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.

Smart Materials are along with Innovation attributes and Artificial Intelligence among the most used “buzz” words in all media. Central to their practical occurrence, many talents are to be gathered within new contextual data influxes. Has this, in the last 20 years, changed some of the essential fundamental dimensions and the required skills of the actors such as providers, users, insiders, etc.? This is a preliminary focus and prelude of this review. As an example, polysaccharide materials are the most abundant macromolecules present as an integral part of the natural system of our planet. They are renewable, biodegradable, carbon neutral with low environmental, health and safety risks and serve as structural materials in the cell walls of plants. Most of them are used, for many years, as engineering materials in many important industrial processes, such as pulp and papermaking and manufacture of synthetic textile fibres. They are also used in other domains such as conversion into biofuels and, more recently, in the design of processes using polysaccharide nanoparticles. The main properties of polysaccharides (e.g. low density, thermal stability, chemical resistance, high mechanical strength…), together with their biocompatibility, biodegradability, functionality, durability and uniformity, allow their use for manufacturing smart materials such as blends and composites, electroactive polymers and hydrogels which can be obtained 1) through direct utilization and/or 2) after chemical or physical modifications of the polysaccharides. This paper reviews recent works developed on polysaccharides, mainly on cellulose, hemicelluloses, chitin, chitosans, alginates, and their by-products (blends and composites), with the objectives of manufacturing smart materials. It is worth noting that, today, the fundamental understanding of the molecular level interactions that confer smartness to polysaccharides remains poor and one can predict that new experimental and theoretical tools will emerge to develop the necessary understanding of the structure-property-function relationships that will enable polysaccharide-smartness to be better understood and controlled, giving rise to the development of new and innovative applications such as nanotechnology, foods, cosmetics and medicine (e.g. controlled drug release and regenerative medicine) and so, opening up major commercial markets in the context of green chemistry.

3 Alma Berenice Jasso-Salcedo, Sandrine Hoppe, Fernand Pla, Vladimir Alonso Escobar-Barrios, 4 Mauricio Camargo and Dimitrios Meimaroglou 5 Instituto Potosino de Investigación Científica y Tecnológica, División Ciencias Ambientales, Camino a la Presa de 6 San José 2055, Col. Lomas 4a Sección. C.P. 78216, San Luis Potosí, S.L.P., México. 7 CNRS, Laboratoire Réactions et Génie des Procédés, Université de Lorraine UMR 7274, Nancy, F-54001, France. 8 Instituto Potosino de Investigación Científica y Tecnológica, División de Materiales Avanzados, Camino a la Presa 9 de San José 2055, Col. Lomas 4a Sección. C.P. 78216, San Luis Potosí, S.L.P., México. 10 Université de Lorraine, ERPI, Equipe de Recherche sur les Processus Innovatifs, EA 6737, Nancy, F-54001, France. 11 12 *Corresponding author e-mail address: dimitrios.meimaroglou@univ-lorraine.fr 13 1 Present address author AB Jasso-Salcedo: Department of Materials and Environmental Chemistry, Arrhenius 14 Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden. 15 16

