A numerical study of aniline production by hydrogenation of nitrobenzene (NBH) and hydrogen production by steam methane reforming (SMR) in a directly coupled membrane reactor is developed. This membrane reactor was proposed aiming to decarbonize heating in SMR and to favor the recovery of all products. Aniline recovery is improved in this reactor as water, a byproduct in NBH, is consumed in SMR. The simulation is performed using a heterogeneous-one dimensional model (Dusty gas model) and results are compared against the homogeneous model. The operating conditions of the reactor were selected using a multi-objective optimization method, genetic algorithms. The aims of the optimization were: methane conversion maximization, minimum membrane area, minimum reactor size, hydrogen yield maximization, nitrobenzene conversion maximization and the maximization of hydrogen recovery. This process was able to achieve complete conversion of methane and nitrobenzene. The hydrogen yield achieved can be as high as the maximum (∼4). 35% of this hydrogen was used as a reactant for aniline production. 99% of the unreacted hydrogen was recovered and purified. As the steam flow was minimized, aniline was obtained with a molar composition (70%), 2.1 times higher than that obtained in a conventional process for aniline production (33%). CO2 was obtained with a purity of 97%, hence, CO2 carbon capture and storage techniques were also favored. In addition, the energy requirements of heating of feedstock, reaction and recovery system of this novel process was 2.7 times lower than that of conventional processes carried out independently.

Ethyl acetate production by esterification and bioethanol recovery from dilute solutions (11 wt %) are energyintensive processes. Although ethanol is a raw material for ethyl acetate production, both technologies are usually studied independently. Here, a cleaning coupled process was proposed. The dilute stream of ethanol was fed to a reactive distillation column, so reflux and columns for dehydration of ethanol were not required. This increases the steam flow at the bottoms of the reactive zone. Steam was found to displace the equilibrium allowing a total recovery of products in the reactive distillation column, ethyl acetate (top) and water (bottoms). So, it was found that, counterintuitively, steam favors the recovery of ethyl acetate, avoiding the over-recycle of raw materials such as acetic acid. An extractive distillation scheme using dimethyl sulfoxide (DMSO) was also proposed to minimize the energy requirements for the purification of a mixture of ethanol, water, and ethyl acetate. The decision tree model was used to estimate the importance of operating variables on total annualized costs. The operating conditions were selected considering steady-state multiplicities. The energy intensity was further decreased by heat integration with vapor compression from 2.4 to 2.8 to 0.9-1.4 MJ/kg-ethyl acetate. The scheme proposed in this work was found to be a novel intensified process as the energy intensity of the scheme proposed was between three and seven times lower than that reported previously in the literature.

This paper proposes a realistic model for energy and carbon management of an advanced municipal wastewater treatment works. Through minimisation of total cost of operations, it provides operators with a visual daily operational schedule based on varying tariffs. This site is the first in the UK with a mixed operational strategy for biomethane produced on site: to burn in CHP (Combined Heat and Power) engines to create electricity, burn in Steam Boilers for onsite steam use or inject the biomethane into the gas distribution network - Natural Gas can be imported to make up shortfalls in biomethane if required. Implemented using a novel mixed integer linear programming (MILP) approach, results indicate that biomethane injection should be maximised for the highest financial gain - the driving force for optimising the remaining operations being the site electricity imports and whether the electricity imported 'generates' carbon emissions. Based on the source of electricity and the new carbon emissions performance criteria, under the current operational strategy importing electricity from carbon-based sources has no tangible impact on site revenues (but does impact CO2 emissions), however carbon free (renewable) electricity sources could see shift in operations leading a revenue increase of 12%

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.

Highlights1. Wastewater treatment sites produce biomethane that can be used to create renewable energy2. Optimisation of on-site biomethane and energy distribution and carbon emissions using MILP3. Varying electrical tariffs result in an optimal daily operational schedule, with an investigation into effect on renewably sourced electricity on site revenues4. Biomethane Injection primary driver of revenues

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.

We herein report a novel kinetic modelling methodology whereby identification of the correct reaction model and kinetic parameters is conducted by an autonomous framework combined with transient flow measurements to enable comprehensive process understanding with minimal user input. An automated flow chemistry platform was employed to initially conduct linear flow-ramp experiments to rapidly map the reaction profile of three processes using transient flow data. Following experimental data acquisition, a computational approach was utilised to discriminate between all possible reaction models as well as identify the correct kinetic parameters for each process. Species that are known to participate in the process (starting materials, intermediates, products) are initially inputted by the user prior to flow ramp experiments, then all possible model candidates are compiled into a model library based on their potential to occur after mass balance assessment. Parallel computational optimisation then evaluates each model by algorithmically altering the kinetic parameters of the model to allow convergence of a simulated kinetic curve to the experimental data provided. Statistical analysis then determines the most likely reaction model based on model simplicity and agreement with experimental data. This automated approach to gaining full process understanding, whereby a small number of data-rich experiments are conducted, and the kinetics are evaluated autonomously, shows significant improvements on current industrial optimisation techniques in terms of labour, time and overall cost. The computational approach herein described can be employed using data from any set of experiments and the code is open-source.

