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In recent years, bioderived ionic liquids have gained attention as a new promising approach for lignocellulosic biomass pretreatment. In this work, Agave tequilana bagasse (ATB), an attractive bioenergy feedstock in Mexico, was pretreated with a bioderived ionic liquid (cholinium lysinate) for the first time. Optimization of the pretreatment conditions, in-depth biomass characterization and methane generation via anaerobic digestion are the main contributions of this work. The results indicated optimized pretreatment conditions of 124 °C, 205 min and 20% solids loading by applying a central composite design. The optimized pretreated ATB was able to produce an elevated sugar yield of 51.4 g total sugars per g ATB due to their high delignification (45.4%) and changes in their chemical linkages although an increase in cellulose crystallinity was found (0.51 untreated vs. 0.62 pretreated). Finally, the mass balance showed that 38.2 kg glucose and 13.1 kg xylose were converted into 12.5 kg of methane per 100 kg of untreated ATB, representing 86% of the theoretical methane yield and evidencing the potential of this biorefinery scheme.

In this article, a mathematical microbial electrochemical cell (MEC) dynamic model is proposed. The model is derived from mass balances of a continuous flow MEC system which consists of two...

The regulation of nonlinear dynamical systems with both continuous and discrete measurements is addressed by a robust control structure that considers the compromise between safety, productivity and quality. This structure is a hybrid cascade control scheme composed of an inner loop which uses on-line or fast enough measurements to regulate a key variable and updates its reference at each sampling period according to the available discrete information of the outer loop. The cascade control scheme includes a reset observer to update the state vector at the intersampling periods, enhancing the robustness of the closed-loop in the face of parametric uncertainty and load changes. A bifurcation study was also carried out to determine the state and control bounds that guarantee the operation of the nonlinear system under safe and attainable conditions, making the saturation limits an instrumental part of the cascade control design to guarantee closed loop stability for systems with multiple stable and/or unstable open loop equilibrium. The performance of the proposed cascade control scheme is illustrated via numerical simulations in two extreme study cases: an open loop unstable chemical reactor and a highly sensitive bioprocesses used in high yield wastewater treatment.

Aims The purpose of this study was to apply cDNA approach for the characterization of active prokaryotic community to understand microbial scenarios and performance of an AnSBR digester fed with acid hydrolysates of Agave tequilana var. azul bagasse (ATAB). Methods and results The digester was implemented for methane production under organic loading rate (OLR) disturbances to correlate physicochemical variables with changes in abundance, diversity and population dynamics of active Bacteria and Archaea by principal components analysis (PCA). Results indicated that methane yield increased as well as active syntrophic relationships for interspecies hydrogen/formate (Anaerolinaceae-Methanobacterium beijingense) and acetate (Anaerolinaceae-Methanosaeta concilii) transfers at 8 g-COD l-1 day-1 . However, methane yield was negatively affected at 16 g-COD l-1 day-1 due to the competition for acetate by active Desulfovibrio marrakechensis and volatile fatty acids inhibition. Conclusions Microbial scenarios obtained by PCA correlations indicated that methane production from acid hydrolysates of ATAB was feasible at 8 g-COD l-1 day-1 . The digester operation at higher OLR only favoured methanogenesis by the hydrogenotrophic pathway. Significance and impact of the study Only cDNA analysis showed Archaea population dynamics, exhibiting high correlation with physicochemical variables towards the understanding of the methanogenic digester performance during OLR disturbances.

Anaerobic digestion (AD) is a powerful bioprocess used mainly in the treatment of highly polluted wastewaters with a valuable byproduct called biogas. However, the difficulties associated with the AD stable operation have prevented its worldwide application. The intrinsic complexity of the physicochemical and biological characteristics combined with the uncertain feedstock conditions, time lags and the lack of reliable measurements have hindered the application of a number of classical and advanced control strategies to deal with the control problem. On the other hand, robust H∞ theory offers a solid framework to handle feedback regulation of state variables on the plant frequency response in the face of disturbances and model uncertainties. This contribution presents a robust H∞ mixed sensitivity control design and tuning for output disturbances rejection and it is applied to an anaerobic digestion plant. This H∞ control holds the balance between robustness in the Chemical Oxygen Demand (COD) regulation and control effort. Simulation results show the effectiveness and benefits of the proposed design and tuning method. COD can effectively be regulated under: (i) load perturbations; (ii) model uncertainties and (iii) persistent output disturbances induced by COD measurement faults. The controller is applied to anaerobic digestion process but the approach can be implemented in distinct applications for organic matter degradation and other similar bioprocess and biological systems.

