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.

Development of a biogas distribution model for a wastewater treatment plant: a mixed integer linear programming approach.

In this paper, we propose a realistic model for gas distribution of an advanced municipal wastewater treatment works and through minimisation of the total cost of gas distribution we perform retrospective optimisation (RO) using historical plant data. This site is the first in the UK with a mixed operational strategy for biomethane produced on site: to burn in combined heat and power (CHP) engines to create electricity, burn in steam boilers for onsite steam use or inject the biomethane into the National Grid. In addition, natural gas can be imported to make up shortfalls in biomethane if required. Implemented using a novel mixed integer linear programming (MILP) approach, to ensure a fast and robust solution, our results indicate the plant operated optimally within accepted tolerance 98% of the time. However, improving plant robustness (such as reducing unexpected breakdown incidents) could yield a significant increase in gas revenue of 7.8%.

An automated computational approach to kinetic model discrimination and parameter estimation.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

Two-dimensional simple proportional feedback control of a chaotic reaction system.

The simple proportional feedback (SPF) control algorithm may, in principle, be used to attain periodic oscillations in dynamic systems exhibiting low-dimensional chaos. However, if implemented within a discrete control framework with sampling frequency limitations, controller performance may deteriorate. This phenomenon is illustrated using simulations of a chaotic autocatalytic reaction system. A two-dimensional (2D) SPF controller that explicitly takes into account some of the problems caused by limited sampling rates is then derived by introducing suitable modifications to the original SPF method. Using simulations, the performance of the 2D-SPF controller is compared to that of a conventional SPF control law when implemented as a sampled data controller. Two versions of the 2D-SPF controller are described: linear (L2D-SPF) and quadratic (Q2D-SPF). The performance of both the L2D-SPF and Q2D-SPF controllers is shown to be superior to the SPF when controller sampling frequencies are decreased. Furthermore, it is demonstrated that the Q2D-SPF controller provides better fixed point stabilization compared to both the L2D-SPF and the conventional SPF when concentration measurements are corrupted by noise.

Intensified design of experiments for upstream bioreactors.

Statistical Design of Experiments (DoE) is a widely adopted methodology in upstream bioprocess development (and generally across industries) to obtain experimental data from which the impact of independent variables (factors) on the process response can be inferred. In this work, a method is proposed that reduces the total number of experiments suggested by a traditional DoE. The method allows the evaluation of several DoE combinations to be compressed into a reduced number of experiments, which is referred to as intensified Design of Experiments (iDoE). In this paper, the iDoE is used to develop a dynamic hybrid model (consisting of differential equations and a feedforward artificial neural network) for data generated from a simulated Escherichia coli fermentation. For the case study presented, the results suggest that the total number of experiments could be reduced by about 40% when compared to traditional DoE. An additional benefit is the simultaneous development of an appropriate dynamic model which can be used in both, process optimization and control studies.

The influence of oscillations on product selectivity during the palladium-catalysed phenylacetylene oxidative carbonylation reaction.

This paper reports on the influence of oscillations on product selectivity as well as the dynamics of product formation during the palladium-catalysed phenylacetylene oxidative carbonylation reaction in a catalytic system (PdI2, KI, Air, NaOAc in methanol). The occurrence of the pH oscillations is related to PdI2 granularity and the initial pH drop after phenylacetylene addition. To achieve pH and reaction exotherm oscillations regulation of the amount of PdI2 is required, ensuring that the initial pH does not fall significantly below 1 after phenylacetylene addition. Experiments in both oscillatory and non-oscillatory pH regimes were performed in an HEL SIMULAR reaction calorimeter with the concentration-time profiles measured using a GC-MS. It is demonstrated that when operating in an oscillatory pH regime product formation may be suppressed until oscillations occur after which there is a steep increase in the formation of Z-2-phenyl-but-2-enedioic acid dimethyl ester. When operating in non-oscillatory pH mode the products are formed steadily over time with the main products being Z-2-phenyl-but-2-enedioic acid dimethyl ester, 2-phenyl-acrylic acid methyl ester and E-3-phenyl-acrylic acid methyl ester.