A trilogy review, based on more than 300 references, is used to underline three challenges facing 1) the supply of sustainable, durable and protected biosourced ingredients such as lipids, 2) the accounting for valuable bio-by-products, such as whey proteins that have added-value potential removing their environmental weight and 3) the practical reliable synthetic biology and evolutionary engineering that already serve as a technology and science basis to expand from, such as for biopolymer growth. Bioresources, which are the major topic of this review, must provide answers to several major challenges related to health, food, energy or chemistry of tomorrow. They offer a wide range of ingredients which are available in trees, plants, grasses, vegetables, algae, milk, food wastes, animal manures and other organic wastes. Researches in this domain must be oriented towards a bio-sustainable-economy based on new valuations of the potential of those renewable biological resources. This will aim at the substitution of fossil raw materials with renewable raw materials to ensure the sustainability of industrial processes by providing bioproducts through innovative processes using for instance micro-organisms and enzymes (the so-called white biotechnology). The final stage objective is to manufacture high value-added products gifted with the right set of physical, chemical and biological properties leading to particularly innovative applications. In this review, three examples are considered in a green context open innovation and bigger data environment. Two of them (lipids antioxidants and milk proteins) concern food industry while the third (biomonomers and corresponding bioplastics and derivatives) relates to biomaterials industry. Lipids play a crucial role in the food industry, but they are chemically unstable and very sensitive to atmospheric oxidation which leads to the formation of numerous by-compounds which have adverse effects on lipids quality attributes and on the nutritive value of meat. To overcome this problem, natural antioxidants, with a positive impact on the safety and acceptability of the food system, have been discovered and evaluated. In the same context, milk proteins and their derivatives are of great interest. They can be modified by enzymatic means leading to the formation of by-products that are able to increase their functionality and possible applications. They can also produce bioactive peptides, a field with almost unlimited research potential. On the other hand, biosourced chemicals and materials, mainly biomonomers and biopolymers, are already produced today. Metabolic engineering tools and strategies to engineer synthetic enzyme pathways are developed to manufacture, from renewable feedstocks, with high yields, a number of monomer building-block chemicals that can be used to produce replacements to many conventional plastic materials. Through those three examples this review aims to highlight recent and important advancements in production, modification and applications of the studied bioproducts. Bigger data analysis and artificial intelligence may help reweight practical and theoretical observations and concepts in these fields; helping to cross the boarders of expert traditional exploration fields and sometime fortresses.

Artificial neural network (ANN) modeling was applied to study the photocatalytic degradation of bisphenol-A. The operating conditions of the Ag/ZnO photocatalyst synthesis and its performance were simultaneously modeled and subsequently optimized to target the highest efficiency in terms of the degradation reaction rate. Two ANN models were developed to simulate the stages of the photocatalyst synthesis and photodegradation performance, respectively. A direct dependence between the two networks was also established, thus making it possible to directly relate the degradation rate of the contaminant, not only to the photodegradation conditions, but also to the photocatalyst synthesis conditions. In this respect, an optimization study was carried out, by means of an evolutionary algorithm, in order to identify the optimal synthesis and photodegradation conditions that would result in the degradation of a maximal amount of the contaminant. Through this integrated approach it was demonstrated that neural network models can be proven valuable tools in the evaluation, simulation and, ultimately, the optimization of different stages of complex photocatalytic processes towards the maximization of the efficiency of the synthesized photocatalyst.

This paper deals with mathematical modeling and experimental validation of a fed-batch emulsion copolymerization reactor of styrene and butyl acrylate in the presence of n-C12 mercaptan as a chain transfer agent.. The model is used to predict the global monomers conversion, the average molecular weights, the particle size distribution and the amount of residual monomers. A subset of the most influential parameters of the model is determined using a parameter estimability approach and identified by minimizing the errors between the predicted and measured data. Some parameters are also obtained from literature. The model is then validated by batch and fed-batch experimental measurements.

ABSTRACTSilver‐modified ZnO particles (Ag/ZnO) are effective catalysts for the photodegradation of water pollutants such as bisphenol‐A. However, until now, their use in continuous processes was back‐drawn because of difficulties associated with their recovery. To overcome this problem, the present work aimed at immobilizing Ag/ZnO in cross‐linked poly(acrylic acid) ‐PAA‐. Ag/ZnO was first silanized using (3‐glycidyloxypropyl)trimethoxysilane and thoroughly dispersed in a water‐acrylic acid solution. The suspension was then submitted to radical polymerization in presence of a cross‐linker (N,N′‐Methylenebisacrylamide). The resulting composites were characterized in terms of chemical structure, morphology, crystallinity, thermal properties, and photostability. Their analyses showed that the silanized particles were chemically anchored to PAA and homogeneously distributed in the matrix. UV‐assisted photocatalysis of bisphenol‐A aqueous solutions showed that immobilized Ag/ZnO can achieve photodegradation performances comparable to pure Ag/ZnO and allows its use in successive cycles and, consequently, in continuous processes. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43528.

