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Many authors have published in the field of relativistic fluid mechanics (Von LAUE, 1911) (HALBWACHS, 1960) (PICHON, 1965) (LICHNEROWICZ, 1966) (BRUHAT, 1966) (MARLE, 1969) (ARZELIES, 1971) (MAHJOUB, 1971) (REZZOLLA et al., 2013) (DISVONZI, 2014) (DERRADI et al., 2016). However, the demonstrations carried out are subject to caution, either due to errors, or by the use of the BOLTZMANN equation or the principle of least action associated with the Lagrangian (FONTEIX et al., 2020). The conference presented at the previous SFGP congress (CAMARGO et al., 2019) was based on the results of a publication (FONTEIX et al., 2020). The aim was to write the energy, momentum and matter balances in special relativity, for continuous fluid mixtures (ARZELIES, 1971). Contrary to custom, these assessments were demonstrated using 2 principles. The first is well known, it is the inseparability of space and time during a change of inertial frame (MINKOWSKI, 1907). The second is our original proposal: in the Tangent Inertial Frame of a flat space-time, the equations of the relativistic dynamics of continuous fluid mixtures and those of classical physics are identical. Thanks to this, we were able to integrate relativistic gravitation (BRAGINSKY et al., 1992) (Mc DONALD, 1997) (TARTAGLIA et al., 2004) in flat space-time (not curved) and electromagnetism. However, we had not considered nuclear reactions and quantum phenomena (SCHULMAN, 1980), nor approached relativistic thermodynamics (STRUCHTRUP, 1998). We will not do it more here, but we will see in the present conference if it is possible, based on the previous results, to make experiments whose results differ significantly from those carried out on earth, namely experiments carried out in the fastest usable vehicle currently available: the International Space Station (ISS).

International audience; Computation‐based techniques and modelling of human knowledge and preferences by using multi‐criteria decision aid (MCDA) methods are integrated in a multi‐scale and multi‐disciplinary approach for the chemical product and process design. The proposed methodology has four main stages: (a) construction of a molecular model for predicting product performance, (b) validation of product performance, (c) selection of alternatives integrating preferences of manufacturers and consumers, and (d) process optimization implementing MCDA methods. The methodology is oriented to find new products that can replace components of formulations, whose performance is already known. It was applied to an exploratory study about the use of glycerol as raw material to produce plasticizers for polyvinyl chloride (PVC), replacing 2‐ethylhexyl phthalate (DEHP). The results of the case study and the proposed process design offer promising prospects regarding their application in other chemical products.

International audience; Glycerol tri-isovalerate presents promising performance as new PVC plasticizer. To establish the conditions under which it must be produced on an industrial scale, a model of the process was developed and adjusted using experimental results and parametric identification with Fisher's maximum likelihood method. Tests were designed to study the influence of temperature, pressure, molar ratio and catalyst concentration on conversion. Also, a multiobjective optimization of the production conditions, in Pareto's sense, was performed using an evolutionary algorithm. Furthermore, a decision aid tool based on two different functions was utilized with the technique MACBETH to select the conditions for scale- up.

International audience; Reactive extraction is a potential alternative for biodiesel production, in which reaction and glycerol separation are simultaneous, increasing conversion, yield and process productivity. This paper presents a stepwise reaction-separation model for a counter-current reactive extraction column, used in homogeneous-base-catalyzed palm oil methanolysis, and the models' parameters are identified from the results of a set of experimental tests. In addition, multi-criteria optimisation of the process conditions was performed using an evolutionary algorithm. The optimum conditions provide for palm oil conversion of 97.7%, a yield to FAME of 99.5% and a process productivity of 1.4 m3 FAME h−1 m−3 in a single reaction step. Finally, a comparison of the process productivity with co-current and batch processes was made. As the counter-current reactive extraction process does not require the intermediate separation stages required in co-current and batch processes, its productivity is 1.5 and 6.9 times higher than those processes, respectively.

