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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%). CO 2 was obtained with a purity of 97%, hence, CO 2 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. [Display omitted] • Operating conditions were selected through a multi objective optimization. • Energy requirements were reduced 2.7 times. • The recovery of the final products (aniline, hydrogen, and CO2) was favored. • Complete conversion of reactants was achieved. • A hydrogen efficiency of 99% was achieved. [ABSTRACT FROM AUTHOR]

The acetone-butanol-ethanol (ABE) fermentation from corn stover was considered. We propose, study and optimise, via process simulation (using MATLAB® and Aspen Plus®) 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. Image 1 • The maximum acetone-butanol -ethanol yield from detoxified corn stover was 270 L ton−1. • Energy requirements for the recovery as low as 7 MJ kg−1 were achieved. • The strategy of start-up/shutdown increased the economic potential more than 3 MM$/year. • Atmospheric operation of the first and the last reactors reduce compression works by ∼12%. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • A new kinetic model describing ethanol production using Zymomonas mobilis ZM4(pZB5). • The significance of the preferential usage of glucose when compared to xylose. • Kinetic fitting using individual (xylose or glucose) and co-fermentation experimental data. • Accurate estimation of the productivity of ethanol. [ABSTRACT FROM AUTHOR]

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 (∼90 g l −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 g l −1  h −1 (10 times higher than that without biomass recycling), consistent with experimental results. [ABSTRACT FROM AUTHOR]

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 systems, 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. [ABSTRACT FROM AUTHOR]

The manual determination of chemical reaction networks (CRN) and reaction rate equations is cumbersome and becomes workload prohibitive for large systems. In this paper, a framework is developed that allows an almost entirely automated recovery of sets of reactions comprising a CRN using experimental data. A global CRN structure is used describing all feasible chemical reactions between chemical species, i.e. a superstructure. Network search within this superstructure using mixed integer linear programming (MILP) is designed to promote sparse connectivity and can integrate known structural properties using linear constraints. The identification procedure is successfully demonstrated using simulated noisy data for linear CRNs comprising two to seven species (modelling networks that can comprise up to forty two reactions) and for batch operation of the nonlinear Van de Vusse reaction. A further case study using real experimental data from a biodiesel reaction is also provided. [ABSTRACT FROM AUTHOR]

Ethyl acetate production by esterification and bioethanol recovery from dilute solutions (11 wt %) are energy-intensive 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. [Display omitted] • Ethyl acetate was produced from dilute ethanol in a novel intensified scheme. • The lowest energy intensity up to date for ethyl acetate production was reported. • Steady-state multiplicities for extractive distillation were found. • Vapor compression technology reduced the energy intensity by 42%. • The coefficient of performance of vapor compression was between 6.1 and 6.7 [ABSTRACT FROM AUTHOR]

In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used in this work. [Copyright &y& Elsevier]

• Established chemical engineering modelling practice applied to COVID 19 Re estimation. • Effective reproduction number modelled as time varying stoichiometry in kinetic model. • Model uses piecewise continuous integration and event and discontinuity management. • Nested optimiser algorithm to estimate Re from data with time varying NPIs. • Calibration and estimation of non-linear response in Re to NPIs throughout epidemic. 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. [ABSTRACT FROM AUTHOR]

• Fed-batch operation achieves a minimum ethanol selling price (MESP) of 2–2.3 $/gal. • De-acetylation increases the final ethanol titer peak and reduces the MESP. • Optimal reactor operating conditions vary with respect to the corn stover quality. • Pre-hydrolysis is not required for fed-batch reactor operation to reduce MESP. • A new kinetic model incorporating acetate inhibition has been developed. 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 (3.2–3.4 MJ-fuel/kg-ethanol or 1.6–1.8 MJ/kg-ethanol). Omitting de-acetylation prior to hydrolysis/co-fermentation increases the minimum ethanol selling price and energy requirements by ~3–14% and ~8–30%, respectively. [ABSTRACT FROM AUTHOR]

