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This study addresses an innovative multivariable control strategy for a wastewater treatment bioprocess with activated sludge. This bioprocess takes place in a continuous reactor located at the Wastewater Treatment Plant Facai, Romania. The proposed adaptive control structure is designed under realistic assumptions: several process variables are unavailable, the reaction kinetics is time-varying and unknown, the influent pollutant flow rate varies in large limits and much more its concentration is totally unknown. The novelty of the approach consists in the insertion of a sliding mode observer (SMO) in overall control structure. The SMO provides the estimates of the unknown influent pollutant concentration. The performance of the adaptive multivariable control algorithm is assessed via numerical simulations based on data provided from the plant.

BACKGROUND This work addresses the design of robust adaptive control strategies for a lactic fermentation process carried out inside a continuous stirred tank reactor (CSTR). This bioprocess is widely used in bioindustry for lactic acid production, and it is characterized by strong nonlinearities and uncertainties. Under the realistic assumption that the reaction kinetics and the influent flow rates are unknown and time-varying, but lower and upper bounds of the uncertainties are known, two innovative robust adaptive control structures are proposed. RESULTS The control strategies are designed by combining a linearizing control law with two novel parameter estimators able to estimate the unknown reaction rates. The first strategy uses a modified observer based estimator and the second one utilizes an interval parameter estimator. The control objective is to adjust the plant load so as to convert the glucose into lactic acid via fermentation, which is directly correlated with the economic aspects of lactic acid production. Numerical simulations are performed in order to validate the proposed solution. CONCLUSION By using the proposed control strategies the process is maintained at certain operating points that correspond to a maximum lactic production rate and a minimal residual glucose concentration. The simulations performed by considering various uncertainties and noisy measurement data show that the proposed novel robust adaptive controllers are suitable to control this kind of fermentation processes. © 2017 Society of Chemical Industry

The present paper focuses on estimation and advanced control structures for an alcoholic fermentation process (AFP) exploited for the ethanol production. Taking into account the inhibition problems, the control aim consists in the preservation of the so-called process inhibitory compounds at a prescribed reduced level in spite of large variations of the influent pollutant concentration and of the load, having as a result a large production of alcohol. Using a strongly nonlinear and uncertain model and realistic operating conditions, novel estimation and advanced control schemes are proposed. More precisely, adaptive control laws designed via novel nonlinear estimation algorithms for unknown inputs and kinetics are developed. To illustrate that the control goal is fulfilled, various tests performed by computer simulations under realistic conditions are included.

The paper addresses the modelling and the design of an adaptive control structure for an aerobic fermentation process under the realistic assumption that some state variables are unmeasurable, the reaction kinetics are incompletely known and time-varying and the influent flow rate has very large variations. More exactly, the studied process is a wastewater biological treatment process, with activated sludge, carried out in a continuous perfectly mixed bioreactor implemented in the Wastewater Treatment Plant (WWTP) Facai, Craiova, Romania. The behaviour and performance of control algorithms are evaluated by several numerical simulations and are compared with the real outputs of the mentioned plant. The aim of this study is to improve the plant performance by implementation of the proposed control algorithms.

This study addresses the design of advanced control schemes implemented for a continuous fermentation process used to produce ethanol. Due to the inaccuracy of the models that express this complex process, a feasible controller is required to maximize the production of ethanol and to minimize its environmental impact, despite the existence of some significant uncertainties. Therefore, novel estimation and control schemes are designed and tested. These schemes are adaptive control laws including nonlinear estimation algorithms: a sliding mode observer to estimate the unknown influent concentration, but also state observers and parameter estimators used to estimate the unknown states and kinetics. Since the temperature is an important factor for an efficient operation of the process, an algorithm for temperature control in the bioreactor is also developed. To verify the control algorithms effectiveness, several tests performed via numerical simulations under realistic conditions are presented.