Asphalt-modification was studied using two different types of triblock copolymers both with star-like molecular architecture: poly(styrene-b-butadiene-b-styrene), SBS, and poly(styrene-b[(butadiene)1−x-(ethylene-co-butylene)x]-b-styrene), SBEBS, to elucidate the effect of the molecular characteristics of the polymer and the polymer-content on the morphology and rheological behavior of polymer-modified asphalt, P-MA. The P-MAs were prepared using a hot mixing process and characterized using fluorescence microscopy and oscillatory shear flow measurements under linear viscoelastic conditions. Results revealed that the morphology of the polymer-rich phase and rheological behavior of the P-MA are dependent on the type and concentration of polymer: (i) P-MAs prepared with SBEBS exhibited higher dispersion in the asphalt matrix and were more elastic and less responsive toward frequency changes; (ii) P-MAs prepared with the polymer (SBS or SBEBS) of the larger molecular weight displayed higher elasticity; and (iii) the increase in the polymer concentration from 3 to 10 wt% resulted in the creation of P-MAs with higher elastic response. These results substantiate the importance of the molecular characteristics of the polymer in determining the properties of P-MAs, in particular composition of the elastomeric-b and molecular weight of these polymers, and the results are explained considering the molecular characteristics of these polymers and the polymer/asphalt interaction. POLYM. ENG. SCI., 53:2454–2464, 2013. © 2013 Society of Plastics Engineers

Summary: This paper deals with the design and control of the morphology of core-shell nanoparticles elaborated by fed-batch emulsion copolymerization of styrene and butyl-acrylate in the presence of a chain transfer agent (n-dodecyl mercaptan). A mathematical model was elaborated and validated. It consists of a system of differential algebraic equations involving 49 unknown kinetic and thermodynamic parameters, many of them being impossible to be accurately estimated, due to the lack of experimental data. A method based on the sensitivity analysis allowed us to determine a subset of the 21 most influential parameters. The 28 non estimable parameters were taken from the literature. The validated model was then used in a dynamic multiobjective optimization to optimize the best profile of the pre-emulsion feed rate to control (i) the composition and average molar masses of the copolymer, (ii) the instantaneous glass transition temperature, corresponding to a core-shell morphology suited for special end-use properties.

International audience; Acrylonitrile and styrene radical copolymerization in a dispersed medium has been investigated. Experiments were carried out in a continuous stirred-tank reactor in the presence of a stabilizing agent elaborated in situ during polymerization. The continuous phase was a polyol. The numerous elementary chemical mechanisms concerning the copolymerization as well as synthesis and grafting of the stabilizing agent together with several physical phenomena clearly show the complexity of the process. Using justified assumptions and a simplified kinetic scheme, a tendency model of the process was developed, using mass balances and thermodynamics. Its unknown parameters were identified by use of an evolutionary algorithm and experimental data resulting from an adapted experimental strategy. This model was then validated and allowed to forecast, with an acceptable order of magnitude, the number and weight average molecular weights, monomers and transfer agent conversions, amounts of solids, copolymer composition, amounts of grafted copolymer, and average particle diameters versus the operating conditions.

International audience; This work reports, for the first time, on the effect of carbon nanotubes (CNT) on the kinetics of the activated polymerization of lactams. Differential scanning calorimetry (DSC) was chosen to study the kinetics of the activated anionic polymerization of ε-caprolactam in the presence of multiwalled carbon nanotubes (MWCNT). A statistical test was carried out to confirm the validity of the experimental approach. The presence of the MWCNT slowed down the polymerization rate. This inhibiting effect depended not only on the MWCNT content but also on its state of dispersion in the polymerizing system. An autocatalytic model coupled with an evolutionary algorithm showed that the MWCNT content had an effect on the autocatalysis while its state of dispersion had an effect on the apparent polymerization kinetic constant.

