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This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice.

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Dissolved biomethane in anaerobic effluents has long been a hurdle for energy harvesting through anaerobic wastewater treatment processes. Here, we present a novel membrane process for dissolved methane recovery through the normal range of domestic wastewater temperatures by utilizing an omniphobic (nonwetting) microporous membrane. In a process driven by a solubility gradient, dissolved methane is extracted from a methane-rich aqueous solution (feed), transported across the omniphobic membrane, and absorbed into a nonpolar organic solvent (draw) that has a high solubility for methane. We fabricated the omniphobic membrane by coating a microporous polymeric membrane with silica nanoparticles, followed by surface fluorination. Using the omniphobic membrane, we demonstrate that nearly ≥90% of dissolved methane is recovered from methane-saturated feedwater at 15, 25, and 35 °C, simulating anaerobic effluents produced in the psychrophilic to mesophilic temperature range, while negligible transport of water is observed. Further measurements and comparative energy analysis suggest that this novel process can enable net energy production, with a higher value at a lower temperature, which outperforms other dissolved methane recovery techniques.

The waves of COVID-19 infections driven by its variants continue to nullify the success we achieved through efficacious vaccines, social restrictions, testing and quarantine policies. This paper models the two major variants-driven waves by two sets of susceptible-infected-quarantined-recovered-vaccinated-deceased coupled dynamics that are modulated by the three main interventions: vaccination, quarantine and restrictions. This $$SI^2Q^2R^2VD$$ S I 2 Q 2 R 2 V D system is used to demonstrate that the second major novel coronavirus wave in the US is caused by the delta variant and the corresponding rapid surge in infectious cases is driven by the unvaccinated pool of the populace. Next, a feedback control based planned vaccination strategy is derived and is shown to be able to suppress the surge in infections effectively.

Mangiferin present in Curcuma amada was extracted with the help of microwave assisted extraction (MAE). The extraction solvent used was ethanol, which is eco-friendly and reduced the risk of environmental hazard. The mangiferin content was found to increase until 500 W, but decreased as the microwave power was increased further. A similar threshold was also obtained for microwave irradiation time. Following a mathematical analysis, an optimal mangiferin yield of 41 μg/mL was obtained from an extraction time of 15.32 s for a microwave power of 500 W.