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Long-term, continuous datasets of high quality are important for instrumentation, control, and automation efforts of wastewater resources recovery facility (WRRFs). This study presents a methodology to increase the reliability of measurements from ammonium ion-selective electrodes (ISEs). This is done by correcting corrupted ISE data with a data source that often is available at WRRFs (volume-proportional composite samples). A yearlong measurement campaign showed that the existing standard protocols for sensor maintenance might still create corrupted dataset, with poor sensor recalibrations responsible for abrupt and unrealistic jumps in the measurements. The proposed automatic correction methodology removes both recalibration jumps and signal drift by using information from composite samples that already are taken for reporting to legal authorities. Results showed that the developed methodology provided a continuous, high-quality time series without the major data quality issues of the original signal. In fact, the signal was improved for 87% of days when a reference sample was available. The effect of correcting the data before use in a data-driven software sensor was also investigated. The corrected dataset led to noticeably smaller day-to-day variations in estimated NH4+ loads, and to large improvements on both median estimates and prediction bounds. The long time series allowed for an investigation of how much training data that is required to fit a software sensor, which provides estimates that are representative for the entire study period. The results showed that 8 weeks of data allowed for a good median estimate, while 16 weeks are required for obtaining good 80% prediction bounds. Overall, the proposed method can increase the applicability of relatively cheaper ISE sensors for ICA application within WRRFs.

In the context of motorcycle, we can assist to an increasing interest toward semi-active suspension control systems able to improve both the comfort and the passenger’s safety in both racing and original equipment manufacturer applications. Such systems implement suitable strategies based on the measure of several quantities, among which the relative velocity of the wheels respect to the vehicle body with the aim of regulating in real-time the damping forces. The actual effectiveness of such strategy strongly depends on the reliability and accuracy of the data measured by the sensors involved in the control loop. Due to their simplicity and good performance in terms of linearity, the most used sensors for suspension displacement measurements are based on linear potentiometers but such kind of sensors suffer of wear and tear and aging higher than the other sensors involved in the control loop strategy. As a consequence, the fault detection of such sensor is strongly recommended to avoid wrong and in some cases dangerous suspension behaviors. To this aim, in this paper a Fault Detection scheme for the rear suspension stroke sensor is designed and verified. The residual generation is based on the use of a Nonlinear Auto-Regressive with eXogenous inputs (NARX) network which is able to effectively take into account for the system nonlinearity. Experimental results have proven the good promptness and reliability of the scheme in detecting different kind of faults as “un-calibration faults” (e.g. due to slight variations of the input/output sensor curve), “hold-faults” (e.g. due to the breaking of the potentiometer cursor), “open circuit” and “short circuit” (e.g. due to electrical interruptions and short circuits, respectively). In addition, to verify the feasibility of a real-time implementation on actual processing units employed in such context, the scheme has been successfully implemented on a microcontroller STM32 based on the general-purpose ARM-M4 architecture. The validation tests and analysis have shown that the proposed Instrument Fault Detection scheme could be successfully developed on these kind of architectures by assuring a real-time operating.

International audience; Microalgae consume carbon dioxide for their growth and to produce secondary metabolites. One of the control system challenges is to maximize this carbon dioxide consumption by controlling microalgae growth, and by maintaining its growth rate at an optimal value. However, there is no such thing as a microalgae growth rate sensor. This paper proposes a software sensor for the specific growth rate and biomass concentration of a microalgae culture in a continuous photobioreactor using the measurements of Total Inorganic Carbon in the medium. Numerical simulations are included to illustrate the performance of the proposed estimation strategy.

