Search

To solve (bio)chemical problems, often required to control the processes. However, the investigated system is not controllable at every state–space point. It is necessary to know what conditions must be provided for controllability. In this paper, we determine conditions for the controllability of chemical and microbiological reactions in isothermal and non-isothermal cases. In two examples, we show how can be applied the results in the study of different processes. • Controllability conditions can be specified for special form systems. • The controllability conditions depend on the type of the reactor. • The controllability is influenced by the temperature dependence of the process. [ABSTRACT FROM AUTHOR]

• Novel gradient based extremum-seeking control scheme. • The ESC scheme is not using exogenous perturbation signals. • The ESC technique is based on the modeling-error compensation approach using the input–output linearizing methodology. • Robustness despite modeling errors and external perturbations is achieved. This paper is focused on the design of a robust model based extremum-seeking controller (ESC) aimed at the online optimization of a class of uncertain nonlinear systems. The ESC scheme is based on the modeling-error compensation approach using a robust input–output linearizing control law coupled with a first-order gradient estimator. The feedback control scheme is able to drive the closed-loop system into a vicinity of the optimal operating set-point. In contrast with perturbation-based ESC algorithms, the control scheme includes an observer-based uncertain estimator for computing unknown components related to model uncertainties and a continuous gradient estimator, while avoiding the use of a perturbation signal for achieving closed-loop convergence to the optimal neighborhood. The convergence of the closed-loop system to the unknown optimal set-point is analyzed. Numerical simulations illustrate the effectiveness of the proposed ESC scheme in two different nonlinear systems. [ABSTRACT FROM AUTHOR]

Highlights • An overview of thermodynamic concepts including reaction invariants is outlined. • Asymptotic observers are adapted to thermodynamically consistent chemical reactors. • A sufficient condition related to the process estimation selection is proposed. • The convergence proof is shown using the port-Hamiltonian framework. • Illustration of theoretical developments by simulations is included. Abstract In this note, an extension of the design of asymptotic observers is proposed for the thermodynamically consistent non-isothermal continuous stirred tank reactor (involving n c active chemical species with n r independent reactions) that has incomplete state information. The two well-known constraints for the implementations of the asymptotic observer are the process operating conditions and the availability of temperature measurement together with (n r - 1) concentration measurements. However, even though the process operating conditions are justified, the requirement of having good estimation performances and high accuracy of (n c - n r + 1) state estimates may only be guaranteed if a so-called feasibility condition of the observer is fulfilled. This feasibility condition is formulated via the full rank constraint of a structural matrix, which is related to the stoichiometric coefficient matrix. A sufficient condition for the fulfilment of the full rank constraint shows that the process estimation selection is not made arbitrarily. Interestingly, as the designed observer is based on thermodynamic information only and does not require knowledge of reaction kinetics, the solution proposed to the feasibility condition has a clear physical meaning. Numerical simulations considered for Van de Vusse reaction system illustrate the application of the theoretical results. [ABSTRACT FROM AUTHOR]

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis–Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software. [ABSTRACT FROM AUTHOR]

Microrespirometry is a recently developed method that combines classical respirometry and microreactor systems, for the characterization of microbial cultures. This method, which allows multiple simultaneous replicates from a single low-volume biomass sample, can improve and simplify the model calibration exercise by providing more and better experimental data. To follow up on the interest in microrespirometry, two model wastewater treatment processes were set up and used for model calibration: an aerobic degradation of 4-chlorophenol by acclimated sludge, and a synthetic wastewater treatment by activated sludge, which were well described by a Haldane model and a modified ASM3 model, respectively. For each process, the model parameters were estimated by model fitting, minimizing the objective function for each replicate independently, and minimizing the objective function for all replicates simultaneously (multiresponse approach). Parameter confidence intervals were determined for all parameter estimates, and the impacts of measurement errors, number of replicates, and parameter correlation were assessed. It was observed that the multiresponse approach presents several advantages, including single-step parameter estimation, independent of the number of replicates, and determination of confidence intervals that include the main sources of uncertainty. It was concluded that microrespirometry is a high-throughput and convenient method for model calibration. [ABSTRACT FROM AUTHOR]

