerlig flertrinns MBBR pilotanlegg. Feilene i sensorens estimering forårsaket av uoverensstemmelse mellom den matematiske modellen og prosesseatferden er undersøkt i Paper B. Robustheten til den virtuelle sensoren ble vurdert ved å observere estimerte næringssammensetningsverdier i en periode på tre måneder. En systematisk metode for å kalibrere målemodellen og oppdatere modellparametere ved hjelp av data fra periodiske laboratoriemålinger er diskutert i Paper B. Begrepet SCADA har alltid vært til stede når online overvåking og kontrollstrategi innen WRRF er nevnt. Den nåværende digitale verdenen med god tilgjengelighet av rimelig kommunikasjonsmaskinvare, kompakte enkeltkortprosessorer og høy beregningskraft presenterer flere muligheter for fjernovervåking og implementering av virtuelle sensorer. Paper C viser til utvikling av en kostnadseffektiv IoT-strategi ved hjelp av et programmeringsspråk med åpen kildekode og rimelig maskinvare. Funksjonalitetene i IoT-infrastruktur demonstreres gjennom implementering av et virtuelt sensorprogram i et kontinuerlig flertrinns MBBR pilotanlegg. En kostnadssammenligning mellom de kommersielt tilgjengelige alternativene som presenteres i Paper A og åpen kildekode-IoT-strategi i Paper B og Paper C fremhever fordelene med den nye overvåkingsinfrastrukturen. Mangel på pålitelige kontrollmodeller har ofte vært årsaken til svake resultater i avanserte kontrollstrategier, som for eksempel Model Predictive Control (MPC) når de implementeres i komplekse biologiske prosesser for fjerning av næringsstoffer. Paper D prøver å løse manglene i MPC ved å kombinere en rekursiv modellparameterestimator med lineær MPC. Den nye MPC-varianten, kalt Adaptiv MPC (AMPC), reduserer MPCs avhengighet av nøyaktigheten i prediksjonsmodellen. Ytelsen til AMPC sammenlignes med ytelsen til en lineær MPC, ikke-lineær MPC og tradisjonell proportionalintegral kaskadekontroll gjennom simulatorbaserte evalueringer utført på Benchmark Simulator plattformen (BSM2). Fordelene med AMPC sammenlignet med de andre kontrollstrategiene er fremhevet i Paper D og demonstreres i sammenheng redusering av energibruk ved lufting i luftebasseng, samt redusering i antall brudd på utslippskrav for ammoniakk og bruk av beregningsressurser. De komplekse avhengighetene mellom forskjellige prosesser i en WRRF utgjør en betydelig utfordring når man skal definere konstante referansepunkter for WRRF under drift. En strategi som bestemmer kontrollsignaler basert på økonomiske parametere i stedet for å opprettholde et fast referansesettpunkt introduseres i Paper E. Den modellbaserte kontrollstrategien fra PaperDforbedres ytterligere ved å inkludere økonomiske parametere iMPCs objektiv funksjon. Denne kontrollstrategien kalles Economic MPC (EMPC) og er implementert for optimal dosering av magnesiumhydroksid i en struvit utvinningsenhet installert i en WRRF. En sammenligningsstudie utført på BSM2 plattformen viste en betydelig forbedring i den totale lønnsomheten ved bruk av EMPC sammenlignet med en konstant eller en flow proportional kontrollstrategi. Robustheten til EMPC-strategien for variasjoner i markedsprisen på struvit er også demonstrert i Paper E. En kombinasjon av kostnadseffektiv overvåkingsinfrastruktur og avanserte kontrollstrategier ved hjelp av avansert IoT og datavitenskapelige verktøy er brukt for å løse flere kritiske utfordringer i WRRF. Den samlede forbedringen i prosesseffektivitet, reduksjon i operasjonskostnader, økt ressursgjenvinning og en betydelig reduksjon i pris for online overvåkningsinfrastruktur bidrar til det overordnede målet om å gå over til bærekraftige WRRF som er i stand til å generere verdiskapende produkter.DOSCON A

Non-linear observability of activated sludge process models

The main contribution of this paper is to present a non-linear observability analysis method of Activated Sludge Models (ASM), which are used in many control applications. The objective is to reduce the unobservable ASM1 model to an observable one that can be used to implement advanced estimation algorithms. Local observability is achieved under certain operating conditions but failed at some points in the whole domain of definition. Furthermore, piece-wise observability rank test is also performed with three measurements and compared with non-linear observability. Simulation results are presented to demonstrate the proposed method. Copyright © 2005 IFA

Comparison of the Biodegradation of n-alkanes and Readily Biodegradable Substrates Using Open Mixed Culture under Aerobic, Anoxic and Anaerobic Conditions

Peer reviewedPublisher PD

Comparing and contrasting traditional membrane bioreactor models with novel ones based on time series analysis

Copyright @ 2013 MDPI AGThis article has been made available through the Brunel Open Access Publishing Fund.The computer modelling and simulation of wastewater treatment plant and their specific technologies, such as membrane bioreactors (MBRs), are becoming increasingly useful to consultant engineers when designing, upgrading, retrofitting, operating and controlling these plant. This research uses traditional phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative and novel time series models based upon input–output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are. Each situation usually proves unique.This article is made available through the Brunel Open Access Publishing Fund

Influence of Aluminum Ion on the Anaerobic Treatment of a Poultry Slaughterhouse Wastewater

The influence of Al 3+ on the anaerobic treatment of a poultry slaughterhouse wastewater was studied in this work. The soluble COD (SCOD), volatile acid (VA) concentrations, and methane yield values were measured and compared for zero, 15, and 40 ppm Al 3+ runs. Methane yields of 55.4, 144.2, and 215.4 ml CH4/g. COD for zero, 15, and 40 ppm Al 3+ concentrations, respectively, were observed. Furthermore, SCOD and VAs were not detectable in the reactor that was seeded with 40 ppm Al 3+. It was concluded that inhibitory effects of long chain fatty acids (LCFAs) on aceticlastic methanogens were reduced by aluminum ion. This conclusion was also corroborated by a new mathematical model for estimating the Monod parameters developed in this work. The main characteristic of this new model is that estimated parameters must satisfy some restrictions, which provides consistency for the estimated parameters

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

Modelling and Evaluation of Sequential Batch Reactor Using Artificial Neural Network

The main objective of wastewater treatment plant is to release safe effluent not only to human health but also to the natural environment. An aerobic granular sludge technology is used for nutrient removal of wastewater treatment process using sequential batch reactor system. The nature of the process is highly complex and nonlinear makes the prediction of biological treatment is difficult to achieve. To study the nonlinear dynamic of aerobic granular sludge, high temperature real data at 40˚C were used to model sequential batch reactor using artificial neural network. In this work, the radial basis function neural network for modelling of nutrient removal process was studied. The network was optimized with self-organizing radial basis function neural network which adjusted the network structure size during learning phase. Performance of both network were evaluated and compared and the simulation results showed that the best prediction of the model was given by self-organizing radial basis function neural network