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.

The acetone-butanol-ethanol (ABE) fermentation from corn stover was considered. We propose, study and optimise, via process simulation (using MATLAB (R) and Aspen Plus (R)) the use of fermentation tanks in-series and in situ product recovery by vacuum evaporation. As the operating time of continuous fermentation processes is usually limited at less than 500 h, shutdown and start-up of the reactors are considered and optimised. A multi-objective optimisation methodology that considers economics as well as the energy requirements of the process was used. The optimal configuration was found to be five fermentation tanks-in-series where the first and the last reactors are operated at atmospheric pressure, and the intermediate vessels are operated under vacuum. The economic potential using this configuration was found to be 45% higher than that of vacuum fermenters operating in parallel (continuous operating mode). Also, the total fuel requirements for ABE recovery and purification system were as low as 7 MJ kg(-1) butanol, a reduction of between 4 and 33% when compared to a parallel configuration of batch, fed-batch or continuous fermenters. The energy efficiency of this recovery and reaction system was as high as 74% when co-generation is considered. (C) 2020 Elsevier Ltd. All rights reserved.

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%.

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.

We develop a novel hybrid epidemiological model and a specific methodology for its calibration to distinguish and assess the impact of mobility restrictions (given by Apple's mobility trends data) from other complementary non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19. Using the calibrated model, we estimate that mobility restrictions contribute to 47 % (US States) and 47 % (worldwide) of the overall suppression of the disease transmission rate using data up to 13/08/2020. The forecast capacity of our model was evaluated doing four-weeks ahead predictions. Using data up to 30/06/20 for calibration, the mean absolute percentage error (MAPE) of the prediction of cumulative deceased individuals was 5.0 % for the United States (51 states) and 6.7 % worldwide (49 countries). This MAPE was reduced to 3.5% for the US and 3.8% worldwide using data up to 13/08/2020. We find that the MAPE was higher for the total confirmed cases at 11.5% worldwide and 10.2% for the US States using data up to 13/08/2020. Our calibrated model achieves an average R-Squared value for cumulative confirmed and deceased cases of 0.992 using data up to 30/06/20 and 0.98 using data up to 13/08/20.

This chapter reviews different methods of inferring system descriptions – models – from data, and discusses their merits in light of data requirements and consideration of available a priori knowledge, which can describe either the structure of interactions and/or the way/form of the occurring interactions. It focuses on hybrid semiparametric modeling. Hybrid models combine fundamental models with data-driven or heuristic-driven models. The attractive traits of hybrid models are their good extrapolation capabilities, low data requirements, and transparent structure, which helps to develop process understanding. All of these properties stem from the incorporated fundamental knowledge. The chapter describes two applications to demonstrate how hybrid models can be used to solve problems that are not easily solved using fundamental understanding alone, but where the fundamental backbone can be exploited by combining it with neural networks.

Reliable kinetic models are important for the design and optimisation of lignocellulosic ethanol production. A new kinetic model describing ethanol production using Zymomonas mobilis ZM4(pZB5) that is applicable to single (glucose or xylose) or mixed (glucose and xylose) substrate fermentations is developed. This work extends previous contributions through consideration of an empirical term for the preferential usage of glucose when compared to xylose as well as the prediction of the production rate of xylitol. Kinetic model parameters are obtained through kinetic fitting using experimental data available in the literature (seventeen fermentations in batch or continuous operation). The average coefficient of determination of xylose, xylitol, ethanol, glucose and biomass predictions using the kinetic model developed in this work was 0.984. Furthermore, it is demonstrated that through the inclusion of xylitol production and the inhibition of xylose consumption by xylitol in the kinetic model the productivity of ethanol may be accurately estimated.

