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Raman spectroscopy is used to determine the concentrations of monomers and polymers in the second stage of a two‐stage emulsion polymerization process. The indirect hard modeling (IHM) approach is applied to monitor inline the reaction in semi‐batch operation in lab scale and pilot scale. This method requires the spectra of all pure components. While the pure spectra of water and monomers are easily measured as pure components, the spectra of two different copolymers are constructed from reaction spectral data containing mixtures. Good predictive capabilities of the developed model are demonstrated. [ABSTRACT FROM AUTHOR]

The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential. We identify six challenges that will open new methods for CE and formulate new types of problems for ML: (1) optimal decision making, (2) introducing and enforcing physics in ML, (3) information and knowledge representation, (4) heterogeneity of data, (5) safety and trust in ML applications, and (6) creativity. Under the umbrella of these challenges, we discuss perspectives for future interdisciplinary research that will enable the transformation of CE. [ABSTRACT FROM AUTHOR]

Extensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the integration of machine learning into existing reduction approaches are discussed. First, key concepts for dynamic model reduction and their limitations are briefly reviewed. Afterwards, promising model structures for reduced hybrid mechanistic/data‐driven models are outlined. Finally, crucial future challenges as well as promising research perspectives are presented. [ABSTRACT FROM AUTHOR]

The common understanding of Power‐to‐X is exclusively the use of renewable electricity to manufacture products currently based on fossil sources. In this paper, it is argued that beyond such e‐Production many of these technologies also include aspects related to demand side management and temporal storage of electricity. Therefore, a definition of Power‐to‐X is suggested that encompasses all three aspects. It is discussed, which of these are relevant under which conditions and illustrative examples are highlighted, which show how process systems engineering can help address common challenges for Power‐to‐X technologies. [ABSTRACT FROM AUTHOR]

An event‐driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot‐plant reactors are presented. An event‐driven approach based on dynamic optimization and nonlinear model predictive control is investigated together with Raman spectroscopy for process monitoring. The benefits and challenges in morphology monitoring are presented together with the used mechanistic models. Results from experiments in lab and pilot‐plant reactors are shown. [ABSTRACT FROM AUTHOR]

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An abstract of the article "2-MTHF/Water as Medium for Multiphase Reaction Systems: Phase Equilibrium Data and Modeling," by M. Aigner is presented.