As motivated by the use of physical variables as Hamiltonian storage functions, this article focuses on dissipative pseudo-Hamiltonian realizations of chemical reaction systems, but with a particular emphasis on the continuous stirred tank reactor (CSTR) in order to further explore the structural differences. More precisely, two different dissipative pseudo-Hamiltonian representations are proposed to a given non-isothermal CSTR dynamics thanks to a unified potential function that verifies a thermodynamic evolution criterion. The first one with the singularity of the structure matrixF(where) resulting from thermodynamically inherent properties of the process, is obtained on the basis of functional separability. Even though the amount of dissipation is explicitly derived and exactly exhibits the process irreversibility, the main disadvantage of such a result is that the derivation of alternative pseudo-Hamiltonian models is impossible. One way to circumvent this inherent difficulty is to consider the Brayton–Moser form of the original dynamics. On this basis, a solution required for the Brayton–Moser formulation is first proposed. Interestingly, the proposed solution is based on thermodynamic information only. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative extremum seeking control design technique for the solution of real-time optimization control problems. The technique considers a proportional-integral approach that minimizes the impact of a time-scale separation on the transient performance of the extremum-seeking controller. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The dynamics are assumed to be of relative degree one with respect to the objective function. Two simulation examples are presented to demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper considers the solution of a real-time optimization problem using adaptive extremum seeking control. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying estimation problem. The proposed approach is shown to avoid the need for averaging results which minimizes the impact of the choice of dither signal on the performance of the extremum seeking control system. Several examples are used to illustrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Ice cream crystallization processes can be modeled by some population and energy balance equations. Due to the infinite dimensional and nonlinear characteristics, such models are highly complex, especially when all the phenomena of nucleation, growth and breakage are considered. Depending on the control problem under consideration, such a complexity can be useless and the control law can be designed on the basis of an input–output reduced order model of the process. In the present paper, we first consider a reduced order model of 6 ordinary differential equations obtained by the method of moments. By means of a sensitivity analysis and a parameter identification, it is shown that, to accurately describe the input–output behavior of the system whatever the conditions are, it is sufficient to change the values of only two parameters of this model, which is really interesting from a control point of view. However, when looking at the simulated data, the complexity of this moments model appears useless, from the input–output point of view. A second model reduction is therefore performed, based on physical assumptions. We finally get a new model with 3 ordinary differential equations, which is validated first on experimental data and then by comparison with the initial moments model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a thermodynamically stable evolution criterion for homogeneous multi-component mixtures with chemical transformation is proposed. The approach is motivated and governed by physical considerations strongly related to the second law of the thermodynamics. More precisely we show that there exists some potential function directly defined on the space of the extensive and/or intensive variables for any transformation, and that meets the evolution criterion without any restriction on the chemical reaction kinetics. As a consequence the irreversibility degree or passivity of the mixture under mass transfer and transport phenomena is explicitly expressed. Some numerical simulations for a homogeneous binary mixture with chemical reaction under multiplicity are given to validate our theoretical developments. [Copyright &y& Elsevier]

Abstract: A model development approach, which aims to build sufficiently simple models so as to ensure parameter identifiability, is presented and used to develop a dynamic mass balance model of photosynthesis and respiration reactions. The model was developed for the control of a highly automated plant growth chamber within the context of the MELiSSA project, a European Space Agency program which aims to develop technology for a future regenerative life support system. Simple, identifiable models are required for this application and are also valuable for other prediction and control applications, which could include the operation of greenhouses or other controlled environment agricultural systems in the future. Experiments were conducted on lettuce (Lactuca sativa cv. Lively) and red beet (Beta vulgaris cv. Detroit Medium Red) in sealed environment plant growth chambers. Environmental variables (including CO2 uptake data) were recorded throughout growth, while biomass and leaf area measurements were taken before transfer to the chamber and at harvest. The model development approach was iterative, with assessments of model reliability throughout. Major metabolic reactions with respect to plant growth were first selected and corresponding mass balance equations were written, taking structural identifiability into account. The kinetic model was selected, from several potential rate laws, based on an analysis of the fit of the model (on identification and independent validation datasets) and the reliability of the parameter estimates (practical identifiability). The application of this systematic model development approach yielded a very simple model of plant growth in which all parameters were identifiable based on the available experimental data (a unique value could be identified for each). The approach could be applied to many other crop modelling problems to obtain simple, identifiable and reliable models. [Copyright &y& Elsevier]

