Productivity analysis and non-linear gain scheduling approach for multi-species bioprocesses with product inhibition

International audienceBioprocesses with product inhibition are known to allow species coexistence. In this work, we first study the productivity of the different possible equilibria, depending on the operating conditions, and show that single species offers the best performances. Then, we propose a control strategy to stabilize the dynamics about the desired equilibria, in presence of instability. Based on output feedback linearization, we propose a family of controllers and a gain-scheduling approach to adapt the controller. Finally, we illustrate our approach on numerical simulations, showing that the attraction basin of the closed-loop system is improved by considering the gain-scheduling approach

Perturbation-based Control of Industrial Fed-batch Bioprocesses

The topic of this thesis is bioprocess control, more specifically control of industrial-scale microbial fed-batch bioprocesses. Its focus is therefore on methods which are easy to implement in an industrial setting, which gives certain limitations on sensors, actuators and control systems. The main part of the work in the thesis concerns control of the microbial substrate uptake rate by manipulation of the feed rate of liquid substrate to the bioprocess. This is an important parameter for improving process yields, as too low feed rates cause starvation of the microorganisms while too high rates lead to production of undesirable by-products. By-product formation decreases metabolic efficiency and the by-products have inhibiting effects on microbial growth and production. At high concentrations these can even halt growth completely, leading to process failure. Due to large batch-to-batch variations and the complexity of the pro- cesses, model-based control can be difficult to use in this type of system. The approach used in this thesis circumvents this problem by utilizing perturbations in the feed rate. It has previously been shown that the metabolic state with regard to substrate uptake rate can be determined by analysing the perturbation response in the dissolved oxygen level of a microbial process. In this thesis, the concept is developed through the use of perturbations at a predefined frequency. This provides a number of advantages and allows for estimation of the metabolic state through observing the perturbation frequency in the measured signal. The concept has been tested experimentally in industrial pilot and pro- duction scale. It has been demonstrated that a controller based on this concept can be used to compensate for batch-to-batch variations in feed demand and can rapidly compensate for changes in the demand. It has also been shown that the method can be used for monitoring and control in bioprocesses with a volume over 100 m3, using a low-complexity estimation algorithm suited for industrial use. The thesis also concerns mid-ranging control in non-stationary processes. A modified mid-ranging controller suited for such processes is proposed, which allows control signals to increase in unison during the course of a fed-batch process while maintaining the advantages of classical mid-ranging control. The concept can for instance be used for control of dissolved oxygen, an important process parameter in many bioprocesses. It has been successfully used for this purpose in pilot scale alongside the type of perturbationbased feed rate controller which is the main topic of this thesis, also showing how the latter can be used in conjunction with other control systems

Bioprocess Monitoring and Control

Process monitoring and control are fundamental to all processes; this holds especially for bioprocesses, due to their complex nature. Usually, bioprocesses deal with living cells, which have their own regulatory systems. It helps to adjust the cell to its environmental condition. This must not be the optimal condition that the cell needs to produce whatever is desired. Therefore, a close monitoring of the cell and its environment is essential to provide optimal conditions for production. Without measurement, no information of the current process state is obtained. In this book, methods and techniques are provided for the monitoring and control of bioprocesses. From new developments for sensors, the application of spectroscopy and modelling approaches, the estimation and observer implementation for ethanol production and the development and scale-up of various bioprocesses and their closed loop control information are presented. The processes discussed here are very diverse. The major applications are cultivation processes, where microorganisms were grown, but also an incubation process of bird’s eggs, as well as an indoor climate control for humans, will be discussed. Altogether, in 12 chapters, nine original research papers and three reviews are presented

Development of advanced monitoring and control tools for rAAV production in the insect cell system

"Since the first publication introducing the concept in 1972, gene therapy has had a series of success stories and setbacks. However, the recent rise of awareness, public interest, promising results in clinical trials and recent market approvals indicate that gene therapy has come to stay. Currently there is a growing interest from the biopharmaceutical industry in gene and cell therapy, mostly using viral vectors. (...)