This paper deals with the development of a mathematical model for emulsion copolymerization of styrene and butyl acrylate carried out in the presence of n-dodecyl mercaptan as chain transfer agent (CTA). The model consisted of a system of differential algebraic equations in which the population balances are based on a new approach that reduces significantly the number of equations involved and the corresponding computational time. Most of the unknown kinetic and thermodynamic parameters of the model were estimated from experimental measurements using a stochastic optimization method based on a genetic algorithm. The results showed a fairly good agreement between model predictions and experiments. The model was then successfully validated through additional experiments carried out in batch and fedbatch reactors and clearly showed that the model was able to predict the time-evolution of overall conversion, amounts of each residual monomer, number and weight average molecular weights of the resulting copolymers and average diameters of the corresponding latex particles for different operating conditions, mainly CTA concentration and reaction temperature. The model was finally used to investigate and confirm the effects of CTA concentration, previously observed by several authors, on the kinetics of this polymerization process and on the main properties of the resulting macromolecules and latex particles.

This work deals with the application of a multicriteria optimization technique for the optimization of a jack pine pulping process, which has a large number of input and output variables. The optimization process is mainly comprised of two steps: reduction of the volume of the search space and determination of good compromise solutions. In the first step, the Pareto domain is approximated by a large number of possible solutions. The optimization technique uses the concept of preference, indifference and veto to determine the best solutions. Results show that an optimal solution zone can be defined and zones of decreasing preference can be drawn. On etudie dans ce travail l'application d'une technique d'optimisation a criteres multiples pour l'optimisation d'un procede de mise en pâte de sapin « Jack Pine », qui possede un grand nombre de variables d'entree et de sortie. Le procede d'optimisation comporte essentiellement deux etapes : la reduction du volume de l'espace de recherche et la determination de bonnes solutions de compromis. Dans la premiere etape, le domaine Pareto est approche par un grand nombre de solutions possibles. La technique d'optimisation utilise le concept de preference, d'indifference et de veto pour determiner les meilleures solutions. Les resultats montrent qu'une zone de solution optimale peut etre definie et que des zones de diminution de la preference peuvent etre tracees.

This contribution presents a new approach on weights determination in industrial decision making aided by OWA operators. Multi-criteria decision aid is a good way, for an industrialists, to determine his preferred compromise products, in the case of risk products or innovative products. The multi-criteria decision support chosen is the Ordered Weighted Average (OWA) operators, introduced by Yager [R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Syst. Man Cybern. 18 (1988) 183-190]. The interest of this aggregation method is, beyond its simplicity of use, product evaluation according unique scale. Furthermore, the weights are not fixed by criterion but according to utility level. First, a learning sample is ranked by the decision-maker. Then, this ranked sample is used in order to determine the weights by parametric identification. For this, an hypothesis of equipartition of the scores of each sample is used. An industrial application, from a food production, illustrates this approach. The ranks obtained from several samples are compared.

12th International Symposium on Process Systems Engineering (PSE) / 25th European Symposium on Computer Aided Process Engineering (ESCAPE), Copenhagen, DENMARK, MAY 31-JUN 04, 2015; International audience; A dynamic optimization frame work is used to produce in a controlled way multilayered latex nanoparticles. The key feature of the method is to track a glass transition temperature profile, which is designed to produce polymer layers with the targeted properties. Several constraints are considered to achieve better control and produce nanoparticles with a specified particle diameter and layers' thicknesses. To enhance the control of the different layers, two separate monomer feeds are considered under starving conditions throughout the fed-batch stages. The emulsion copolymerization of styrene and butyl acrylate in the presence of n-C12 mercaptan, as chain transfer agent (CTA), is illustrated here as a case study. The optimal feed profiles of the pre-emulsioned monomers are obtained using a genetic algorithm.