Abstract: In this paper, we present an adaptive extremum seeking control scheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired setpoints that maximize the biomass production. It assumes that only the substrate concentration is available for on-line measurement. Lyapunov stability is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations. [Copyright &y& Elsevier]

Abstract: This paper deals with the design and application of a nonlinear multivariable controller in an anaerobic digestion system (AD) carried out in two interconnected fixed bed bioreactors. The proposed control scheme is derived from a mathematical model of the AD system described by a set of partial differential equations and consists of an estimator and two nonlinear control laws. The first law is developed to regulate the volatile fatty acids in the first bioreactor while the second aims at maintaining the chemical oxygen demand at predetermined set-points in the second bioreactor. The performance of the control algorithm is evaluated via numerical simulations in the face of load disturbances, parameter kinetic uncertainties and set-point changes. Stability and convergence properties of the proposed control scheme are also addressed in this paper. [Copyright &y& Elsevier]

Abstract: In this work we analyze the transient behavior of the dynamics of multiple species competing in a chemostat for a single resource, presenting slow/fast characteristics. We prove that coexistence among a subset of species, with growth functions close to each other, can last for a substantially long time. For these cases, we also show that the proportion of non-dominant species can be increasing before decreasing, under certain conditions on the initial distribution. [Copyright &y& Elsevier]

Abstract: Process and power plant control, along with fault detection/isolation are being addressed by significant on-going research with many theoretical developments focused on improvements for all of these major industrial applications. This report provides an overview of the current key problems, recent accomplishments and trends, as well as a forecast of anticipated developments within this very important field of industrial applications. [Copyright &y& Elsevier]

Abstract: This paper deals with the steady-state optimal design of two interconnected Continuous Stirred Tank Biological or Chemical Reactors in which a single reaction occurs. In particular, three new qualitative consequences of results originally proposed by Harmand, Rapaport, and Trofino [Harmand, J., Rapaport, A., & Trofino, A. (2003). Optimal design of two interconnected bioreactors—Some new results. AIChE Journal, 49 (6),1433–1450] and Harmand, J., Rapaport, A., and Dramé [Harmand, J., Rapaport, A., & Dramé, A. K. (2004). Optimal design of two interconnected enzymatic reactors. Journal of Process Control, 14 (7),785–794] are presented. First, a unifying graph-based approach (originally adapted by Bischoff [Bischoff, K. B. (1966). Optimal continuous fermentation reactor design. The Canadian Journal of Chemical Engineering, 281–284] from a technique first proposed in the early 1960s by Aris [Aris, R. (1962). On optimal adiabatic reactors of combined types. Canadian Journal of Chemical Engineering, 40, 87–92] for the design of adiabatic reactors) for the optimal design of biological systems is proposed. This approach permits an easy interpretation of the concept of “Steady-State Equivalent Biological Systems” proposed in the above cited papers. Secondly, it is shown that a number of very different design optimization problems, other than those specifically treated in the literature, can be solved using the propositions introduced in these papers. In particular, it is shown that the “Steady-State Equivalent Biological Systems” concept can be extended to catalytic chemical processes, and conditions to optimally design both catalytic and autocatalytic systems in a unifying way are derived. Thirdly, an interesting corollary of the results enables us to show qualitatively that there exists an infinite number of ways for designing (not necessarily optimally) any single-rate biological process, depending on the way the process is configured in terms of input flow rate and recirculation loop. [Copyright &y& Elsevier]