Highlights • ABE fermentation under vacuum (batch, fed-batch and continuous operation). • Selection of the optimum operating mode via economic optimisation. • The scheduling of the dynamic reactors minimised the size of the heat-pump system. • Fed-batch operation is the most economical choice. • Continuous operation has the highest productivity and energy consumption. Abstract 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 fed-batch). 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. [ABSTRACT FROM AUTHOR]

Highlights • Successful selection of appropriate kinetic model structure(s) using experimental data. • A multi-layered (consecutive) optimisation framework automates and complements established kinetic fitting practice. • An ILP approach to systematically develop a large set of feasible reaction schemata. • Effective model structure discrimination using statistical metrics and Monte Carlo simulation. Abstract 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. Graphical Abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

The construction and application of a multiple nitrogen liquefier unit (NLU) optimal scheduling tool is discussed. Constrained by customer demands and subject to electricity spot market prices over a week-ahead horizon, a retrospective optimiser (RO) determines the minimum scheduling costs. Plant start-up penalties and inter-site optimisation capabilities are incorporated into the optimisation model to emulate realistic operational flexibilities and costs. Using operational data, actual process schedules are compared to the RO results leading to improved process scheduling insights; such as increasing afternoon NLU operation during the spring to utilise lower power pricing caused by high solar generation. The RO is used to output a trackable load management key performance indicator to quantify potential and achieved scheduling improvements. Subsequently, correlations between renewable energy generation and spot market power prices are developed. Forecast pricing is used within a predictive optimiser (PO) to automatically generate an optimal schedule for the week ahead to meet projected customer demands. The RO provides potential hindsight savings of around 11%, and the PO up to 8% (representing significant cost savings for such energy intensive processes). [ABSTRACT FROM AUTHOR]

The development and application of an integrated real-time production scheduling and control strategy for a multiple cryogenic air separation unit (ASU) and compressor plant is discussed. Using a top-down optimisation approach, the operational targets for ASU production and compressor configuration are obtained for a given customer demand and subsequently managed using a real-time optimisation strategy. This is integrated with existing control to implement the steady-state configuration targets subject to process disturbances, power price fluctuations and against network change penalty weightings. Network material balance and network component operating constraints are met while simultaneously minimising plant reconfiguration costs during transient operation which occurs as a result of changing demands. Implemented using mixed integer linear programming, it is demonstrated that the two-stage optimisation strategy improves site operating costs by an average of 5% over the considered trial period (which would translate into substantial cost savings for such an energy intensive process). [ABSTRACT FROM AUTHOR]

The development and on-line application of a steady-state optimisation strategy for a multiple cryogenic air separation unit and compressor plant is discussed. Implemented using mixed integer linear programming (MILP), it is demonstrated that the optimiser improves site efficiency at steady state by reduction of power consumption by up to 5% (a significant saving for such an energy intensive process) while meeting customer demand specifications. This is achieved through determination of the production distribution of the air separation units and optimal load distribution of the compression network, while simultaneously ensuring network material balance and network component operating constraints are met. In addition, the work demonstrates achievable benefits of demand side load management during peak power pricing periods, using liquid oxygen as an effective energy storage device. A key constituent of the optimisation strategy is linear modelling to predict individual unit power consumption. Piece-wise linear data-based models of compressor and air separation unit power are shown to provide accurate models which improve existing on-site power prediction by up to 80% for compressors and 60% for the air separation units. [ABSTRACT FROM AUTHOR]

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 CO 2 emissions), however carbon free (renewable) electricity sources could see shift in operations leading a revenue increase of 12% [Display omitted] • Optimisation of biogas and energy distribution and carbon emissions monitoring using MILP. • Changes to current operations are needed to meet emissions pledges. • Changes to operational strategy could also see revenues increase by up to 12%. • Biomethane Injection primary driver of revenues. [ABSTRACT FROM AUTHOR]