Summary▪We augment the well-known susceptible – infected – recovered – deceased (SIRD) epidemiological model to include vaccination dynamics, implemented as a piecewise continuous simulation. We calibrate this model to reported case data in the UK at a national level,▪Our modelling approach decouples the inherent characteristics of the infection from the degree of human interaction (as defined by the effective reproduction number,Re). This allows us to detect and infer a change in the characteristic of the infection, for example the emergence of the Kent variant,▪We find that that the infection rate constant (k) increases by around 89% as a result of the B.1.1.7 (Kent) COVID-19 variant in England,▪Through retrospective analysis and modelling of early epidemic case data (between March 2020 and May 2020) we estimate that ∼1.2M COVID-19 infections were unreported in the early phase of the epidemic in the UK. We also obtain an estimate of the basic reproduction number as,R0= 3.23,▪We use our model to assess the UK Government’s roadmap for easing the third national lockdown as a result of the current vaccination programme. To do this we use our estimated model parameters and a future forecast of the daily vaccination rates of the next few months,▪Our modelling predicts an increased number of daily cases as NPIs are lifted in May and June 2021,▪We quantify this increase in terms of the vaccine rollout rate and in particular the percentage vaccine uptake rate of eligible individuals, and show that a reduced take up of vaccination by eligible adults may lead to a significant increase in new infections.

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.

—An artificial intelligence based on a genetic algorithm to build chemical reaction network (CRN) from chemical species concentration data from batch reaction is introduced. This is achieved through a two level optimization approach. The first level constructs the CRN through combinations of stoichiometric coefficients of all chemical species and optimized using genetic algorithm. Second level determines the best estimate for the reaction rate constants for each of the reactions using a standard non-linear optimization algorithm. The process is repeated through a number of generations where the genetic algorithm will successively reduce the number of possibilities through elimination of poor CRNs (based on how closely the CRN is able to predict concentration profiles) and retaining and re-optimizing better CRNs. This system's capability is demonstrated on an experimental data for the reaction between trimethyl orthoacetate and allyl alcohol. The results show that the system is able to develop a CRN that when simulated provides an accurate model (model predictions matching experimental measurements) with little human intervention.

The automated inference of physically interpretable (bio)chemical reaction network models from measured experimental data is a challenging problem whose solution has significant commercial and academic ramifications. It is demonstrated, using simulations, how sets of elementary reactions comprising chemical reaction networks, as well as their rate coefficients, may be accurately recovered from non-equilibrium time series concentration data, such as that obtained from laboratory scale reactors. A variant of an evolutionary algorithm called differential evolution in conjunction with least squares techniques is used to search the space of reaction networks in order to infer both the reaction network topology and its rate parameters. Properties of the stoichiometric matrices of trial networks are used to bias the search towards physically realisable solutions. No other information, such as chemical characterisation of the reactive species is required, although where available it may be used to improve the search process.

Intensifying the boil up rate of azeotropic reactive distillation offers a novel and industrially viable means of increasing the rate of condensation reactions. The strategy can be as effective as using a catalyst and may lend itself to continuous operation. When applied to the condensation of carboxylic acids and amines such intensification provides an environmentally-benign route to a variety of amides. The impact of intensified azeotropic distillation on direct amidation using both a production scale batch reactor and a cascade of CSTRs is discussed and compared.

The direct synthesis of amides from the corresponding carboxylic acids and amines is shown to operate under varying degrees of mixed kinetic and mass transfer rate control when water is removed by azeotropic distillation. Unless the volumetric heat input rate is reported, it is not possible to make a valid comparison between different catalysts, as the difference in Q(boil) alone can be responsible for the apparent difference in observed rate. A systematic approach is developed to quantify the contribution of boil-up rate to conversion rate and so decouple the physical rates from the chemistry. Intensive boiling is used to improve the removal of water during azeotropic distillation and considerably enhance conversion. The results show that some acylations previously thought to be difficult or impossible can be achieved in the absence of coupling agents under green conditions. The use of a cascade of CSTR flow reactors operating under intensified conditions is assessed for scale up of direct amidation reactions and compared to a production scale batch reactor. The findings and conclusions of this work have general applicability to all condensation reactions.

For the communication by Gennadiy Ilyashenko, Andrew Whiting and Allen Wright, in Issue 11+12, 2010, pp 1818–1825 (DOI: 10.1002/adsc.201000391), the authors have found errors in the Acknowledgments and in Figure 3. The original versions need to be replaced with the following: Acknowledgements We thank the EPSRC for a grant (GR/S85368/01), Dr Katarina Novakovic, Ms Julie Parker and Mr S Walton for technical support, and Oxford Chemicals Ltd. for supplying thioacetic acid 1 and trans-2-hexenal 2a. Figure 3.1

Rather than proceeding through a Michael-type or 1,4-addition, thioacetic acid adds across unsaturated aldehydes in an autocatalytic manner and involving a double exotherm, as demonstrated by both adiabatic and reaction calorimetry NMR studies show that an intermediate acyl-thio-hemiacetal is involved and that the product continues to react competitively with thioacetic acid

This paper reports the influence of reaction temperature on the occurrence and characteristics of pH oscillations that are observed during the palladium-catalysed phenylacetylene oxidative carbonylation reaction in a catalytic system (PdI2, KI, air, NaOAc) in methanol. Isothermal experiments were performed over the temperature range 10-50 degrees C. The experiments demonstrate that oscillations occur in the range 10-40 degrees C and that a decrease in reaction temperature results in an increase in the period and amplitude of the pH oscillations. Furthermore, it is observed that during oscillations at any specific temperature, the time taken for pH to increase from a minimum to a maximum value varies with respect to reaction time. However, the time required for the pH to fall from maximum to new minimum is approximately constant with respect to the reaction time and is a function of the reaction temperature.