This paper deals with the observability analysis of nonlinear tubular bioreactor models. Due to the lack of tools for the observability analysis of nonlinear infinite-dimensional systems, the analysis is performed on a linearized version of the model around some steady-state profile, in which coefficients can be functions of the spatial variable. The study starts from an example of tubular bioreactor that will serve as a case study in the present paper. It is shown that such linear models with coefficients dependent on the spatial variable are Sturm–Liouville systems and that the associated linear infinite-dimensional system dynamics are described by a Riesz-spectral operator that generates a C0 (strongly continuous)-semigroup. The observability analysis based on infinite-dimensional system theory shows that any finite number of dominant modes of the system can be made observable by an approximate point measurement. [Copyright &y& Elsevier]

This paper aims at giving an overview of available results of state and parameter approaches for chemical and biochemical processes. It is largely organized as a tutorial and starts with a brief reminder concerning the design of extended Luenberger (ELO) and Kalman (EKO) observers, followed by an illustrative nonlinear observer algorithm. Evaluation of the performance of classical observers in presence of model uncertainties will serve as a basis for the motivation of designing asymptotic and interval observers, that do not require the knowledge of the process kinetics. The design of state observers with known kinetic models but uncertain kinetic parameters will then be considered via suggestions of improvements of the EKO and the introduction of two other types of observers (observers where the unknown parameters are used as design parameters; adaptive observers). Finally, the design of on-line parameter estimation schemes will be introduced. One of the objectives of the present survey is also to suggest new research directions. [Copyright &y& Elsevier]

We design moving horizon state estimators for a general model of bioprocesses. The underlying optimization is nonconvex due to the microbial growth kinetics, which are modeled as nonlinear functions. We relax the nonconvex growth constraints so that the optimization becomes a second-order cone program, which can be solved efficiently at large scales. Unfortunately, solutions to the relaxation can be inexact and thus lead to inaccurate state estimates. To recover feasible, albeit potentially locally optimal solutions, we use the concave–convex procedure, which here takes the form of a sequence of second-order cone programs. We find that the moving horizon state estimators outperform the unscented Kalman filter on numerical examples based on the gradostat and anaerobic digestion when there is high process noise or parameter error. • We design a moving horizon estimator for a general bioprocess model. • It is a convex relaxation, which can be solved using second-order cone programming. • When not exact, we use the convex-concave procedure to obtain feasible solutions. [ABSTRACT FROM AUTHOR]

The direct catalytic conversion of bioethanol to butadiene, also known as the Lebedev process, is one of the most promising solution to replace the petro-based production of this important bulk chemical. Considering the intricate reaction mechanism—where a combination of acid-catalyzed dehydration reactions and metal-catalyzed dehydrogenation have to take place simultaneously—tailor-made bifunctional catalysts are required. We propose to use non-hydrolytic sol-gel (NHSG) chemistry to prepare mesoporous Ta-SiO2 materials which are further promoted by Ag via impregnation. An acetamide elimination route is presented, starting from silicon tetraacetate and pentakis(dimethylamido)tantalum(V), in the presence of a Pluronic surfactant. The catalysts display advantageous texture, with specific surface area in the 600–1000 m² g−1 range, large pore volume (0.6–1.0 mL g−1), an average pore diameter of 4 nm and only a small contribution from micropores. Using an array of characterization techniques, we show that NHSG allows achieving a high degree of dispersion of tantalum, mainly incorporated as single sites in the silica matrix. The presence of these monomeric TaOx active sites is responsible for the much higher dehydration ability, as compared to the corresponding catalyst prepared by impregnation of Ta onto a pristine silica support. We attempt to optimize the butadiene yield by changing the relative proportion of Ta and Ag and by tuning the space velocity. We also demonstrate that Ag or Cu can be introduced directly in one step, during the NHSG process. Copper doping is shown to be much more efficient than silver doping to guide the reaction towards the production of butadiene. [ABSTRACT FROM AUTHOR]