This paper deals with the modeling of the rheological behavior of polyethylene (PE)/supercritical CO 2 solutions. Melt viscosity measurements were performed using a capillary tube die attached to a twin screw extruder, for different amounts of CO 2 injected into the extruder barrel (from 0 to 4 wt%). The viscosity of the solutions was determined by measuring the pressure drop and the polymer flow rate through the die. A D-optimal experimental design was developed to determine the minimal number of experiments necessary with a minimization of the error on the model coefficients. The experimental results showed that the viscosity of PE / CO 2 solutions depends on shear rate, temperature, pressure, and CO 2 concentration. The pressure in the capillary was modeled by a second order polynomial. A theoretical model based on a power law was proposed to describe the pseudoplastic behavior of PE / CO 2 solutions with different shear rates. Correction factors were included to take into account the effects of temperature, pressure, and CO 2 concentration.

International audience; This paper is concerned by the industrial application of Ordered Weighted Averaging OWA operators, introduced by Yager. These operators allow to express the type of compromises, by the notion of linguistic quantifiers, such as "most" of criteria. The interest of this method of aggregation is, beyond its simplicity of use, its evaluation of products according a unique scale. Furthermore, the weights are not fixed by criteria but by levels of performance. In this paper we present a methodology of classification of products by two approaches. The first one is based on a learning sample and the second one on linguistic quantifiers. An industrial application, from a food production, illustrates these approaches. We then discuss the classifications obtained by these two approaches and we present a comparison.

This study deals with the development of an assisting tool, which helps the decision-making taking into account the human aspect of the decision-maker preferences. The proposed method is applied to the multicriteria optimisation of a reactive extrusion process.

This study deals with the development of a multicriteria optimization procedure and its application to emulsion polymerization processes for which several criteria, related to the productivity and to the quality of the final materials (in terms of end use properties), must be optimized simultaneously. An approach by Pareto domain determination and ranking by decision making support is presented.

The search of an experimental design based on D-optimality criterion to improve a bioprocess is proposed. The chosen model is a second-degree polynomial one selected for its satisfying precision leading to an accurate representation of non-linear systems with a low number of parameters. The optimization is carried out thanks to a real encoding diploid genetic algorithm developed within our laboratory. This study shows the setting of a multiobjective optimization procedure for bioprocesses having, in all cases, several criteria to optimize. A new approach by Pareto’s domain determination and ranking by Decision Engineering tool is presented.

Residence time distribution (RTD) measurements can be interpreted and simulated using compartment models which consist in interconnection of elementary modules, e.g. the plug flow reactor or the perfect mixing cell. However, the choice of the appropriate model may be a troublesome task when the considered reactor is of a complex geometry. The present investigation was aimed at developing a new algorithm for the automatic generation of compartment models based both on RTD curves and the physical description of the studied processes. We used for this purpose the possibility theory developed by Zadeh (Fuzzy Sets and Systems 1 (1978) 3), which consists in affecting weighing coefficients on the possible occurrence of a given event. The use of this method for chemical engineering has been proposed by Kraslawki (Chem. Eng. Process. 26 (1989) 185) but so far, it has been mainly tested in human sciences, as by Sandri and Bittencourt (1994) for investigations of associative human memories. In this context the compilation of existing work allowed the selection of nearly 40 models, which makes it possible to describe a large variety of industrial situations. Based on the RTD curves and process characteristics, a list of rules could be deduced. Weighing coefficients for possibility degree and necessity degree have been introduced to link the rules of the models. Finally, the so-called “fusion” procedure consisting of the compilation of all rules, leads to the selection of the most appropriate model. Addition of new rules or new models affects neither the structure of the software nor the “fusion” procedure. The software is very flexible and its evolutions are only limited by the user's expertise in chemical engineering. This paper deals mainly with the description of the algorithm and few examples of illustration.

To develop product within shorter deadlines remains a major challenge for the businesses. One of the possible answers is to capitalize "ready to use" expert knowledge. The choices of the customer being multiple, our research is oriented towards tolls and methods analysis and optimisation multi-criteria. In this paper, a generic approach and the Rough Sets Method (RSM) are briefly presented, which help the decision-making taking into account the human aspect of the decision maker preferences. This allows informal knowledge to be formalized based on the "preferences" and "no preferences" of experts. RSM is applied to an industrial study in a high yield pulping process and cheese production.

