Highlights This study empirically assesses the determinants of organizations’ intentions and organizations’ behaviors towards a circular economy for plastics. Attitudes and perceived behavioral control of corporate decision makers are the strongest predictors of organizations’ intentions and organizations’ behaviors respectively. Most organizations are willing to contribute to a circular economy for plastics, but they are failing to do so due to some critical barriers. This study suggests some measures to overcome this intention-behavior gap. Government or relevant institutions should provide incentives, disseminate knowledge, and establish networking platforms.

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The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

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