• It has been shown that, if knowledge on the reaction functions is available, we can design observers with improved convergence properties under the reasonable assumption of monotonicity. • If the unmeasurable state enters linearly in the reaction functions we propose an observer that converges in finite-time , under very weak excitation assumptions. • If this dependence is nonlinear , the observer ensures exponential convergence. • The results have been illustrated with three benchmark examples. The problem of designing state observers for reaction systems whose convergence rate is faster than the standard asymptotic observers [7] is addressed in this paper. It is assumed that the reaction functions are known and that there are more measurements than "independent" reactions. If the unmeasurable state enters linearly in the reaction functions we propose an observer that converges in finite-time , under very weak excitation assumptions. If this dependence is nonlinear , we additionally assume that there is an element of the reaction functions vector that depends only on one unmeasurable state and that these functions are strictly monotonic. Under these conditions, a state observer that ensures exponential convergence of the states that appear in the reaction functions is designed. For the unmeasurable states that do not appear in these functions, the convergence is similar to the one of the asymptotic observers. [ABSTRACT FROM AUTHOR]

A stochastic system loses stability in the sense of probability if its noise port is nonvanishing at the desired state. This further leads to the loss of stochastic passivity if the storage function is simultaneously expected to be positive definite. For this reason, the current work defines a kind of novel stability, called stochastic weak stability covering the convergence of distribution and ergodicity, for characterizing stable behaviors of such a class of stochastic systems. We also define the concept of stochastic weak passivity that admits stochastic passivity only outside a neighborhood of the desired state rather than in the whole state space. Based on it, the stochastic weak passivity theorems are developed to guide control design for stabilizing the stochastic systems with persistent noise in the sense of stochastic weak stability. We demonstrate the validity of these results by applying them to a nonlinear chemical process system. • Provide conditions under which stochastic systems are not stochastically passive. • Propose a weak stability notion, including convergence in distribution & ergodicity. • Propose stochastic weak passivity for systems that are not stochastically passive. • This weak passivity requires passivity outside a neighborhood of a desired point. • Stochastic weak passivity is associated with the weak stability notion. [ABSTRACT FROM AUTHOR]

This paper studies the influence of conservative phenomena at the mesoscopic scale that affect the behavior of macroscopic variables in chemical reactions, generally understood as purely dissipative processes and whose mathematical formulation is usually derived using macroscopic variables. It is shown that conservative phenomena at the mesoscopic scale can affect the entropy production by transiently “pulling away” the system from the thermodynamic equilibrium. Two case studies are presented to illustrate this fact: the first one is an isolated system with a single reaction including two different scenarios, a purely dissipative reaction; and a second one that considers the influence of conservative elements at the mesoscopic scale. The second case generalizes the results to multiple reactions. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a robust sliding mode-based extremum-seeking controller aimed at the online optimization of a class of uncertain reaction systems. The design methodology is based on an input-output linearizing method with variable-structure feedback, such that the closed-loop system converges to a neighborhood of the optimal set point with sliding mode motion. In contrast with previous extremum-seeking control algorithms, the control scheme includes a dynamic modelling-error estimator to compensate for unknown terms related with model uncertainties and unmeasured disturbances. The proposed online optimization scheme does not make use of a dither signal or a gradient-based optimization algorithm. Practical stabilizability for the closed-loop system around to the unknown optimal set point is analyzed. Numerical experiments for two nonlinear processes illustrate the effectiveness of the proposed robust control scheme. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the FinFET fabrication flow, a gap fill oxide is used to fill the space between the Si fins that needs to be recessed to define the height of the fin trench. However, with the used flowable chemical vapor deposition (FCVD) oxide, the eth rate depends on fin spacing, a phenomenon called pattern loading. In this work, we used water structuring by dissolved ions to decrease the etch rate between fins and control pattern loading. We first showed, using ATR-FTIR (Attenuated Total Reflection – Fourier Transform Infra-Red), that structure making and breaking ions proposed by Marcus (2009) for bulk solutions, could be used to modulate water structuring in nanotrenches. Then, the Tetra-propyl Ammonium cation (TPA+) and the triply ionized Phosphoric acid anion (PO 4 3−) were selected as structure making additives in HF 0.5%. Optimization of the etchant composition allowed to suppress pattern loading for a gap of 15 nm width using TPAH:HF mixtures, but not using HF:H 3 PO 4 mixtures. The etch rate obtained with TPAH:HF at unity pattern loading and at room temperature was about 4.7 nm/min. It could be improved to about 7 nm/min by increasing the temperature. The inefficacy of the Phosphoric acid was ascribed to a pH effect, as the mono-ionized Phosphoric acid species formed in HF was classified as a borderline ion with little structuring capability by Marcus. [Display omitted] • Ions proposed by Marcus (2009) for bulk solutions could be used to modulate water structuring in nanotrenches. • Pattern loading for a fin gap of 15 nm width could be cancelled by adding TPAH to HF 0.5%. • The etch rate of FCVD oxide was decreased between fins by adding TPA+ as structure making cation. [ABSTRACT FROM AUTHOR]