This paper focuses on the specific growth rate estimation problem in a Polyhydroxybutyrate bioplastic production process by industrial fermentation. The kinetics of the process are unknown and there are uncertainties in the model parameters and inputs. During the first hours of the growth phase of the process, biomass concentration can be measured online by an optical density sensor, but as cell density increases this method becomes ineffective and biomass measurement is lost. An asymptotic observer is developed to estimate the growth rate for the case without biomass measurement based on corrections made by a pH control loop. Furthermore, an exponential observer based on the biomass measurement is developed to estimate the growth rate during the first hours, which gives the initial condition to the asymptotic observer. Error bounds and robustness to uncertainties in the models and in the inputs are found. The estimation is independent of the kinetic models of the microorganism. The characteristic features of the observer are illustrated by numerical simulations and validated by experimental results., Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales

A pH indirect control algorithm for the continuous prefermentation of yo-gurt starter culture by strains Lactobacillus bulgaricus 2–11 and Streptococcus thermophilus 13A is proposed. A linear relationship between lactose concentra-tion and pH value in the reactor is determined. On this basis, a new model of continuous mode of cultivation is proposed that includes pH dynamical equa-tion. A software sensor of lactose consumption rate is derived. The obtained estimates are used for pH control derivation by lactose feeding. The results of simulation investigations show good performance of the proposed control algorithm.