This study deals with the development of an assisting tool, which helps the decision-making taking into account the human aspect of the decision maker preferences. The proposed method is applied to the multicriteria optimisation of a reactive extrusion process.

The optimization of complex processes usually involves many competing objectives; in this case there is typically no solution that yields optimal values for all of the objective criteria and the decision-maker must therefore find a reasonable compromise. In recent years, new multicriteria methods have been developed to assist the practitioner in achieving a judicious compromise among the various competing objectives. One method, the rough set method (RSM), is able to encapsulate the preferences of an expert within a simple set of logical rules that are used to rank a large number of feasible solutions according to these preferences. The RSM was used in this investigation to determine the optimal operating region of a high yield pulping process. This pulping process has seven input variables that can be manipulated to optimize four objective criteria characterizing the product: brightness, specific refining energy, extractive content, and rupture length. Results show that an optimal solution zone can be easily defined and zones of decreasing preference can be drawn. This information is very useful to the practitioner for choosing the desired operating conditions and for analyzing the robustness of the preferred operating scenarios from a process control point of view.

International audience; In the present work, a comprehensive theoretical and experimental study is carried out on the anionic polymerization of lauryllactam in the presence of a macroactivator (alpha,omega dicarbamoyloxy caprolactam polydimethylsiloxane), which was carefully synthesized and characterized prior to its use. Following the developments of a previous study, a predictive model is developed on the basis of an analytical kinetic scheme that takes into account important branching and condensation side reactions, resulting in a number of different functional end-groups on the produced macromolecules. A series of batch polymerization experiments, carried out under different operating conditions in terms of temperature and macroactivator concentration, are used for the identification of the kinetic parameters of the model as well as for its subsequent validation. Through these experiments, it becomes apparent that the proposed model and kinetic mechanism are capable of describing the behavior of the system with accuracy.

Some of the most practical aspects of operations research to have appeared in the last decade are multiple decision criteria methodologies. With these new methods, conflicting objectives can be formally incorporated more naturally into the decision process and those solutions that offer the best compromise can be identified. These techniques can be used with advantage in engineering. This paper describes how these decision-making tools can help to optimise bioprocess operations. One particular optimisation algorithm, the net flow method, is described and used to determine the optimal operating condition for the production of gluconic acid. It is shown that these new tools, in addition to providing guidance to optimize the fermentation, also provide valuable information regarding the zone of possible solutions.

Multicriteria optimization of processes without particular knowledge is obviously of pratical interest. The usual way of dealing with such problems is based on choice of a structure for the model, which can be difficult without previous knowledge on the process. Consequently, this classical way does not seem to work properly in such conditions. This work describes an easy and wide using iterative methodology to solve the problem of the determination of an optimal zone of Pareto in which the decision maker picks up the working conditions corresponding to his choices. This new approach relies on the only available data, variation intervals for each variable and allows to minimize the number of experiments. This new method proves to be very accurate for the definition of an optimal working zone for the synthesis of a biosurfactant from lactose.

Silane grafting and water cross-linking of polypropylene (PP) are a recent method to modify its properties, such as melt strength, heat, and chemical resistance. This work aims at grafting silanes onto PP by reactive extrusion. The occurrence of the grafting of silane onto PP was confirmed by Fourier transform infrared (FTIR) and a method based on FTIR was developed to quantify the amount of polymerized silane and that of silane grafted onto PP. The molar mass of the silane-grafted PP and its melt viscosity were also measured. A multiobjective optimization strategy was used to study the effects of processing conditions on the quality of the silane-modified PP. It was concluded that to maximize the amount of silane grafted on PP and minimize the amount of polymerized silane and the decrease in PP chain scission, screw speed and barrel temperature should be low and feed rate high. POLYM. ENG. SCI., 2013. © 2013 Society of Plastics Engineers