An adaptive funnel control method is considered for the regulation of the output for a class of nonlinear infinite-dimensional systems on real Hilbert spaces. After a decomposition of the state space and some change of variables related to the Byrnes–Isidori form, it is shown that the funnel controller presented in Berger et al. (2020) achieves the control objective under some assumptions on the nonlinear system dynamics, like well-posedness and Bounded-Input-State Bounded-Output (BISBO) stability. The theory is applied to the regulation of the temperature in a chemical plug-flow tubular reactor whose reaction kinetics are modeled by the Arrhenius nonlinearity. Furthermore a damped sine–Gordon model is shown to fit the required assumptions as well. The theoretical results are illustrated by means of numerical simulations. [ABSTRACT FROM AUTHOR]

A preface for the journal "Mathematical and Computer Modelling of Dynamical Systems," vol. 14, no.1, February 2008, issue 1, is presented.

This paper deals with the design of a robust nonlinear observer as a software sensor to achieve the on-line estimation of the concentration of Volatile Fatty Acids (VFA) in a class of continuous anaerobic digesters (AD). Taking into account the limited availability of on-line sensors for AD process, in this contribution is assumed that only the methane outflow rate is available for on-line measurement. The estimation method is based on a modified version for a two-dimensional mathematical model of AD process. From the differential algebraic observability approach it is shown that the VFA concentration is detectable from the methane outflow rate measurements. The observer convergence is analyzed by using Lyapunov stability techniques. Numerical simulations illustrate the effectiveness of the proposed estimation method for a four-dimensional AD model with uncertainties associated with unmodeled dynamics and disturbances in the inflow composition. [ABSTRACT FROM AUTHOR]

This paper is concerned with the monitoring of a biodiesel production process, more specifically with the monitoring of the esterification of grease trap wastes, a low quality feedstock for biodiesel production typically characterized by its high content of Free Fatty Acids (FFAs). The esterification takes place in a jacketed Continuous Stirred Tank Reactor (CSTR). A reset observer is designed and applied in order to provide on-line estimation of the concentration of FFAs from temperature measurements within the CSTR. In addition, the proposed reset observer is compared to two other observers (classical fuzzy observer and extended Kalman filter). According to a multiple range test conducted for analyzing the estimation error, the monitoring task for the process under study has been better fulfilled by the reset observer which is able to update the estimation results every instant when the measurements were available. [ABSTRACT FROM AUTHOR]

This paper develops the concept of decomposition for chemical reaction networks, based on which a network decomposition technique is proposed to capture the stability of large-scale networks characterized by a high number of species, high dimension, high deficiency, and/or non-weakly reversible structure. We present some sufficient conditions to capture the stability of a network when it can be decomposed into a complex balanced subnetwork and a few 1-dimensional subnetworks (and/or a few two-species subnetworks) with shared species between different subnetworks. The results cover encouraging applications on autocatalytic networks with some frequently-encountered biochemical reactions examples of interest, such as the autophosphorylation of PAK1 and Aurora B kinase, or autocatalytic cycles originating from metabolism. [ABSTRACT FROM AUTHOR]

This paper proposes an extremum-seeking control approach for a class of uncertain nonlinear dynamical systems subject to constraints. It is assumed that the objective function along with the constraints are available for measurement. The contribution of the paper is to formulate the extremum-seeking problem as a time-varying estimation problem. The constraints are enforced by using an interior point strategy that preserves feasibility of the systems' trajectories. Simulation examples are used to illustrate the effectiveness of the proposed technique. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] Extremum-seeking control approach for a class of fed-batch bioreactor with uncertain growth rate. [•] Extremum-seeking controller based on sliding modes techniques. [•] The controller scheme guarantees robustness against modeling errors, parametric variations and external perturbations. [ABSTRACT FROM AUTHOR]

