Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

Tannins were used as reinforcing components for polypropylene with anti-UV properties via dynamic curing extrusion. The influence of cross-linked tannins in different weight fraction and their anti-UV capacity on morphological, mechanical, rheological, crystallize and thermal properties were studied. The experimental results indicated that the cross-linked tannins improve Young’s modulus, crystallinity, and thermal stability and reinforce the internal network of polypropylene. After UV accelerated weathering, polypropylene had fewer surface cracks, lower carbonyl index, fewer crystallinity decreases and less mechanical properties loss with increasing tannin content.

This article deals with the development of a multicriteria analysis, and its application to the optimization of batch emulsion polymerization processes. This new approach in the domain of polymer reaction engineering illustrates how a multiobjective optimization aided by a genetic algorithm and using the Pareto concept of domination is useful. In this process (emulsion homopolymerization of styrene), several objectives were simultaneously required, e.g., a high quality of the resulting products together with a high productivity. The aim of this study was to find the optimal experimental conditions to obtain simultaneously the minimum reaction time and designed values for both average molecular weights and particles size. To do that, an adapted mathematical model, able to describe all the process physicochemical phenomena, was been first elaborated. The multicriteria analysis then gave a set of nondominated points with conflicting criteria. A decision support system was then developed and applied to rank the Pareto solutions set and to propose some good solutions by taking into account the decision maker's preferences. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 87: 2383–2396, 2003