International audience; A multivariable adaptive feedback control for highly uncertain continuous anaerobic digestion processes is proposed to regulate the volatile fatty acids (VFA) concentration, the strong ions concentrations, and the total and intermediate alkalinities. The multivariable control scheme includes a Luenberger observer to estimate both the unmeasured variables (i.e., VFA) and unknown microbial growth kinetics. The control approach is designed using an exponential Lyapunov function to resemble the typical exponential biological growth of the involved microbial consortia. Taking into account physicochemical equilibrium, alkalinities are represented as a function of the state variables. As a result, the control problem becomes a regulation problem on alkalinities, and in turn, a tracking control problem on the state variables, with two manipulated variables—the dilution rate and the feed rate of a strong alkali solution—while the state variables’ set-points are given as a function of pH. The implementation of this multivariable control scheme was experimentally tested and validated in a 0.982 m3 pilot plant treating agro-industrial wastewater, and demonstrated to be robust in the face of unknown microbial growth kinetics. Results showed the potential for practical application and optimization of industrial digesters.

The distinctive feature of bioreactors used for wastewater treatment processes (WWTP) is the use of specialized microbial ecosystems as catalysts for the conversion processes. The concept is based on two advantages: its sustainable character and the possible diversification of their products in order to optimize both bioenergy and biomolecule production. These ideas match with a novel concept called “Environmental Biorefinery”, in which the main objective is to minimize the pollution rejected to the natural environment while getting as much as possible of high added-value products. One of the most studied processes in this context is anaerobic digestion (AD), whose main product until now is the so-called biogas, which is a mixture of methane (CH4) and carbon dioxide (CO2) with trace amounts of other gases such as hydrogen sulfide (H2S) and hydrogen (H2). In the past, most of industrial AD WWTP (digesters) were used for reaching minimum standards in the composition of the effluent, and eventually for maximizing biogas production. Nevertheless, the classical concept of anaerobic digester, as conventional wastewater treatment process or methane production process merely can now be overcome with highly innovative outputs. Nowadays, a fundamental research objective in all current variants of AD processes is the search for optimal operating conditions to maximize the productivity of biogas, as well as to optimize their performance as efficient biorefinery using Instrumentation, Control, and Automation approaches (ICA) for wastewater treatment. This paper aims to describe the most important model-based ICA approaches and to analyze their potential for increasing efficiency of AD process. Experimental and simulation results show that these control approaches are a useful, efficient, and low cost alternative that may be implemented at industrial scale to maximize the productivity of biogas.

This work addresses the hydrogen production from enzymatic hydrolysates of Agave tequilana bagasse and the valorization of the acidogenic effluent for methane production in anaerobic sequencing batch reactors (ASBRs). Regarding hydrogen production, the ASBR was operated at four organic loading rates (OLRs), which were modified by decreasing the cycle time (from 24 to 12 h) and increasing the COD concentration (from 8 to 12 and 16 g L−1). Results showed that the highest OLR promoted the highest hydrogen production rate of 25.2 ± 2.1 NmL L−1 h−1. Conversely, the hydrogen molar yield remained constant, obtaining similar values to the highest reported for lignocellulosic hydrolysates in continuous reactors (1.6H2-mol molconsumed sugar−1). Regarding methane production from the acidogenic effluent, an unexpected methane suppression was observed during the first 5 cycles of the ASBR operation. Such event was attributed to the disaggregation of the granular sludge due to the remaining hydrolytic activity of the enzymatic cocktail used for the hydrolysates production. This was corroborated by feeding acetate to an ASBR (positive control) and supplying the enzymatic cocktail. Overall, even though the ASBR configuration demonstrated its suitability for hydrogen production, further studies are needed to coupling the methanogenic phase in different reactor configurations.

BACKGROUND Pilot-scale studies focused on evaluating the robustness of biofilm-based anaerobic digestion processes for further application at full-scale are scarce. Therefore, the aim of this work was to evaluate the performance of a 445 L packed bed reactor (PBR) operated at different organic loading rates (OLRs between 4–12.5 g COD/L*d) for the treatment of tequila vinasses. The reactor performance was correlated with the microbial dynamics to elucidate the specific role of the microbial communities into the degradation pathways that governs the process. RESULTS The PBR was operated during 231 days under different OLRs showing a stable performance. The COD removal and methane yield were maintained throughout the reactor operation at 86-89% and 0.24-0.28 L CH4/g CODadded, respectively. Meanwhile, the highest volumetric methane production rate of 3.03 L CH4/d*L was reached at the highest OLR, 12.5 g COD/L*d. Regarding microbial dynamics, the Bacteria and Archaea populations were able to adapt to the OLR disturbances, favoring the interactions between syntrophic Bacteria and Methanosaeta at high OLRs. CONCLUSION This work contributes to the scarce information regarding anaerobic treatment of tequila vinasses at pilot-scale and demonstrates that PBR is a promising and robust configuration that allows treating higher OLRs than current reported technologies.