The Linear-Quadratic (LQ) optimal control problem is studied for a class of first-order hyperbolic partial differential equation models by using a nonlinear infinite-dimensional (distributed parameter) Hilbert state-space description. First the dynamical properties of the linearized model around some equilibrium profile are studied. Next the LQ-feedback operator is computed by using the corresponding operator Riccati algebraic equation whose solution is obtained via a related matrix Riccati differential equation in the space variable. Then the latter is applied to the nonlinear model, and the resulting closed-loop system dynamical performances are analyzed. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims at emphasizing the importance of a systemic approach together with new investigation tools provided by molecular biology to give new insights into microbial ecology. Through the presentation of four recently obtained results about the coexistence of species in competition on a single resource, it is argued that such an approach can have major impact on ecological problems. In the first example, we revisit the existing models of biotechnology and show how they can be updated in some sense to better describe the real world by taking into account the spatial heterogeneity in a very simple but effective way (in the sense that their qualitative predictions are in accordance with the observed data). In the second example, we show how an optimally designed biosystem may give insights to the understanding of the so-called barrier effect. The third example proposes a new explanation for coexistence, that we called “long run coexistence”, in studying the transient behavior of a high dimensional system describing N species in competition on a single substrate. Finally, a last example presents the original concept of “biological control” which refers to the use of a particular species to globally stabilize an unstable biological process. [Copyright &y& Elsevier]

Abstract: The concept of asymptotic stability is studied for a class of infinite-dimensional semilinear Banach state space (distributed parameter) systems. Asymptotic stability criteria are established, which are based on the concept of strictly m-dissipative operator. These theoretical results are applied to a nonisothermal plug flow tubular reactor model, which is described by semilinear partial differential equations, derived from mass and energy balances. In particular it is shown that, under suitable conditions on the model parameters, some equilibrium profiles are asymptotically stable equilibriums of such model. [Copyright &y& Elsevier]

A new approach for solving convection–diffusion–reaction equations is presented. The method is based on the separation of the different phenomena. At each time step, convection, diffusion and reaction are applied successively on the reactor mesh. This sequencing method allows us to solve both hyperbolic and parabolic equations and to extend the frequency response of the numerical solution. Simulation results are given for linear and nonlinear reaction kinetics. These are compared with the exact solution for the high diffusion case and with up-wind finite difference methods for all cases. [Copyright &y& Elsevier]

Hybrid materials are intensely studied for potential applications in heterogeneous catalysis. Organic groups at the catalyst surface can modify not only its hydrophilicity, but also acidity, hydrothermal stability, porosity, etc. In some cases, tuning such properties leads to improved catalytic performance. Often, however, the organic moieties are limited to methyl groups introduced via post-grafting. Here, a series of mesoporous hybrid aluminosilicate materials was prepared in one pot by non-hydrolytic sol–gel (NHSG). Aromatic, aliphatic, pendant, and bridging organic groups were incorporated. The presence of the organic groups in the bulk and at the outermost surface of the materials was verified by solid-state NMR, IR, ToF–SIMS, and XPS. The hybrid aluminosilicates were tested as catalysts in the gas phase ethanol dehydration to ethylene, and most of them outperformed the inorganic catalyst benchmark. While a direct influence of surface hydrophobicity (as probed by water sorption and water contact angle measurements) appeared unlikely, characterization of acidity (IR-pyridine) revealed that the improved performance for hybrid catalysts could be correlated with a modification of the acidic properties. The latter are determined by the quality of the dispersion of Al centers in the form of isolated sites in the hybrid silica matrix, which itself appears to be influenced by the presence of organic groups in the non-aqueous synthesis. All in all, this study establishes a "ranking" for a variety of organic groups in terms of their influence on the catalyst activity. [ABSTRACT FROM AUTHOR]