This essay of the temporary Working Group “100% Digital” of the Association “Process, Apparatus and Plant Engineering” PAAT in ProcessNet addresses characteristics of a digital twin from the user's point of view and is intended to help solution providers to give product development a customer‐oriented direction. For this purpose, the different data models are described which play a role in the life cycles of chemical processes, plants, and products. In particular, for existing (brownfield) plants, essential aspects of the digital twin are subsequently formulated. Further expressions and consequences, e.g., on qualification and training, are deduced from the above.

Im internationalen Wettbewerb kann der Digitale Zwilling für die deutsche Industrie ein entscheidender Wettbewerbsvorteil sein. Dieser Essay adressiert Merkmale eines Digitalen Zwillings aus Anwendersicht und soll Lösungsanbietern helfen, der Produktentwicklung eine kundenorientierte Richtung zu verleihen. Dazu werden die unterschiedlichen Datenmodelle beschrieben, die in den Lebenszyklen von chemischen Prozessen, Anlagen und Produkten eine Rolle spielen. Insbesondere für bestehende (Brownfield‐)Anlagen werden abschließend wesentliche Aspekte des Digitalen Zwillings ausformuliert. Weitere Ausprägungen und Auswirkungen, z. B. auf die Aus‐ und Fortbildung, werden daraus abgeleitet.

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We introduce a gray‐box approach for modeling the molecular weight distribution in step‐growth polymerization reactions using the aggregation population balance equation. The approach is based on extracting a data‐based kernel function from in‐process measurements of the molecular weight distribution. The method is applied to historical data from an industrial batch polymerization reactor. The resulting model is used for decision support in production by predicting the reaction endpoint corresponding to a target molecular weight. The accuracy of the predictions proved to be sufficient for the deployment of the method.

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knowledge integrated into an engineering model that is validated with process and experimental data then can be used to determine where to intervene.An important mechanism is fouling associated with undesired polymerization. It is generally characterized by much longer time scales than the desired polymerization. Although polymer formation might be at the trace level, solubility issues can bring it to importance. Frequently, trace amounts of other chemicals are involved, such as catalyst, initiator or impurities. Typical examples are isocyanates which may react by itself to form undesired cyclic isocyanourates or cabodiimines, the trace polymerization of unsaturated monomers and the thermal initiation of styrene. A sound kinetic model approach combines both classical and theoretical techniques. Thermochemistry data, e.g., obtained by ab-initio quantum chemistry methods, spectral and reactivity data must be translated into polymerization kinetic type of information. A support-ing engineering model covers both overall process conditions and local phenomena like, e.g., skin temperatures, variation of process parameters and mixing conditions. For a successful evaluation and optimization of plant conditions, coupling of the kinetic polymerization model with the engineering model is necessary. These types of coupled models are routinely used in The Dow Chemical Company to intensify chemical processes by controlling fouling.

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