Aftersetting the ground of the quantum innovation potential of biosourced entitiesand outlining the inventive spectrum of adjacent technologies that can derivefrom those, the current review highlights, with the support of Bigger Dataapproaches, and a fairly large number of articles, more than 250 and 10,000patents, the following. It covers an overview of biosourced chemicals andmaterials, mainly biomonomers, biooligomers and biopolymers; these are producedtoday in a way that allows reducing the fossil resources depletion anddependency, and obtaining environmentally-friendlier goods in a leaner energyconsuming society. A process with a realistic productivity is underlined thanks to the implementation of recent andspecifically effective processes where engineered microorganisms arecapable to convert natural non-fossil goods, at industrial scale, into fuelsand useful high-value chemicals in good yield. Those processes, furtherdetailed, integrate: metabolic engineering involving 1) system biology, 2) syntheticbiology and 3) evolutionary engineering. They enable acceptable productionyield and productivity, meet the targeted chemical profiles, minimize theconsumption of inputs, reduce the production of by-products and furtherdiminish the overall operation costs. As generally admitted the properties ofmost natural occurring biopolymers (e.g., starch, poly (lactic acid), PHAs.)are often inferior to those of the polymers derived from petroleum; blends andcomposites, exhibiting improved properties, are now successfully produced.Specific attention is paid to these aspects. Then further evidence is providedto support the important potential and role of products deriving from thebiomass in general. The need to enter into the era of Bigger Data, to grow andincrease the awareness and multidimensional role and opportunity of biosourcingserves as a conclusion and future prospects. Although providing a largereference database, this review is largely initiatory, therefore not mimickingprevious classic reviews but putting them in a multiplying synergisticprospective.

Nowadays, the challenge of understanding relationships between catalysts properties and performance in the context of heterogeneous catalysis is a hot topic. Indeed, catalytic processes are generally affected by many different operational parameters that need to be modeled and optimized. The challenge can be addressed using artificial neural networks due to their flexibility to work without mathematical description of the process. The present work enters within the framework of the photodegradation of water contaminants using ZnO-based catalysts. ZnO is a non-toxic cheap material with an interesting photocatalytic potential. However, its application is reduced because of its poor efficiency, photocorrosion and difficulties for recovery. The objective of this work is to improve this efficiency, regarding particularly the photodegradation of an endocrinal disruptor: bisphenol-A (BPA), via the synthesis of a new catalytic system based on ZnO and the modeling of both the synthesis process and photocatalytic performance of this new catalytic system. Modeling and optimization will be carried out using artificial neural network tools coupled to an evolutionary algorithm. The connection between the two artificial neural network models will make it possible to identify the optimal synthesis parameters that lead to the maximum photocatalytic efficiency (within the studied domain), thus shedding light on the association of the system structure with its photocatalytic performance.

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.

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 (α,ω−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.

This review provides a critical overview of the recent methods and processes developed for the production of cellulose nanoparticles with controlled morphology, structure and properties, and also sums up (1) the processes for the chemical modifications of these particles in order to prevent their re-aggregation during spray-drying procedures and to increase their reactivity, (2) the recent processes involved in the production of nanostructured biomaterials and composites. The structural and physical properties of those nanocelluloses, combined with their biodegradability, make them materials of choice in the very promising area of nanotechnology, likely subject to major commercial successes in the context of green chemistry. With a prospective and pioneering approach to the subject matter, various laboratories involved in this domain have developed bio-products now almost suitable to industrial applications; although some important steps remain to be overcome, those are worth been reviewed and supplemented. At this stage, several pilot units and demonstration plants have been built to improve, optimize and scale-up the processes developed at laboratory scale. Industrial reactors with suitable environment and modern control equipment are to be expected within that context. This review shall bring the suitable processing dimension that may be needed now, given the numerous reviews outlining the product potential attributes. An abundant literature database, close to 250 publications and patents, is provided, consolidating the various research and more practical angles.

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

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.

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 superior performance of NFM, without rejecting, at the same time, the values obtained using RSM.

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 to possible lack of measurements, the estimation of some parameters may be impossible. The main limitations to the parameters estimability are their weak effect on the measured outputs and the 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 parameters out of the 49 involved 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.