Previous works showed the positive effect of detoxification on fermentative hydrogen production using acid hydrolysates of agave bagasse in batch assays. However, the influence of detoxification on process stability and energy recovery through a subsequent methanogenesis stage that allows bringing this technology closer to full-scale applications has yet to be addressed. Therefore, this work aimed to compare the performance of detoxified and undetoxified agave bagasse acid hydrolysates for two-stage hydrogen and methane production in semi-continuous reactors. First, hydrogen was produced in an anaerobic sequencing batch reactor (AnSBR), achieving volumetric hydrogen production rates (VHPR) of 10 and 26.30 mL H2 L−1 h−1 for the undetoxified and detoxified hydrolysates, respectively. The energy recoveries (as hydrogen) were 0.02 kJ g−1-bagasse for the undetoxified hydrolysate and 0.18 kJ g−1-bagasse for the detoxified one. Secondly, the effluents from hydrogen production reactors were used as a substrate in methanogenic AnSBRs, where similar volumetric methane production rates (VMPR) and energy recoveries (as methane) were obtained for undetoxified (0.48 L-CH4 L−1 d−1 and 3.82 kJ g−1-bagasse) and detoxified (0.44 L-CH4 L−1 d−1 and 4.09 kJ g−1-bagasse) hydrolysates. Results demonstrated that detoxification considerably increases the overall process stability and hydrogen production. Conversely, the detoxification process did not significantly enhance methane production. Additionally, unlike the detoxified hydrolysates, the use of undetoxified hydrolysates showed similar global energy yields (H2 + CH4) with or without nutrients addition (3.84 vs. 3.21 kJ g−1-bagasse), pointing out that detoxification is only suggested for processes where hydrogen is the target product.

Sequential 2k factorial and central composite designs were used to optimize Agave tequilana bagasse (ATB) pretreatment by using 1-ethyl-3-methylimidazolium acetate ([Emim][OAc]). Reaction time, temperature and solids loading were the studied factors while sugar yield was the response variable. Results indicated that optimal conditions (119 °C, 142 min) using high solids loading (30%) were achieved at lower temperatures and reaction times than those previously reported in the literature. It was also revealed that solid recovery after pretreatment with [Emim][OAc] is a key factor. The increase in enzymatic digestibility of pretreated ATB was correlated to a decrease in crystallinity and lower lignin content as observed using microscopy techniques and weaken chemical bonds by Fourier transform infrared spectroscopy. Yields of glucose and xylose in the hydrolysate were 41.3, and 13.0 kg per 100 kg of untreated ATB, which are equivalent to glucan and xylan conversions of 75.9% and 82.9%, respectively.

The aim of this work was to compare the biohydrogen production potential of undetoxified and detoxified acid hydrolysates from A. tequilana bagasse. Detoxification was carried out with activated carbon at different concentrations and pH values. Results indicated that pH was not a significant variable, while the lowest evaluated concentration of activated carbon (1% p/v) significantly promoted the highest removal of acetic acid (89%) with minimal losses of fermentable sugars. Regarding dark fermentation experiments, central composite designs were used to optimize COD and pH variables for both substrates, undetoxified and detoxified hydrolysates (activated carbon 1% p/v and pH 0.6). At optimal conditions, the detoxified hydrolysate produced 33% more biohydrogen than the undetoxified one. Hydrogen molar yields were 1.71 and 1.23 mol H2/molsugar, respectively. This improvement was correlated to changes in metabolic byproducts, since the detoxified hydrolysate produced only acetic and butyric acids, while lactic acid was detected in the undetoxified hydrolysate.

A comparison study on the energy recovery from single and two-stage anaerobic digestion of Agave tequilana bagasse hydrolysates is presented in this contribution. Firstly, the A. tequilana bagasse was acid or enzymatically hydrolyzed and then both hydrolysates were digested at different concentrations (20–100% v/v) in batch reactors. Results showed that the two-stage anaerobic digestion outperformed the single-stage process. During the acidogenesis phase of the two-stage process, high hydrogen yields were reached with the enzymatic hydrolysate at a concentration of 40% (3.4 mol H 2 /mol hexose); whilst during methanogenic phase, the highest methane yield was obtained at a concentration of 20% for both hydrolysates (0.24 L CH 4 /g COD). The overall energy recovery analysis demonstrated that the two-stage process outperformed 3.3 times the single-stage process when comparing the highest energy recoveries obtained in each process. This is the first study that reveals such enhancement for the anaerobic digestion of lignocellulosic hydrolysates.

In this contribution, stability and passivity properties of a class of thermodynamic processes are addressed from a thermodynamical point of view. These thermodynamic processes can be constituted by multiple spatially homogeneous dynamic subsystems modeled by ordinary differential equations. It is shown that the internal entropy production may be used as a Lyapunov function candidate to prove the isolated system stability properties and as a storage function to assess the passivity properties when the system interacts with the surroundings. In addition, it is shown that the stability condition depends on a matrix whose dimension is equal to the number of modeled dynamical phenomena taking place within the system, i.e. the number of phenomena can be smaller than the system dimension. Moreover, a port-controlled Hamiltonian representation of this class of systems based on the internal entropy production is developed. Finally, the theory proposed is applied to three study cases: a heat exchanger, a ideal gas adiabatic chemical reactor and a ideal gas jacketed chemical reactor.