A novel multivariable adaptive control scheme for a lactic fermentation biotechnological process with unknown inputs taking place inside two sequenced Continuous Stirred Tank Reactors (CSTRs) is developed. The design of the control scheme is achieved under the hypothesis that the influent substrate concentrations and the reaction kinetics are unknown and time-varying, but also a part of the state variables are unmeasurable. The adaptive controller includes a linearizing controller connected with two state observers used for the real-time estimation of the unknown input substrate concentrations and with a parameter estimator which is used for kinetics estimation. The estimation of unknown influent substrate concentrations is realized by using the measurements of the internal substrate and lactic acid concentrations. The proposed parameter estimator employs the information which is provided on-line by two sliding-mode state observers. The performance of the designed control scheme is evaluated via simulation experiments conducted for a lactic acid production process (LAPP) carried out inside two sequenced CSTRs. The dynamics of the considered bioprocess is described by a nonlinear, incompletely known and time varying model.

The paper addresses the developing method of an adaptive control structure for an anaerobic digestion process under the realistic assumption that some state variables are unmeasurable, the reaction kinetics are incompletely known and time-varying and moreover the influent pollutant concentration is completely unknown. More exactly, the process that is taken into account is a wastewater biological treatment process operated in a continuous perfectly mixed bioreactor. The control solution proposed in the paper is based on an innovative adaptive technique. The controller design is achieved by combining an exact linearizing controller with a sequence of two cascaded state observers and with a parameter estimator. The proposed adaptive control structure is able to cope with several problems such as uncertainty of inputs and of kinetics, unavailability of on-line measurements, disturbances and noisy measurements. The behaviour and efficacy of estimation and control structures are evaluated by several digital simulations.

The paper addresses the design and the analysis of adaptive and robust-adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on-line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms.

This paper presents the design and analysis of a multivariable robust-adaptive control strategy for a continuous aerobic wastewater treatment bioprocess. The design procedure is developed under the realistic assumption that the bacterial growth rates are uncertain and the influent flow rates are disturbed, but some lower and upper bounds of these uncertainties (possibly time varying) are known. The control structure is achieved by combining a linearizing control law with an interval observer able to estimate lower and upper bounds of unmeasurable states. The performance of the control algorithms are illustrated by simulations performed for a recycled wastewater treatment bioprocess for which kinetic dynamics are highly nonlinear, time-varying and incompletely known.

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a class of anaerobic wastewater treatment processes. The design procedures are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. To achieve these control strategies, firstly, a state asymptotic observer and a robust interval observer for a class of bioprocesses are presented. Secondly, by using these observers, adaptive, robust and robust-adaptive control schemes are developed and analyzed. The adaptive control structure is achieved by combining a linearizing control law with a state asymptotic observer and with a parameter estimator used for on-line estimation of unknown kinetics. The robust control structure is designed by combining a linearizing control law with an interval observer able to estimates a lower and an upper bound of unmeasurable states. Also, the uncertain process parameters are replaced by their lower and upper bounds assumed known. The robust-adaptive controller uses a linearizing control law coupled with an interval observer for the unmeasured states, and with a parameter estimator. These approaches are applied to a particular wastewater treatment bioprocess based on anaerobic fermentation. The effectiveness of the proposed algorithms is validated by several numerical simulations.

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

This work deals with some sliding mode (SM) and adaptive sliding-mode control (SMC) strategies for a class of nonlinear biotechnological processes. First, a dynamical SM-based feedback strategy is designed in order to ensure the asymptotic output stabilization of nonlinear bioprocesses. The control law design is done by means of a combination between the exact linearization approach and the SMC. Second, an adaptive SMC strategy is derived for this class of bioprocesses. In order to deal with the parametric uncertainties of the bioprocesses, the adaptive form of the SMC law is designed by means of direct, overparameterized adaptive control techniques available for linearizable systems. The paper also presents the implementation of the proposed control strategies for two typical bioprocesses belonging to the studied nonlinear class. The first prototype process takes place into a Continuous Stirred Tank Bioreactor, and the second is a lipase production process that takes place inside a Fed-Batch Bioreactor. Copyright © 2007 John Wiley & Sons, Ltd.

This paper presents the design of adaptive and robust-adaptive control strategies for an anaerobic bioprocess with biogas production. The design schemes are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with a state asymptotic observer or an interval observer, and with a parameter estimator used for on-line estimation of unknown kinetics of the process. Numerical simulations are performed to test the proposed algorithms.

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a nonlinear propagation bioprocesses carried out inside a fixed bed reactor. The design schemes are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately state asymptotic observer or an interval observer and with a parameter estimator used for on-line estimation of unknown kinetics. The effectiveness of the proposed algorithms is validated by numerical simulations applied in the case of a prototype fixed bed bioprocess.

