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Commercially important extrusion film casting (EFC) processes for manufacturing plastic films or sheets are hampered by several instabilities that severely limits their productivity. In this research we focussed on one important instability: the draw resonance that occurs during the EFC process mainly under extensional flow conditions. Draw resonance is the sustained periodic oscillations in the film dimensions, notably film width and thickness, when the process operates beyond a critical draw ratio (CDR). In this research our goal was to reduce this draw resonance instability by incorporating well dispersed nanoclay fillers in a base polymeric resin (such as a linear low density polyethylene – LLDPE) to determine how these nanocomposite (NC) formulations can prevent or reduce the draw resonance defect. EFC experiments were conducted on the base resin and on the NC formulations under non-isothermal conditions to determine the onset of the draw resonance experimentally. Conventional linear stability analysis was performed to determine the onset of the draw resonance defect numerically. Numerical predictions for the onset of draw resonance were in qualitative agreement with our experimental data. Our results showed that incorporating appropriate nanoclay concentrations in a base polymeric resin indeed enhanced the EFC process stability for those polymer formulations and thus can have important economic implications for processors.

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Drop size is a crucial parameter for the efficient design and operation of the rotating disc contactor (RDC) in liquid–liquid extraction. The current work focuses on providing local and global explanations for the prediction of the drop size in a rotating disc contactor (RDC). The Random Forest (RF) regression model is a robust machine learning algorithm that can accurately capture complex relationships in the data. However, the interpretability of the model is limited. In order to address the issue of interpretability of the developed RF model, in the current work, we employed Local Interpretable Model-Agnostic Explanations (LIME) of the predictions of the RF model. This provides both local and global views of the model and thereby helps one to gain insights into the factors influencing predictions. We have provided local explanations depicting the impact of different attributes on the prediction of the output for any given input example. We have also obtained global feature importance, providing the top subset of informative attributes. We have also developed local surrogate models incorporating second order attribute interactions. This has provided important information about the effect of interactions on the drop size prediction. By augmenting the random forest model with LIME, it is possible to develop a more accurate and interpretable model for estimating the drop size in RDCs, ultimately leading to improved performance and efficiency.