This paper considers the economic assessment and optimisation of a bioethanol production process using corn stover (CS) as the feedstock. This includes a comparison between the use of batch and fed-batch reactors with and without deacetylation. As a basis of the study, a kinetic model describing the co-fermentation of substrates producing ethanol using Zymomonas mobilis is proposed. The model extends work available in the literature to include acetate inhibition. The reported optimisation studies include realistic variations in feedstock quality, deacetylation, a mechanical pre-treatment stage and a green recovery system: extractive distillation with vapour compression. Results indicate that the use of fed-batch reactors using a deacetylation stage achieves an ethanol yield of between 267 and 334 L/ton dry basis of CS and economic potential of between 0.4 and 5.5 MM USD/year higher than the use of batch reactors. This also has the lowest energy requirements in the product recovery stage (3.2-3A MJ-fuel/kg-ethanol or 1.6-1.8 MJ/kg-ethanol). Omitting de-acetylation prior to hydrolysis/cofermentation increases the minimum ethanol selling price and energy requirements by similar to 3-14% and similar to 8-30%, respectively.

This paper demonstrates how the stoichiometry and kinetic model of a chemical synthesis involving multiple reactions can be selected via a computational approach which uses consecutive optimisation steps. First, a list of all feasible stoichiometric relations consistent with the molecular weights or the elemental makeup of participating species is developed using integer linear programming ( ILP). A second ILP is then used to construct all plausible stoichiometric schemata ( combinations of the stoichiometric equations) which are used to instantiate kinetic model structures. Using a numerical integration routine, the models are simulated and unknown parameters estimated using an iterative optimisation algorithm. Produced model structures are then numerically scored, ranked and compared. This allows selection between competing models using both physical and the statistical evidence the data provides. The methods are demonstrated using synthetic and experimental data sets assuming liquid-phase reactions occurring in a well-mixed isothermally operated batch reactor. (C) 2018 Elsevier Ltd. All rights reserved.

Butanol production from corn stover via (acetone-butanol-ethanol) ABE fermentation under vacuum was studied in this work. The reactor operating strategies considered were batch, fed-batch and continuous. The integrated reactor and vacuum separation process includes energy integration by a heat-pump system as well. A mathematical model describing the dynamics of the integrated reactors and the network of compressors and heat exchangers has been developed. The dynamic process models were used to select the optimum production strategy using economic optimisation where a methodology to determine the effect of scheduling of parallel reactor operation on the sizing of the heat-pump system was developed. The results suggest that the optimal operating mode for the integrated reaction system was fed-batch. Although the fed-batch process had the highest economic potential (37.8 MM USD), the compressor work for batch process operation (36.6 MM USD) was the lowest (1.8 MJ/kg ABE, 18% lower than that of fedbatch). Continuous process operation demonstrated the lowest economic potential (23.8 MM USD) and the highest compression work (2.87 MJ/kg ABE). The energy requirements of the purification system, a double-effect distillation process, were found to be between 3.45 and 4.14 MJ/kg ABE. (C) 2018 Elsevier Ltd. All rights reserved.

A kinetic model describing acetone-butanol-ethanol (ABE) production applicable to both single substrate fermentations of glucose and xylose as well as co-fermentation of the substrates has been developed. The model accounts for carbon catabolite repression as well as the inhibition of kinetic rates at high substrate concentrations (similar to 90 gl(-1)). Model parameters were obtained through kinetic fitting to previously report experimental data. The model was used to study the design and operation of a continuous ABE fermentation process (with and without recycle of biomass). For continuous operation, it was shown that multiple steady-states exist at low dilution rates. For operation with recycle of biomass, the influence of recycle rate on both biomass concentration and ABE productivity were studied. The results indicate that for a range of recycle and dilution rates, ABE productivity can increase to 16 gl(-1) h(-1) (10 times higher than that without biomass recycling), consistent with experimental results. (C) 2018 Elsevier B.V. All rights reserved.

In this work, a hybrid semi-parametric modelling framework implemented using mixed integer linear programming (MILP) is used to extract (coupled) nonlinear ordinary differential equations (ODEs) from process data. Applied to fed-batch (bio) chemical reaction syftems, unknown (or partially known) system connectivity and/or reaction kinetics are represented using a multivariate rational function (MRF) superstructure. The MRF's are embedded within an ODE framework which is used to incorporate known system model characteristics. Using derivative estimation, the ODEs are decoupled and a MILP algorithm is then used to identify appropriate constitutive model terms using sparse regression. Superstructure sparsity is promoted using a L-0- pseudo norm penalty, i.e. the cardinality of the model parameter vector, enabling the simultaneous yet decoupled identification of the parameters and model structure discrimination. Using simulated data, two case studies demonstrate a principled approach to hybrid model development, distilling unknown elements of (bio) chemical model structures from process data. (C) 2017 Elsevier Ltd. All rights reserved.