COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions.

Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.

Insights into the dynamics and control of COVID-19 infection rates.

This work aims to model, simulate and provide insights into the dynamics and control of COVID-19 infection rates. Using an established epidemiological model augmented with a time-varying disease transmission rate allows daily model calibration using COVID-19 case data from countries around the world. This hybrid model provides predictive forecasts of the cumulative number of infected cases. It also reveals the dynamics associated with disease suppression, demonstrating the time to reduce the effective, time-dependent, reproduction number. Model simulations provide insights into the outcomes of disease suppression measures and the predicted duration of the pandemic. Visualisation of reported data provides up-to-date condition monitoring, while daily model calibration allows for a continued and updated forecast of the current state of the pandemic.

Butanol production from lignocellulose by simultaneous fermentation, saccharification, and pervaporation or vacuum evaporation.

Techno-economic study of acetone, butanol and ethanol (ABE) fermentation from lignocellulose was performed. Simultaneous saccharification, fermentation and vacuum evaporation (SFS-V) or pervaporation (SFS-P) were proposed. A kinetic model of metabolic pathways for ABE fermentation with the effect of phenolics and furans in the growth was proposed based on published laboratory results. The processes were optimized in Matlab®. The end ABE purification was carried out by heat-integrated distillation. The objective function of the minimization was the total annualized cost (TAC). Fuel consumption of SFS-P using poly[1-(trimethylsilyl)-1-propyne] membrane was between 13.8 and 19.6% lower than SFS-V. Recovery of furans and phenolics for the hybrid reactors was difficult for its high boiling point. TAC of SFS-P was increased 1.9 times with supplementation of phenolics and furans to 3g/l each one for its high toxicity. Therefore, an additional detoxification method or an efficient pretreatment process will be necessary.