In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.

Drying of porous media is strictly governed by heat and mass transfer. However, contrary to the definition that drying is simultaneous transport mechanisms of heat and mass, most past and current models either account for temperature or concentration gradient effects on drying. Even though the complexity of computations of these processes varies with area of application, in most cases, the Dufour and Soret effects are neglected. This leads to deviations and uncertainties on the assumptions and interpretations of these and other relevant effects on drying. This paper covers the theoretical methods to derive the coupled transfer effects. In addition, this work proposes and formulates relevant heat and mass transfer equations, as well as the governing equations for drying processes with Dufour and Soret effects. The application of a numerical approach to solve the equations allows for studying of the influence of these effects on the design and operation of dryers. It is shown that the Soret effect can be highly relevant on drying operations with dynamic heating operation. While for drying processes where the steady state drying process predominates, the effect is deemed negligible.