A new, more environmentally benign route to hydroxyethylsulfonylstyrene has been developed, starting from 4-bromobenzenethiol, involving a solventless thioether formation, water-based perborate oxidation, and Suzuki-Miyaura cross coupling with a vinylborate reagent.

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.

New insights into the mechanism for the transition metal-mediated oxidation of hydroxamic acids to give intermediate acyl nitroso species, with subsequent hetero-Diels-Alder trapping are presented. The activation of triphenylphosphine-ligated ruthenium-salen complexes is examined, and evidence is presented for the ruthenium-oxo species which are involved in the oxidative process of the hydroxamic acid. The observation of the lack of asymmetric induction involved in the intermolecular cycloaddition process involving the intermediate acyl nitrsoso species is explained, with the aid of comparing the ruthenium-salen-based systems with nitrosotoluene, and copper(I)/copper(II) BINAP-based catalysis of nitrosopyridine complexes. This study demonstrates the importance of secondary coordination to achieve asymmetric induction in nitroso-Diels-Alder reactions.

The objective of this work is to facilitate the determination of a reliable reaction network for the l-proline catalysed aldol reaction using high throughput technologies (HTT). The availability of reliable reaction network is fundamental to predictive kinetic modelling including scale up, replacing a batch process with a continuous one, optimisation, thermal safety, process simulation, etc. The extent to which HTT experiments may be used to provide the necessary quantitative understanding of both physical and chemical phenomena of the system studied is assessed through a four stage development programme. For this particular system it is shown that, in conjunction with appropriate experimental designs, meaningful data streams for mechanistic/kinetic studies can be generated. The experimental data allowed a reaction network for the l-proline catalysed aldol reaction to be proposed and verified through a kinetic modelling exercise.

The oxidative carbonylation of phenylacetylene was studied in order to determine the production distribution and reaction conditions which result in oscillatory versus non-oscillatory behaviour. Unambiguous identification of the products and their distribution was undertaken, after which, it was found that reaction conditions which result in oscillatory behaviour produce high conversions, and more selective product distribution.

This paper demonstrates how, in principle, a chemical reaction mechanism (reaction network) can be inferred using relatively simple systematic mathematical and statistical analyses of experimental data obtained from chemical reactors. This method involves specifying a global ordinary differential equation (ODE) model structure capable of representing an entire set of possible chemical reactions. Mathematical and statistical tests are then used to reduce the ODE model structure to a subset of reactions. Finally, a model rationalisation procedure, relying on exploiting the basic rules of reaction chemistry, is used to obtain a consistent set of reactions which are combined to give the overall reaction network. The identification procedure is demonstrated for pure batch operation with a worked example using simulated noisy data from an extended Van de Vusse reaction network consisting of five species and four elementary reactions [Van de Vusse, J.G., 1964. Plug-flow type reactor versus tank reactor. Chemical Engineering Science 19, 994–997]. A further case study of a semi-batch (fed batch) system using simulated data from a simplified biodiesel system, with six chemical species involved in three elementary reactions, is provided. It is shown that the method is able to correctly identify the underlying structure of the network of chemical reactions and provide accurate estimates of the network rate constants.

This paper demonstrates the identification of underlying nonlinear ordinary differential equation (ODE) models that represent chemical reaction networks. The proposed method uses species' concentration and rate of heat evolution data which can be obtained from a reaction calorimeter. The identification procedure is demonstrated for a simulated Van de Vusse reaction system. It is shown that accurate estimates of the network rate constants and individual heats of reactions may also be generated.

This article demonstrates, using simulations, the potential of the S-system formalism for the inference of unknown chemical reaction networks from simple experimental data, such as that typically obtained from laboratory scale reaction vessels. Virtually no prior knowledge of the products and reactants is assumed. S-systems are a power law formalism for the canonical approximate representation of dynamic non-linear systems. This formalism has the useful property that the structure of a network is dictated only by the values of the power law parameters. This means that network inference problems (e.g. inference of the topology of a chemical reaction network) can be recast as parameter estimation problems. The use of S-systems for network inference from data has been reported in a number of biological fields, including metabolic pathway analysis and the inference of gene regulatory networks. Here, the methodology is adapted for use as a hybrid modelling tool to facilitate the reverse engineering of chemical reaction networks using time series concentration data from fed-batch reactor experiments. The principle of the approach is demonstrated with noisy simulated data from fed-batch reactor experiments using a hypothetical reaction network comprising 5 chemical species involved in 4 parallel reactions. A co-evolutionary algorithm is employed to evolve the structure and the parameter values of the S-system equations concurrently. The S-system equations are then interpreted in order to construct a network diagram that accurately reflects the underlying chemical reaction network.