This paper addresses the design of an adaptive control strategy for an alcoholic fermentation bioprocess, which takes place inside of a Fed-batch Bioreactor (FBB). This bioprocess is highly nonlinear, some state variables are not measurable and moreover the reaction kinetics is not perfectly known. The kinetic parameters are estimated by using a high-gain nonlinear estimator. An asymptotic state observer is derived so as to provide the unavailable states necessary to the kinetics estimator. Finally, the indirect adaptive control law is designed by combining a linearizing controller with the nonlinear observers. Several numerical simulations are performed, in order to test the proposed nonlinear adaptive control strategy.

Pateikti realaus laiko nuotekų valymo proceso, vykstancio bioreaktoriuje, vertinimas ir adaptyvusis valdymas. Sis procesas yra netiesinis, naudojami būsenų matavimai turi trūkumų ir reakcijos kinetika nėra tiksliai žinoma. Kinetiniai parametrai įvertinami naudojant didelio stiprinimo netiesinius stebiklius, o nesamos būsenos yra rekonstruojamos naudojant asimptotinį būsenų stebiklį. Valdymo tikslas – palaikyti žemą tersalų koncentracijos lygį. Adaptyviojo valdymo taisyklė yra sudaryta kombinuojant tiesinimo valdiklį su netiesiniais stebikliais. Siūlomų algoritmų veiksmingumui nustatyti buvo atliktos skaitmeninės imitacijos. Il. 8, bibl. 17 (anglų kalba; santraukos anglų ir lietuvių k.). DOI: http://dx.doi.org/10.5755/j01.eee.117.1.1047

This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production, which is carried out in continuous stirred tank bioreactors. Firstly, an indirect adaptive control structure based on the nonlinear process model is derived by combining a linearizing control law with a new parameter estimator. This estimator is used for on-line estimation of the bioprocess unknown kinetics, avoiding the introduction of a state observer. Secondly, a tuning procedure of estimator design parameters is achieved by stability analysis of the control scheme. The effectiveness and performance of estimation and control algorithms are illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying, and completely unknown, and not all the state variables are measurable.

Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.

This work presents several issues concerning the modelling, kinetic estimation and nonlinear control of a baker’s yeast process that takes place inside a fed-batch bioreactor (FBB). First, a nonlinear model of the bioprocess is presented taking into consideration two approaches: the classical modelling scheme and the bond graph method. Second, to reconstruct the missing on-line kinetic information, estimation strategies are proposed. Finally, by using the process dynamical model and the on-line estimation strategies, an adaptive control scheme is designed in order to maintain a certain level of ethanol concentration, regardless of uncertainties and disturbances. This objective is attained by designing an adaptive controller as a combination between a linearizing control law and a high-gain observer. Numerical simulations are provided to illustrate the behaviour of the proposed estimation and control techniques.

This paper deals with the problem of on-line estimation and nonlinear control of a wastewater treatment process, which takes place inside a biological Sequencing Batch Reactor. This bioprocess is highly nonlinear, the on-line measurements are lack and the reaction kinetics is not perfectly known. The unknown kinetic parameters are estimated by using nonlinear observers, based on high-gain approach. Also, the unavailable states are reconstructed by using an asymptotic state observer. The control objective is to maintain a low level of pollutant concentration. This goal is achieved by using a linearizing control law combined with the proposed nonlinear observers, which results in an adaptive control law. Simulations are included in order to test the behavior of the proposed estimation and control algorithms.

This work deals with the design and analysis of some nonlinear and neural adaptive control strategy for a lactic acid production that is carried out in continuous stirred tank bioreactors. An indirect adaptive controller based on a dynamical neural network used as on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. The effectiveness and performance of both control algorithms are illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying and completely unknown.

This paper studies the design and the analysis of a nonlinear and neural adaptive control strategy for a complex nonlinear and time varying wastewater treatment bioprocess. In fact a direct adaptive controller based on a radial basis function neural network used as an on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. A realistic case study, which consists of a complex bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor, is fully analyzed. Computer simulations are included to demonstrate the behaviour and the performance of the proposed controllers.