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

Process development for platform chemical production from agricultural and forestry residues

As part of a bio-based economy, biorefineries are envisaged to sustainably produce platform chemicals via biochemical conversion of agricultural and forestry residues. However, supply risks, the recalcitrance of lignocellulosic biomass, and inhibitor formation during pre\uadtreatment impair the economic feasibility of such biorefineries. In this thesis, process design and assessment were developed with the aim of addressing these hurdles and improving the cost-effectiveness of lignocellulose-derived platform chemicals.To expand the feedstock base and reduce operational costs, logging residues served as underutilised and inexpensive raw material. The major impediment in converting logging residues was their high recalcitrance and low cellulose content, which resulted in low attainable ethanol titres during simultaneous saccharification and co-fermentation (SSCF). Pretreatment optimisation reduced inhibitor formation and recalcitrance, and led to enzymatic hydrolysis yields at par with those obtained for stem wood, despite the less favourable chemical composition. Upgrading logging residues with carbohydrate-rich oat hulls increased ethanol titres to >50\ua0g/L using batch SSCF at 20% WIS loadings, demonstrating the potential to further decrease downstream processing costs. To alleviate the toxicity of inhibitors generated during pretreatment, preadaptation was applied to Saccharomyces cerevisiae. Exposure to the inhibitors in the pretreated liquid fraction improved ethanol production during subsequent fermentation. Transferring the concept of preadaptation to lactic acid production by Bacillus coagulans cut the process times by half and more than doubled the average specific lactic acid productivity, showcasing how preadaptation could decrease operational costs.To assess the performance and robustness of process designs against process input variations, a multi-scale variability analysis framework was developed. The framework included models for bioprocess, flowsheet, techno-economic, and life cycle assessment. In a case study, multi-feed processes, in which solids and cells are fed to the process using model-based predictions, were more robust against variable cellulolytic activities than batch SSCFs in a wheat straw-based ethanol biorefinery. The developed framework can be used to identify robust biorefinery process designs, which simultaneously meet technological, economic, and environmental goals

APPLICATION OF PROCESS SYSTEMS ENGINEERING TOOLS AND METHODS TO FERMENTATION-BASED BIOREFINERIES

Biofuels produced from lignocellulosic biomass via the fermentation platform are sustainable energy alternatives to fossil fuels. Process Systems Engineering (PSE) uses computer-based tools and methods to design, simulate and optimize processes. Application of PSE tools to the design of economic biorefinery processes requires the development of simulation approaches that can be integrated with existing, mature PSE tools used to optimize traditional refineries, such as Aspen Plus. Current unit operation models lack the ability to describe unsteady state fermentation processes, link unsteady state fermentation with in situ separations, and optimize these processes for competing factors (e.g., yield and productivity). This work applies a novel architecture of commercial PSE tools, Aspen Plus and MATLAB, to develop techniques to simulate time-dependent fermentation without and with in situ separations for process design, analyses and optimization of the operating conditions. Traditional batch fermentation simulations with in situ separations decouple these interdependent steps in a separate “steady state” reactor followed by an equilibrium separation of the final fermentation broth. A typical mechanistic system of ordinary differential equations (ODEs) describing a batch fermentation does not fit the standard built-in power law reaction kinetics model in Aspen Plus. To circumvent this challenge, a novel platform that links the batch reactor to a FORTRAN user kinetics subroutine (incorporates the ODEs) combined with component substitution (to simulate non-databank components) is utilized to simulate an unsteady state batch and in situ gas stripping process. The resulting model system predicts the product profile to be sensitive to the gas flow rate unlike previous “steady state” simulations. This demonstrates the importance of linking a time-dependent fermentation model to the fermentation environment for the design and analyses of fermentation processes. A novel platform linking the genetic algorithm multi-objective and single-objective optimizations in MATLAB to the unsteady state batch fermentation simulation in Aspen Plus through a component object module communication platform is utilized to optimize the operating conditions of a typical batch fermentation process. Two major contributions are: prior concentration of sugars from a typical lignocellulosic hydrolysate may be needed and with a higher initial sugar concentration, the fermentation process must be integrated with an in situ separation process to optimize the performance of fermentation processes. With this framework, fermentation experimentalists can use the full suite of PSE tools and methods to integrate biorefineries and refineries and as a decision-support tool to guide the design, analyses and optimization of fermentation-based biorefineries

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable

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