In this work, an experimental implementation of a model based output feedback (OF) scheme applied to regulate the volatile fatty acids concentration of an anaerobic digestion pilot plant is presented. The OF scheme was developed within a global nonlinear dynamics framework, resulting in an interlaced control-observer design in the light of passivity, observability, and bifurcation properties. From a industrial control perspective, the resulting OF scheme is a saturated PI controller with: (i) systematic construction and tuning and (ii) nonlocal robust stability conditions in terms of control gains and limits. Experimental results demonstrate the applicability, robustness and reliability of the OF control scheme in a real environment under several load disturbances, severe uncertainties and typical operational failures.

The saturated OF control problem for a class of four-state anaerobic digesters with volatile fatty acids (VFAs) measurement is addressed. The reactor must operate about an optimal steady-state, with maximum VFA consumption that is locally open-loop stable but not necessarily structurally stable. The problem is addressed as an interlaced control-observer design in the light of passivity, observability, and bifurcation properties. The result is a saturated linear PI controller with: (i) systematic construction and tuning and (ii) nonlocal robust stability conditions in terms of control gains and limits. The proposed approach is illustrated with a representative case example through numerical simulations, including a comparison with the switching control approach.

In this contribution, the stability and passivity properties of a class of chemical reactors are addressed from a thermodynamical point of view. For this purpose, a thermodynamical consistent model is derived from a generic gas reactor model whose rate is based on the reaction progress at the mesoscopic scale. It is shown that the internal entropy production may be used as a candidate Lyapunov function to prove the isolated system stability properties and as a storage function to emphasize the passivity properties when the chemical reactor interacts with the surroundings.

Agave tequilana bagasse is the main solid waste of the tequila manufacturing and represents an environmental issue as well as a potential feedstock for biofuel production due to its lignocellulosic composition and abundance. In this contribution, this feedstock was subjected to pretreatments with HCl and H 2 SO 4 for sugar recovery and methane was produced from the hydrolysates in batch and sequencing batch reactors (AnSBR). Sugar recovery was optimized by using central composite designs at different levels of temperature, acid concentration and hydrolysis time. Results showed that at optimal conditions, the HCl pretreatment induced higher sugar recoveries than the H 2 SO 4 one, 0.39 vs. 0.26 g total sugars/g bagasse. Furthermore, the H 2 SO 4 hydrolysate contained higher concentrations of potential inhibitory compounds (furans and acetic acid). Subsequent anaerobic batch assays demonstrated that the HCl hydrolysate is a more suitable substrate for methane production; a four-fold increase was found. A second optimization by using HCl as acid catalyst and methane production as the response variable demonstrated that softer hydrolysis conditions are required to optimize methane production as compared to sugar recovery (1.8% HCl, 119 °C and 103min vs. 1.9% HCl, 130 °C and 133min). This softer conditions were used to feed an AnSBR for 110 days and evaluate its stability at three different cycle times (5, 3 and 2 days). Results showed stable reactor performances at cycle times of 5 and 3 days, obtaining the highest methane yield and production at 3 days, 0.28 NL CH 4 /g-COD and 1.04 NL CH 4 /d respectively. Operation at shorter cycle times is not advised due to microbial imbalance.

By combining nonlinear control theory and constructive analysis, we address the control of dynamical systems with a cascade structure where the first subsystem is connected in series with the second subsystem, while the input acts in parallel on both subsystems, which may exhibit multiple steady states with bistable behavior, typically undesired in practice due to safety or operational conditions. The resulting regulation scheme is a cascade control structure which stabilizes the second subsystem within a desired region of the state space while guaranteeing the output regulation in the first subsystem. The performance of the proposed control is evaluated and compared to nonlinear single-input-single-output controllers in a simulation example of a chemical reaction taking place in two reactors connected in series.

This study examines the performance of an anaerobic fixed-film bioreactor under seasonal operating conditions prevailing in medium and small size Tequila factories: start-up, normal operation and particularly, during the restart-up after a long stop and starvation period. The proposed start-up procedure attained a stable biofilm in a rather short period (28 days) despite unbalanced COD/N/P ratio and the use of non-acclimated inoculum. The bioreactor was restarted-up after being shut down for 6 months during which the inoculum starved. Even when biofilm detachment and bioreactor clogging were detected at the very beginning of restart-up, results show that the bioreactor performed better as higher COD removal and methane yield were attained. CE-SSCP and Q-PCR analyses, conducted on the biofilm prokaryotic communities for each operating condition, confirmed that the high COD removal results after the bioreactor clogging and the severe starvation period were mainly due to the stable archaeal and resilient bacterial populations.

An Asymptotic Observer (AO) is proposed to analyze and to study biochemical transient-state systems. In agreement with a rigorous conservation analysis, the AO is able to estimate unmeasured internal metabolite concentrations without knowledge of the kinetic model and the initial condition of the state variables of the metabolic network. The AO methodology is applied in the pentose phosphate pathway of Saccharomyces cerevisiae. Results demonstrated good accuracy and stability properties of AO for rebuilding the unknown metabolic information under transient conditions of biochemical networks. According to its features, AO could become a useful tool to better understand biochemical systems in dynamic state.