International audience; The present paper proposes a new methodology to integrate expert judgement in new product development decision process. In particular, within the garment industry product evaluation data come mainly from judges or consumer panels. Treatment of aggregate data is difficult as some measures could seem to be contradictory. To deal with this issue the present paper proposes the application of a sequential fitting (SEFIT) approach to exploit information from the whole set of data. SEFIT methods, proposed originally by (Mirkin, 1990) attempt to explain the variability in the initial data (commonly defined by a sum of squares) through an additive decomposition terms in the model. In this case, data from expert's evaluation of a set of garment products, concerning six predetermined fashion themes (judge perception), are treated to determine the importance level of each criterion

Accurate estimation of the model parameters is required to obtain reliable predictions of the products end-use properties. However, due to the mathematical model structure and/or to a possible lack of measurements, the estimation of some parameters may be impossible. This paper will focus on the case where the main limitations to the parameters estimability are their weak effect on the measured outputs or the correlation between the effects of two or more parameters. The objective of the method developed in this paper is to determine the subset of the most influencing parameters that can be estimated from the available experimental data, when the complete set of model parameters cannot be estimated. This approach has been applied to the mathematical model of the emulsion copolymerization of styrene and butyl acrylate, in the presence of n-dodecyl mercaptan as a chain transfer agent. In addition, a new approach is used to better assess the true confidence regions and evaluate the accuracy of the parameters estimates in more reliable way.

In this paper, a new approach for optimization is presented. This approach tends to imitate principles of organic evolution processes as rules for an optimization procedure. The two techniques used, haploid and diploid algorithms, are based on different organic structure modelling approaches and both use genetic concepts such as population, recombination and mutation as evolution rules to guide the optimum search. The theoretical analysis of this stochastic approach, especially with regard to issues about the global convergence, is currently in progress. However, previous tests and those presented here validated the method. These genetic algorithms are presented and their performances are compared. The comparison pertains to the convergence characterized by the number of generations needed to reach the solution, and running times are also compared with respect to applications that limit search time severely. Several complex optimization problems involving concrete applications were studied with different tuning parameters. Examples were selected to show the behaviour of the two algorithms when faced with several optima and nonlinear constraints. A practical application deals with a hydrodynamical problem, where discrete and actual variables are simultaneously handled in the same parameter optimization problem.

International audience; This paper presents the implementation of two multicriteria optimization methods based on different approaches, namely, Rough Set Method (RSM) and Net Flow Method (NFM), to the manufacture by reactive extrusion of linear thermoplastic polyurethanes (TPUs), appropriate for medical applications. A preliminary study allowed determining the process operating conditions for which the polymerization time and the average residence time of the reactants in the extruder are of the same order of magnitude. Prior to the optimization, a neural network model able to predict with acceptable accuracy the effect of the operating conditions on the output process variables, was constructed and validated. This model was then used to determine, using Pareto's concept, a set of non-dominated solutions constituting Pareto's domain. These solutions were then ranked according to the preferences of a decision maker using NFM and RSM. This allowed providing the 10% highest ranked solutions of Pareto's domain and proposing a set of optimal operating conditions for the production, with the lowest energy consumption, of TPUs with targeted properties and high purity. Experimental validation runs carried out under similar operating conditions gave rise to criteria values confirming the su- perior performance of NFM, without rejecting, at the same time, the values obtained using RSM

Dans cet article, une nouvelle technique basee sur les principes des algorithmes genetiques est proposee pour traiter des problemes d'optimisation globale. Cette technique d'optimisation utilise des concepts tires de la genetique des populations tels que la taille de la population, la naissance, la mort, la mutation. Sa principale caracteristique est d'effectuer la recherche en operant sur des variables reelles. Apres une description de l'algorithme, des etudes de recherche d'optimum et leurs resultats sont presentes pour differentes fonctions. La facilite d'implantation de cette nouvelle methode la rend particulierement utile pour resoudre des problemes d'optimisation nonlineaire avec ou sans contraintes

