Over the past three decades, mixed-matrix membranes (MMMs), comprising an inorganic filler phase embedded in a polymer matrix, have emerged as a promising alternative to overcome limitations of conventional polymer and inorganic membranes. However, while much effort has been devoted to MMMs in practice, their modeling is largely based on early theories for transport in composites. These theories consider uniform transport properties and driving force, and thus models for the permeability in MMMs often perform unsatisfactorily when compared to experimental permeation data. In this work, we review existing theories for permeation in MMMs and discuss their fundamental assumptions and limitations with the aim of providing future directions permitting new models to consider realistic MMM operating conditions. Furthermore, we compare predictions of popular permeation models against available experimental and simulation-based permeation data, and discuss the suitability of these models for predicting MMM permeability under typical operating conditions.
We present a novel theory for estimation of the effective permeability of pure gases in flat mixed-matrix membranes (MMMs), in which effective medium theory (EMT) is extended to systems with finite filler size and membrane thickness. We introduce an inhomogeneous filler volume fraction profile, which arises due to depletion of the filler in regions adjacent to the membrane ends, into the MMM permeation model. In this way, the effective medium approach (EMA) can still be applied to systems where the dispersant size is not small in comparison to the membrane thickness, and for which a permeability profile arises in the MMM that is dependent on both filler size and membrane thickness, besides the filler-polymer equilibrium constant. It is found that increase in particle size reduces the effective membrane permeability at fixed membrane thickness, and that the effective membrane permeability increases with increase of the membrane thickness to asymptotically reach the value predicted by existing models. The present theory is validated against detailed simulations of the transport in MMMs, and theoretical predictions are found to be in agreement with those obtained from the exact calculations. Further, comparison of the exact effective permeability at different filler volume fractions is made for different packing configurations, showing variations in dispersant packing structure to have only a very weak effect on MMM performance.
This work presents
a methodology for scaling up batch processes
(BPs). First, the most popular scaleup methods differentiating batch
from continuous processing are reviewed, revealing that traditional
scaleup approaches do not consider BP characteristics and that many
particular successful cases are reported, but no formal procedure
has been developed for scaling up these processes. Considering these
facts, a novel scaleup procedure is presented, in which a process
phenomenological-based semiphysical model (PBSM) and its Hankel matrix
are used for computing the state impactability index (SII) that allow
the designer to determine (i) the main process dynamics at each stage
of the batch and (ii) the critical point of the operating trajectory
(OT) at which the batch must be scaled up. Finally, the methodology
is applied to a batch suspension polymerization reactor, comparing
the scaled unit design using this approximation and a traditional
method.
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.
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