An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.

In this paper, a multiple-input multiple-output (MIMO) model-based robust control scheme is proposed for the indirect control of both total alkalinity and the ratio (intermediate alkalinity)/(total alkalinity) by regulating volatile fatty acid concentrations and strong ions concentration, while guaranteeing the so-called operational stability in anaerobic digestion (AD) processes. The proposed MIMO regulator is an adaptive controller derived from an AD model which incorporates the physicochemical equilibrium of the system as well as the use of a robust interval observer to estimate key process bounds that are used in the computation of the control efforts. Numerical simulations were carried out for a number of operating conditions under the most uncertain scenarios. Results showed that the proposed multivariable control law is able to recover the system stability around a pre-determined set point in the face of parameter uncertainty and load disturbances.

A Virtually Controlled Observer (VCO) was proposed to estimate both, the unmeasured states and the unknown inputs (influent substrate concentrations: Chemical Oxygen Demand (DCO) and Volatile Fatty Acids (VFA) in a 1 m3 up-flow fixed-bed anaerobic digestion (AD) pilot plant. The VCO is developed without the full knowledge of the AD kinetics and consists of an asymptotic observer and an output feedback controller, in which, one of the observer inputs (the hypothetical -unmeasured- influent substrate concentration) is updated by a feedback control law, which is devised to regulate the estimation error of a measured output. Results show that the proposed observer is robust in the face model mismatch, load and operating conditions changes and sensor faults as it was able to reconstruct the influent substrate concentration.

A robust state estimation scheme is proposed for anaerobic digestion (AD) processes to estimate key variables under the most uncertain scenarios (namely, uncertainties on the process inputs and unknown reaction and specific growth rates). This scheme combines the use of the IWA Anaerobic Digestion Model No. 1 (ADM1), the interval observer theory and a minimum number of measurements to reconstruct the unmeasured process variables within guaranteed lower and upper bounds in which they evolve. The performance of this robust estimation scheme is evaluated via numerical simulations that are carried out under actual operating conditions. It is shown that under some structural and operational conditions, the proposed robust interval observer (RIO) has the property of remaining stable in the face of uncertain process inputs, badly known kinetics and load disturbances. It is also shown that the RIO is indeed a powerful tool for the estimation of biomass (composed of seven different species) from a minimum number of measurements in a system with a total of 32 variables from which 24 correspond to state variables.

International audience; Anaerobic digestion (AD) is a highly nonlinear time-varying process commonly used for biological wastewater treatment, which is subject to large disturbances of both influent concentrations, and flow rates that may lead the process to a breakdown. In order to compensate the effect of these disturbances, the dynamics of the main state variables - including biomass - must be closely monitored and used to improve the process performance. However, AD processes still suffer from a lack of reliable and cheap sensors of key process variables to insure the right process operation. This has led to the development of estimation schemes, which infer the information of such key variables from the available measurements. Nevertheless, reliable measurements are not always possible to get because these readings may be corrupted by noise or erroneous due to sensor failures and as a consequence, they may lead to deteriorated control efforts and the eventual crash of the AD process. In this article, we propose an integrated system for the detection, isolation, and analysis of faults in AD processes by using interval observers (IO). The proposed approach was experimentally implemented on a 1-m(3) pilot scale anaerobic digester. Based on the comparison between the measured outputs and their corresponding estimates, results show that this approach was able to detect sensor failures as well as faults in the basic hypotheses made during the design step.

The saturated Output-Feedback (OF) control problem for a class of four-state anaerobic digestors with volatile fatty acids (VFAs) measurement is addressed. The reactor must operate about an optimal steady-state, with maximum VFA consumption, that is locally stable but not structurally unstable. The problem is addressed as an interlaced control-observer design within a geometric control framework in the light of passivity, observability, and bifurcation properties. The result is a saturated linear PI OF controller with global robust stability conditions in terms of control gains and limits. The proposed approach is illustrated with a representative case example through numerical simulations.

In this paper, the regulation problem of convection governed dynamical processes described by linear distributed parameter models with position-independent inputs is addressed by combining the method of characteristics (MC) and the Malek-Zavarei and Jamshidi theorem. In this approach, the MC is used to transform the first order partial differential equations model of such processes into a set of time-delayed ordinary differential equations whose elements are used to generate certain matrices that satisfy linear matrix inequalities that render the proportional integral (PI) control parameters that guarantee the process stability. The performance of the proposed PI controller is tested via numerical simulations in the temperature regulation of a linear heat exchanger and compared to a tightly tuned classical PI controller. The application of the proposed controller is extended to regulate the outlet concentration in a nonlinear non-isothermal plug flow reactor. It is shown that the proposed controller is able to handle the infinite dimensional nature of the process model yielding smooth and unsaturated responses around the predetermined set-point trajectory in the face of load changes, time varying boundary conditions and set-point changes.