The exponential development of biotechnology has lead to a quasi unlimited number of potential products going from biopolymers to vaccines. Cell culture has therefore evolved from the simple cell growth outside its natural environment to its use to produce molecules that they do not naturally produce. This rapid development could not be continued without new control and supervising tools as well as a good process understanding. This requirement involves however a large diversity and a better accessibility of process measurements. In this framework, software sensors show numerous potentialities. The objective of a software sensor is indeed to provide an estimation of the system state variables and particularly those which are not obtained through in situ hardware sensors or laborious and expensive analysis. In this context, This work attempts to join the knowledge of increasing bioprocess complexity and diversity and the time scale of process developments and favours systematic modelling methodology, its flexibility and the speed of development. In the field of state observation, an important modelling constraint is the one induced by the selection of the state to estimate and the available measurements. Another important constraint is the model quality. The central axe of this work is to provide solutions in order to reduce the weight of these constraints to software sensors development. On this purpose, we propose four solutions to four main questions that may arise. The first two ones concern modelling uncertainties. 1."How to develop a software sensor using measurements easily available on pilot scale bioreactor?" The proposed solution is a static software sensor using an artificial neural network. Following this modelling methodology we developed static software sensors for the biomass and ethanol concentrations in a pilot scale S. cerevisae cell culture using the measurement of titrating base quantity, agitation rate and CO₂ concentration in the exhaust gas. 2."How to obtain a reaction scheme and a kinetic model to develop a dynamic observation model?". The proposed solution is to combine three elements: a systematic methodology to generate, identify and select the possible reaction schemes, a general kinetic model and a systematic identification procedure where the last step is particularly dedicated to the identification of observation models. Combining these methodologies allowed us to develop a software sensor for the concentrations of an allergen produced by an animal cell culture using the discrete measurement of glucose, glutamine and ammonium concentrations (which are also estimated in continuous time by the software sensors). The two other questions are dealing with kinetic model uncertainty. 3 "How to correct kinetic model parameters while keeping the system observability?". We consider the possibility to correct some model parameters during the process observation. We propose indeed an adaptive observer based on the theory of the most likely initial conditions observer and exploiting the information from the asymptotic observer. This algorithm allows to jointly estimate the state and some kinetic model parameters. 4 "How to avoid any state observer selection that requires an a priori knowledge on the model quality?". Answering this question lead us to the development of hybrid state observers. The general principle of a hybrid observer is to automatically evaluate the model quality and to select the appropriate state observer. In this work we focus on kinetic model quality and propose hybrid observers that evolves between the state observation from an exponential observer (free convergence rate tuning but model error sensitivity) and the one provided by an asymptotic observer (no kinetic model requirement but a convergence rate depending on the dilution rate). Two strategies are investigated in order to evaluate the model quality and to induce the state observation evolution. Each of them have been validated on two simulated cultures (microbial and animal cells) and one real industrial one (B. subtilis). ∙ In a first strategy, the hybrid observer is based on the determination of a parameter that drives the state estimation from the one obtained with an exponential observer (exponential observation) when the model is of good quality to the one provided by an asymptotic observer (asymptotic observation) when a kinetic model error is detected. The evaluation of this driving parameter is made either with an a priori defined function or is coupled to the identification of the initial conditions in a most likely initial conditions observer. ∙ In another strategy, the hybrid observer is based on a statistical test that compares the state estimations provided by an exponential and an asymptotic observer and corrects the state estimation according to it./ Le rapide développement des biotechnologies permet actuellement d'envisager un nombre quasi illimité de produits potentiels allant du biopolymère au vaccin. La culture cellulaire a dès lors évolué de la simple croissance de cellules en dehors de leur environnement naturel à son exploitation pour la production de molécules qu'elles ne produisent pas naturellement. Un tel développement ne peut se poursuivre sans l'utilisation de nouvelles technologies de contrôle et de supervision ainsi q'une bonne compréhension et maîtrise du biprocédé. Cette exigence nécessite cependant une meilleure accessibilité et une plus grande variabilité des mesures des différentes variables de ce procédé. Dans ce contexte, les capteurs logiciels présentent de nombreuses potentialités. L'objectif d'un capteur logiciel est en effet de fournir une estimation des états d'un système et particulièrement de ceux qui ne sont pas mesurés par des capteurs physiquement installés sur le système ou par de longues et coûteuses analyses. Cet objectif peut être obtenu en combinant un modèle du système avec certaines mesures physiques au sein d'un algorithme d'observation d'état. Dans ce domaine de l'observation des bioprocédés, ce travail tente de considérer, à la fois, l'augmentation de la complexité et de la diversité des bioprocédés et l'exigence d'un développement rapide en favorisant le caractère systématique, flexible et rapide des méthodes proposées. Dans le cadre de l'observation des bioprocédés, une importante contrainte de modélisation est induite par la sélection des états à estimer et des mesures disponibles pour cette estimation. Une seconde contrainte est la qualité du modèle. L'axe central de ce travail est de fournir certaines solutions afin de réduire le poids de ces contraintes dans le développement de capteurs logiciels. Pour ce faire, nous proposons quatre réponses à quatre questions qui peuvent survenir lors de ce développement. Les deux premières questions concernent l'incertitude de modélisation. Quant aux deux questions suivantes, elles concernent l'incertitude du modèle lui-même. 1."Comment développer un capteur logiciel exploitant des mesures facilement disponibles sur un bioréacteur pilote?". La réponse que nous apportons à cette question est le développement d'un capteur logiciel statique basé sur un réseau de neurones artificiels. Cette structure nous a permis de développer des capteurs logiciels de concentrations en biomasse et éthanol au sein d'une culture de S. cerevisae utilisant les mesures en ligne de quantité de base titrante, de vitesse d'agitation et de concentration en CO₂ dans le gaz sortant du réacteur. 2."Comment obtenir un schéma réactionnel et un modèle cinétique pour l'identification d'un modèle dynamique d'observation". Afin de répondre à cette question, nous proposons de combiner trois éléments: une méthode de génération systématique de schémas réactionnels, une structure générale de modèle cinétique et une méthode d'identification dont la dernière étape est particulièrement dédiée à l'identification de modèles d'observation. La combinaison de ces éléments nous a permis de développer un capteur logiciel permettant l'estimation continue de la concentration en un allergène produit par une culture de cellules animales en utilisant des mesures échantillonnées de glucose, glutamine et ammonium (qui sont elles aussi estimées en continu par le capteur logiciel). 3."Comment corriger certains paramètres cinétiques tout en maintenant l'observabilité du système?". Nous considérons ici la possibilité de corriger certains paramètres du modèle cinétique durant le procédé de culture. Nous proposons, en effet, un observateur d'état adaptatif exploitant la théorie de l'observateur par identification des conditions initiales les plus vraisemblables et l'information fournie par un observateur asymptotique. L'algorithme proposé permet ainsi de fournir une estimation conjointe de l'état et de certains paramètres cinétiques. 4."Comment éviter la sélection d'un observateur d'état nécessitant une connaissance, a priori, de la qualité du modèle?". La dernière contribution de ce travail concerne le développement d'observateurs d'état hybrides. Le principe général d'un observateur hybride est d'évaluer automatiquement la qualité du modèle et de sélectionner l'observateur d'état approprié. Au sein de ce travail nous considérons la qualité du modèle cinétique et proposons des observateurs d'état hybrides évoluant entre un observateur dit exponentiel (libre ajustement de la vitesse de convergence mais forte sensibilité aux erreurs de mesures) et un observateur asymptotique (ne nécessite aucun modèle cinétique mais présente une vitesse de convergence dépendante du taux de dilution). Afin de réaliser cette évaluation et d'induire l'évolution de l'observation d'état entre ces deux extrémités, deux stratégies sont proposées. Chacune d'elle est illustrée sur deux cultures simulées (une croissance bactérienne et une culture de cellules animales) et une culture réelle de B. subtilis. ∙ Une première stratégie est basée sur la détermination d'un paramètre de pondération entre l'observation fournie par un observateur exponentiel et un observateur asymptotique. L'évaluation de ce paramètre peut être obtenue soit au moyen d'une fonction définie a priori soit par une identification conjointe aux conditions initiales d'un observateur par identification des conditions initiales les plus vraisemblables. ∙ Une seconde stratégie est basée sur une comparaison statistique entre les observations fournies par les deux types d'observateurs. Le résultat de cette comparaison, lorsqu'il indique une incohérence entre les deux observateurs d'état, est alors utilisé pour corriger l'estimation fournie par l'observateur exponentiel., Doctorat en sciences agronomiques et ingénierie biologique, info:eu-repo/semantics/published