In this chapter, some classical, adaptive and neural control strategies for a simple planar robotic manipulator with two revolute joints were designed and implemented. First, the conventional computed-torque method was discussed. This control method solves the precision tracking problem, by using an exactly linearization of the nonlinearities of the manipulator model. The main disadvantage is the assumption of an exactly known dynamic model. If the model is imprecise known, it is necessary to design adaptive and/or neural control strategies. Direct and indirect adaptive controllers have been studied and implemented in order to preserve the tracking performances when parameter uncertainties occur. From the simulation point of view, it can be noticed that the evolution of tracking errors remains good, even if the estimated parameters are used in the control law. Also, three neural based control strategies were developed: a feedforward neural controller, a feedback neural control scheme, and a feedback error based neural controller. The simulations showed that the proposed neural controllers obtain results comparable to those achieved using adaptive control strategies. If a classical (PD or computed-torque) controller already controls a manipulator the advantage of proposed neural structures is that extension to a neural controller for performances improvement is easy.

This work deals with the design and analysis of a nonlinear and neural adaptive control strategy for an anaerobic depollution bioprocess. A direct adaptive controller based on a radial basis function neural network used as on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics wastewater biodegradation process, are included to illustrate the behaviour and the performance of the presented controller.

This paper deals with estimation and adaptive control strategies for a biotechnological process, which is in fact a lipase production process that takes place inside a fed-batch bioreactor. The lipase production process is highly nonlinear and, furthermore, the available on-line measurements are lack and the reaction kinetics is not perfectly known. On-line state estimation strategies based on extended Luenberger observer and asymptotic observer approach are derived. The unknown kinetic parameters of the bioprocess are estimated by using nonlinear techniques, such as regressive parameter estimator and high-gain observer. The control goal is to maximize the lipase production by controlling the substrate feeding rate. A nonlinear feedback control law is obtained by means of exact linearization technique. By coupling this controller with the parameter estimation algorithms, a nonlinear adaptive controller is obtained. Numerical simulations are included in order to test the behavior and the performance of the proposed estimation and control strategies.

The paper presents the design and analysis of some nonlinear and neural adaptive control strategies for a class of time-varying and nonlinear processes. In fact, a direct adaptive controller based on a radial basis function neural network used as online approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controllers design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics fermentation process, are included to illustrate the behaviour and the performance of the presented control laws.

The paper studies the design and analysis of some nonlinear and neural adaptive control strategies for a wastewater treatment process, which is an activated sludge process with nonlinear, time varying and not exactly known kinetics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed and then is compared with a classical linearizing controller. The neural controller design is achieved by using an input-output feedback linearization technique.

This paper presents the design and the analysis of some nonlinear adaptive control strategies for a class of anaerobic depollution processes that are carried out in continuous stirred tank bioreactors. The controller design is based on the input-output linearization technique. The adaptive control structure is based on the nonlinear model of the process and is combined with a state observer and a parameter estimator which play the role of the software sensors for the on-line estimation of biological states and parameter variables of interest of the bioprocess. The resulted control methods are applied in depollution control problem in the case of the anaerobic digestion bioprocess for which dynamical kinetics are strongly nonlinear and not exactly known, and not all the state variables are measurable. The effectiveness and performance of both estimation and control algorithms are illustrated by simulation results.

This paper presents the design of a nonlinear multivariable adaptive control strategy for a class of wastewater depollutionbioprocesses that are carried out in recycle bioreactors. The adaptive control structure is achieved by combining a linearizing controllaw with a new state observer and a parameter estimator, which play the role of the software sensors for on-line estimation of biologicalstates and parameter variables of interest of the bioprocess. Simulation results are included to illustrate the performance of bothestimation and control algorithms.

This paper presents the control problem of a class of propagation bioprocesses that are carried out in fixed bed reactors. Since the dynamics of these processes are described by partial differential equations, in order to obtain useful models for control purposes, a possible method consists of approximation of their infinitely order associated models by finite order models. A class of nonlinear adaptive controllers are then designed based on these finite order models, which consist of a set of ordinary differential equations obtained here by orthogonal collocation method. Computer simulations conducted in the case of a fixed bed reactor are included to illustrate the performances of the proposed adaptive controllers.