In this paper, the robust regulation of volatile fatty acids (VFA) and total alkalinity (TA) is addressed to improve the stability of continuous anaerobic digestion (AD) processes. The control scheme is derived from an AD model that includes TA as state variable and it is composed of a model-based multiple-input multiple-output feedback control and an extended Luenberger observer which not only allows the estimation of the process kinetics and variations in the influent composition but also the introduction of an antiwindup structure. The wastewater flow rate is used to regulate the VFA concentration, whereas an alkali solution is added directly to the digester to regulate TA. The controller performance is evaluated via numerical simulations showing excellent responses under the influence of control input saturations, noisy measurements, load disturbances and uncertain kinetics. Finally, it is shown that well-known practical stability criteria for AD processes can be also fulfilled by the proposed control scheme.

In this paper, a nonlinear robust regulator, based on fuzzy techniques, is proposed as an alternative procedure design to guarantee stability for a wider range of variation in both the exosystem and the immersion. This robust regulator is a fuzzy error feedback controller which relies on the existence of an internal model, obtained by finding, if possible, an observable immersion of the exosystem dynamics, which allows to generate all the possible steady state inputs for the admissible values of the system parameters in order to track and/or reject time-varying references and/or disturbances, while maintaining the stability conditions of the system. Finally, the proposed fuzzy controller scheme is compared with traditional techniques through a case study to test the robustness properties in the face of parameter uncertainties and changes on load disturbances.

In this paper a “virtually controlled observer” (VCO) is proposed to estimate simultaneously the influent substrate concentration and unmeasured state variables in continuous anaerobic digestion processes. The hypothetical (unmeasured) influent substrate concentration is updated by a feedback control, which is regulated by the estimation error of a measured output. The proposed approach is firstly illustrated upon a simple continuous bioprocess. Then, the performance of the observer is tested via numerical simulations in a continuous anaerobic digestion model. Results showed that it is possible to estimate both, the influent substrate concentration and unmeasured states in the digester in the face of parametric uncertainties.

The surface functionalization process was accomplished in a consecutive 3 step process including: (1) Argon- and oxygen-plasma enhanced generation of free radical sites on cellophane surfaces; (2) “In situ” gas phase derivatization in the absence of plasma using hydrazine, ethylene diamine, or propylene diamine; (3) Second “in situ”, gas phase derivatization in the absence of plasma using oxallyl chloride or “ex situ” derivatization in the presence of glutaraldehyde. The presence of free radical sites on the plasma exposed cellophane surfaces was demonstrated using “in situ” sulfur dioxide and nitric oxide labeling techniques. It was shown that the free radical sites readily react under “in situ” conditions with the stable chain-precursor components and generate the desired spacer-chain molecules. ESCA, ATR-FTIR analysis and dying techniques were used to monitor the cellophane surface changes. A factorial design was used for selecting the optimal plasma parameters. Functionalized cellophane substrates were used for immobilization of α-chymotrypsin in the presence of spacer-chain molecules. The activity of the immobilized α-chymotrypsin was found to be lower in comparison to the activity of the free enzyme and the presence of virgin cellophane in the free enzyme solution also reduced significantly the activity of the enzyme. It is suggested that the swollen state of the cellophane plays a significant role in the decrease of the immobilized enzyme activity.

Trajectory tracking or rejecting persistent disturbances with digital controllers in nonlinear processes is a class of problems where classical control methods breakdown since it is very difficult to describe the dynamic behavior over the entire trajectory. In this paper, a model-based robust control scheme is proposed as a potential solution approach for these systems. The proposed control algorithm is a robust error feedback controller that allows us to track predetermined operation profiles while attenuating the disturbances and maintaining the stability conditions of the nonlinear processes. Various numerical simulation examples demonstrate the effectiveness of this robust scheme. Two examples deal with effective trajectory tracking in chemical reactors over a wide range of operating conditions. The third example analyses the attenuation of periodic load in a biological reactor. All examples illustrate the ability of the robust control scheme to provide good control in the face of parameter uncertainties and load disturbances.

The effect of different glucose concentrations (10–100 g/l) on succinic acid production by Actinobacillus succinogenes was studied. The maximum succinic acid concentration obtained was 33.8 g/l from 54.7 g/l of glucose. Productivities, conversion yields, and specific rates decreased when the amount of initial glucose was increased. The growth stopped with 22 g/l of acid mixture produced (succinic, formic, and acetic acids) and succinic acid production stopped with 45 g/l of acid mixture produced. These results point to a double inhibitory effect by glucose and products on growth and succinic acid production. The inhibition phenomenon was adequately described by Jerusalimsky equations for specific rate of growth and specific rate of succinic acid production. These expressions helped to quantify substrate and product inhibition effects.