International audience; A mathematical model for recombinant bacteria which includes foreign protein production is developed. The experimental system consists of an Escherichia Coli strain and plasmid pIT34 containing genes for bioluminescence and production of a protein, beta-galactosidase. This recombinant strain is constructed to facilitate on-line estimation and control in a complex bioprocess. Several batch experiments are designed and performed to validate the developed model. The design of a model structure, the identification of the model parameters and the estimation problem are three parts of a joint design problem. A nonlinear observer is designed and an experimental evaluation is performed on a batch fermentation process to estimate the substrate consumption.

Three different methodologies are assessed which provide predictions of the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging from a simple regression model over an adaptive grey-box model to a complex hydrological and full dynamical wave model (Chow et al., 1988). The simple regression model is estimated as a transfer function model of rainfall intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates adaptation to the dry weather flow as well as the rainfall runoff. The full dynamical flow model is a distributed deterministic model with many parameters, which has been calibrated based on extensive measurement campaigns in the sewer system. The three models are compared by the ability to predict the hydraulic load one hour ahead. Five rain events in a test period are used for evaluating the three different methods. The predictions are compared to the actual measured flow at the plant one hour later. The results show that the simple regression model and the adaptive grey-box model which are identified and estimated on measured data perform significantly better than the hydrological and full dynamical flow model which is not identifiable and needs calibration by hand. For frontal rains no significant difference in the prediction performance between the simple regression model and the adaptive grey-box model is observed. This is due to a rather uniform distribution of frontal rains. A single convective rain justifies the adaptivity of the grey-box model for non-uniformly distributed rain, i.e. the predictions of the grey-box model were significantly better than the predictions of the simple regression model for this rain event. In general, models for model-based predictive control should be kept simple and identifiable from measured data.

International audience; A new technology for ethanol production is proposed. For this purpose immobilized yeast cells of strainsSaccharomyces cerevisiae 46 EVD or Saccharomyces cerevisiae BO 213 were used. Double coated Caalginatecapsules realized with a new method for immobilization was applied. In this way, a maximalmechanical stability of immobilized cells was achieved. For control algorithm design, a software sensor ofethanol production rate based on on-line or laboratory measurements of ethanol concentration was derived.The software sensor information is used to switch automatically mode of cultivation from batch to continuousone. Applying direct digital control by LABVIEW system, ethanol concentration is stabilized at a constantvalue that guarantees maximum productivity.