New software sensors based on the Extended Kalman Filter technique have been developed for the monitoring of animal cell perfusion cultures. They use a kinetic model describing the growth, death and metabolism of hybridoma cells as a function of the medium composition. The model was initially validated on a batch culture and found to correctly predict the continuous perfusion culture kinetics, except for the production of ammonia and lactate. Using the measurement of a single component in the culture medium, in this case glucose, the Extended Kalman Filter provides an excellent evaluation of the time variation of the concentrations of living and dead cells, of glutamine and antibodies, during the whole perfusion culture for a retained cell density rising from 1 to 11 x 10(6) cells.ml-1 inside the reactor.

In polymer industry, engineers seek to obtain polymers with prescribed end-use properties, high productivities, and low cost. Thus, the optimization of a manufacturing process with all those goals and constraints belongs to a problem domain that aims to achieve the best trade-off possible. This article concerns the optimization of the batch emulsion polymerization of styrene and a- methylstyrene. An accurate model was developed to describe the complete patterns of the emulsion polymeri- zation. Key parameters of the model were identified on the basis of batch experimental data. The model was then used to simulate, under several operating conditions, the polymerization rate, the overall conversion of monomers, and the number and weight-average molecular weights. A multicriteria optimization approach based on an evolu- tionary algorithm and the concept of dominance from the Pareto frontier theory was used. Last, a decision aid system based on the Choquet integral was proposed to determine the optimal operating conditions with the pre- ferences of the decision maker taken into account. V C 2011 Wiley Periodicals, Inc. J Appl Polym Sci 120: 3421-3434, 2011

International audience; A multicriteria optimization approach based on an evolutionary algorithm has been developed to determine the optimal control policy for a fed-batch emulsion copolymerization reactor, particularly for styrene and butyl acrylate in the presence of n-C12 mercaptan as chain transfer agent. The process model was elaborated and validated experimentally in order to predict the global monomer conversion, the number and weight average molecular weights, the particle size distribution and the residual monomers mass fraction. The process objectives were to produce core-shell particles (hard core and smooth shell) with specific end-use properties and high productivity. This has been achieved by the maximization of the monomers overall conversion at the end of the process and the minimization of the error between the glass transition temperature and a designed profile subject to a set of operational constraints. The nondominated Pareto solutions obtained were ranked according to a decision making aid method based on a decision maker preferences and experience using multi-attribute utility theory. Finally, the best solution was implemented experimentally.

This paper deals with estimability analysis, parameter identification and experimental validation of a model developed for a batch reactor where the emulsion copolymerization of styrene and butyl acrylate in the presence of n-dodecyl mercaptan as a chain transfer agent takes place. Accurate estimation of the model parameters is required to obtain reliable predictions of the products end-use properties. However, due to the mathematical model structure and topossible lack of measurements, the estimation of some parameters may be impossible.The main limitations to the parameters estimability are their weak effecton the measured outputs andt he correlation between these effects. The objective of the method developed in this paper is to determine the subset of the most influencing parameters that can be estimated from the available experimental data when the complete set of model parameters cannot be estimated. In the case study, it was shown that only 21 parametersout ofthe 49involved in the model were estimable. The values of the remaining 28 non estimable parameters were taken either from previous studies or from literature. Moreover a new method has been developed to determine the confidence domain for the set of estimable and identified parameters.

International audience; A rheometer-reactor was designed to study the effect of the presence of multiwalled-carbon nanotubes (MWCNT) on the kinetics of the activated anionic polymerization of ε-caprolactam (AAPCL). The presence of the MWCNT slowed down the kinetics of the AAPCL. Moreover, the higher the MWCNT content, the more important its adverse effect on the kinetics of the AAPCL. On the other hand, the presence of the MWCNT could also adversely affect the molar mass of the resulting polyamide 6 when its content was high enough (about 1.0 wt % or higher). Possible mechanisms are discussed.