International audience; This paper is focused on the experimental implementation of a robust control scheme for the regulation of volatile fatty acids (VFA) in continuous anaerobic digestion processes. The robust scheme is made of an output feedback control, and an extended Luenberger observer is used to estimate the uncertain terms of the process (i.e., influent concentration and process kinetics). The control scheme is implemented in a pilot plant up-flow fixed-bed reactor that is treating industrial wine distillery wastewater. The performance of the robust scheme is tested over a period of 36 days, under different set-point values and several uncertain scenarios, including model mismatch, badly known parameters, and load disturbances. Experimental results show that the VFA concentration can be effectively regulated over a wide range of operating conditions. In addition, it is shown that the control scheme has a structure that improves its performance in the presence of noisy measurements and control input saturations.

In this work a robust nonlinear scheme is proposed to control spatially distributed convective systems described by first-order hyperbolic partial differential equations by manipulating the flow velocity. The proposed scheme is designed after the method of characteristics is used to establish key structural properties of the system dynamics. The resulting feedback control, which can be seen as a proportional integral controller with variable integration time, does not require measurements for several axial points nor infinite dimensional state estimations. The proposed controller is applied successfully to two heat exchange simulation examples and a nonisothermical plug flow reactor. It is shown that it is robust in the face of uncertain parameters and load disturbances. Finally, the performance of the robust controller is compared to other control applications.

International audience; A new robust state estimation scheme is proposed for chemical and biochemical processes. This scheme is a nonlinear interval observer which is robust in the face of incertainties of the inputs, load disturbances and the nonlinearity of the system, while maintaining excellent stability conditions. We show that this observer yields satisfactory estimates of the unmeasured states whithin a guaranteed interval determined by the available measurements and some reasonable hypotheses about the observer inputs. For demonstrating the effectiveness and applicability of the proposed estimation scheme, we apply it in an actual anaerobic digestion pilot plant for wastewater treatment in the presence of uncertainties including measuring errors, sensor failures, time varying inputs and unmeasured disturbances. On-line implementation results reveal reveal that the proposed robust nonlinear interval observer is a practical and encouraging approach to the robust state estimation in uncertain nonlinear processes.

Anaerobic digestion is a highly nonlinear time-varying process used for biological wastewater treatment which is subject to large disturbances of both influent concentrations and flow rates. These perturbations can lead to the crash of the digester and thus, the dynamics of the main state variables - including biomass - must be closely monitored to use this information in the design and implementation of advanced control schemes. However, such processes still suffer From a lack of reliable and cheap sensors. As a consequence, efficient monitoring, control and decision support systems are needed in order to insure the correct process operation. Particularly, there is an increasing interest on the proposal of Fault Detection and Isolation (FDI) and Fault Detection and Analysis (FDA) integrated systems. In this paper, we propose the use of interval observers in order to detect and isolate sensor faults as well as input changes in biological systems that are not observable. This approach is experimentally implemented on a 1 m 3 pilot scale anaerobic digestion continuous process.

Atmospheric dielectric barrier discharge (DBD) treatments of wood were done to attain water repellency on wood surfaces. A specially designed frequency controlled parallel-plate DBD reactor was utilized to produce the discharges. Ethylene, methane, chlorotrifluoroethylene and hexafluoropropylene were used as DBD reagents. Contact angle, water absorption, X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) measurements on the modified surfaces were performed. For methane and ethylene, XPS data showed an increased surface atomic concentration of carbon from 72.7% on untreated samples up to 80.7 and 96%, respectively, whereas nearly 50% fluorine concentration was observed with fluorinated reagents. The C-1s spectrum of hexafluoropropylene-DBD-treated wood sample showed that the CF3 group was introduced in a relative amount of 19%. AFM images showed distinct features for each of the DBD treatments, such as a deposit of a thin uniform film in the case of ethylene-DBD treatment, whereas the hexafluoropropylene-DBD treatment resulted in the nucleation of plasma-derived entities at the fiber surface and the subsequent growth of a film. Under optimized conditions the water contact angle was in the range of 139 degrees- 145 degrees. The combination of depositing a low surface energy polymer on an already rough surface gave the surface-treated wood a highly hydrophobic character.

This paper deals with the simultaneous regulation of volatile fatty acids (VFA's) and total alkalinity (TA) in anaerobic digesters. The control scheme is conformed by an output feedback control and an extended Luenberger observer used to estimate the uncertainties associated to the controlled states ( i.e. , kinetics terms and inlet composition). The inlet flow rate is used to regulate the VFA's concentration, whereas an alkali solution is added directly to the digester for the regulation of the TA concentration. The control scheme is evaluated via numerical simulations under different operating conditions. Results show that the control law is capable to regulate the VFA's and TA despite of load disturbances, uncertainties in the kinetics terms, noisy measurements and control inputs restrictions.

In recent years, anaerobic digestion has been recognized as a suitable alternative for tequila vinasses treatment due to its high energy recovery and chemical oxygen demand (COD) removal efficiency. However, key factors such as the lack of suitable monitoring schemes and the presence of load disturbances, which may induce unstable operating conditions in continuous systems, have limited its application at full scale. Therefore, the aim of this work was to evaluate the anaerobic sequencing batch reactor (AnSBR) configuration in order to provide a low cost and easy operation alternative for the treatment of these complex effluents. In particular, the AnSBR was evaluated under different pH–temperature combinations: 7 and 32 °C; 7 and 38 °C; 8 and 32 °C and 8 and 38 °C. Results showed that the AnSBR configuration was able to achieve high COD removal efficiencies (around 85%) for all the tested conditions, while the highest methane yield was obtained at pH 7 and 38 °C (0.29 L/g COD added). Furthermore, high robustness was found in all the AnSBR experiments. Therefore, the full-scale application of the AnSBR technology for the treatment of tequila vinasses is quite encouraging, in particular for small and medium size tequila industries that operate under seasonal conditions.

A hybrid (continuous–discrete) cascade control is proposed to regulate both, volatile fatty acids (VFA) and chemical oxygen demand (COD) concentrations in two-stage (acidogenic-methanogenic) anaerobic digestion (TSAD) processes. The outer loop is a discrete controller that regulates the COD concentration of the methanogenic bioreactor by using a daily off-line measurement and that modifies the set-point tracked by inner loop, which manipulates the dilution rate to regulate the VFA concentration of the acidogenic bioreactor, estimated by continuous on-line conductivity measurements, avoiding acidification. The experimental validation was conducted in a TSAD process for the treatment of tequila vinasses during 110 days. Results showed that the proposed cascade control scheme was able to achieve the VFA and COD regulation by using conventional measurements under different set-point values in spite of adverse common scenarios in full-scale anaerobic digestion processes. Microbial composition analysis showed that the controller also favors the abundance and diversity toward methane production.

This paper presents the design and the experimental validation of a nonlinear control approach for the regulation of volatile fatty acids (VFA) in an anaerobic digester. Such an approach is conformed by an output feedback control and an extended Luenberger observer which allows the estimation of the uncertain terms associated to the VFA dynamics (i.e., in∞uent composition and kinetic terms). The nonlinear approach is experimentally validated in a 0.528m 3 up-∞ow flxed-bed anaerobic digester used for the treatment of industrial wine distillery wastewater. The experimental results show that the VFA regulation is achieved in spite of uncertain kinetics, restrictions in the control input and unknown load disturbances. Copyright c ∞2007 IFAC

A robust regulator for input delayed systems is applied to stabilize a class of bioreactors with a sampled delayed measurement, exhibiting partially known nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. This approach is applied on an anaerobic digestion model for the treatment of wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law perform quite well, leading the output COD towards its set-point.

This paper presents an approach to tune the gains of adjustable asymptotic observers on a class of continuous bioreactors. This approach is based on a careful analysis of the error estimation dynamics. The gains calculated with this approach improve the observer performance and guarantee a high convergence rate in the estimation of unmeasured variables with an accurate degree that can be chosen beforehand by the user. This approach was tested through numerical simulations in an anaerobic digestion process for wastewater treatment for the estimation of biomass concentrations, in which the gains obtained actually allowed the adjustable asymptotic observer to estimate the biomass concentrations with absolute precision. In order to show the usefulness of the adjustable asymptotic observer improved with this approach, its performance is compared in the face of the classical asymptotic observer with excellent results.

International audience; A robust regulation law is applied to the stabilization of a class of biochemical reactors exhibiting partially known highly nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. Based on some structural and operational conditions, this regulation law is shown to exponentially stabilize the aforementioned bioreactors around a desired set-point. This approach is experimentally applied and validated on a pilot-scale (1 m3) anaerobic digestion process for the treatment of raw industrial wine distillery wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law gave excellent performances leading the output COD towards its set-point and keeping it inside a pre-specified interval.

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.

A linear feedforward/feedback approach is derived for an up-flow fixed bed anaerobic digester; which used for the treatment of industrial wine distillery wastewater. The approach is novel in the sense that (i) the prescribed reference is the feedforward signal while the tracking error is the feedback signal and (ii) such an approach deals with uncertain kinetic terms, and attenuates load disturbances from the inlet composition. In addition, it requires neither the measurement nor the structure of such disturbances. Moreover, departing from physical properties of the process, some open-loop stability properties are analyzed. Finally, an antireset windup (ARW) scheme completes the control approach by taking into account constraints by actuator saturation. Results show that the ARW scheme improves the closed-loop performance.

A model-based robust control scheme is applied to a continuous stirred-tank polymerization reactor operating in an oscillatory regime to produce a polymer for a wide range of polymer properties. The proposed control algorithm is a robust error feedback controller that allows us to track the oscillatory profile of the monomer conversion with predetermined amplitude and frequency while maintaining the reactor temperature at a constant set-point. A case study is simulated to test the robustness properties of the proposed controller in the face of load disturbances and parameter variations and uncertainties.