Allen Wright

The influence of reaction temperature on the oscillatory behaviour in the palladium-catalysed phenylacetylene oxidative carbonylation reaction.

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 bipolar mechanism for charge transferin a fluidised bed electrode

Abstract A simulation model is presented for a bipolar process of charge transfer in an apparently monopolar Fluidised Bed Electrode. Histograms of local overpo

PARAMETER ADAPTIVE CONTROL OF THE FED-BATCH PENICILLIN FERMENTATION

Abstract This paper describes a study to investigate the application of parameter adaptive control to mould growth in a fed-batch fermentation for penicillin production. Two models for the penicillin fermentation are described: a new mechanistic model is outlined and compared with a model based upon the work of Bajpai with an extension to include terms for the production of carbon dioxide. The Bajpai model was initially used to represent the penicillin process for simulation purposes, with real-time experiments being carried out on a twenty litre pilot plant fermenter. The penicillin mould biomass is controlled to a reference trajectory by applying adaptive control to the on-line estimate of biomass obtained from a measurement of carbon dioxide production, use of the Bajpai fermentation model and an extended Kalman filter. The adaptive algorithm considered is the Generalised Predictive controller, a form of long range, receding horizon, predictive control. Results from the application of adaptive control to the penicillin fermentation are presented. This work forms part of an industrial collaborative project the aim of which is the optimising control of large fed-batch fermenters.

A computer architecture to support the operation of virtual organisations for the chemical development lifecycle

Abstract Fine chemical and pharmaceutical manufacturers focus on new product development as a means of growth, with time to market as key driver, Time to market improvements are, however, limited by a companys infrastructure; for example, in-house design and manufacturing capacity, ability of respond to a dynamic environment etc. In many marketplaces, there is an increasing trend towards outsourcing and operating networks of specialist providers. Different specialist companies may be involved at all stages in the R&D lifecycle, providing services ranging from basic research or safety testing, to industrial scale manufacturing. This outsourcing concept can be generalised to support advanced notions of collaboration such as loosely-coupled but highly integrated networks of companies cooperating as a single enterprise. These networks can be described as dynamic Virtual Organisations (VOs) (Demchenko, 2004) . This paper will outline a computer based architecture developed to address the operational difficulties associated with the creation and operation of this type of VO and present it within the context of the chemical development lifecycle.

Inference of chemical reaction networks using hybrid s-system models

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.

Reverse Engineering Chemical Reaction Networks from Time Series Data

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.

Parameter Adaptive Control of the Fed-Batch Penicillin Fermentation

Abstract This paper describes a study to investigate the application of parameter adaptive control to mould growth in a fed-batch fermentation for penicillin production. Two models for the penicillin fermentation are described: a new mechanistic model is outlined and compared with a model based upon the work of Bajpai with an extension to include terms for the production of carbon dioxide. The Bajpai model was initially used to represent the penicillin process for simulation purposes, with real-time experiments being carried out on a twenty litre pilot plant fermenter. The penicillin mould biomass is controlled to a reference trajectory by applying adaptive control to the on-line estimate of biomass obtained from a measurement of carbon dioxide production, use of the Bajpai fermentation model and an extended Kalman filter. The adaptive algorithm considered is the Generalised Predictive controller, a form of long range, receding horizon, predictive control. Results from the application of adaptive control to the penicillin fermentation are presented. This work forms part of an industrial collaborative project the aim of which is the optimising control of large fed-batch fermenters.

Utilizing a Genetic Algorithm to Elucidate Chemical Reaction Networks: An Experimental Case Study

—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 systems 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.

A General Computer Simulation Package for Batch Chemical Reactors

Abstract This paper describes the development of a computer aided design package for stirred tank batch chemical reactors with the option of simultaneous multicomponent baten distillation. The package uses an intelligent syntax analyser to interpret chemical reactions and simply expressed operating conditions to construct and solve rigorous mass and energy balance equations. The system is flexible allowing a reaction vessel of any size to be simulated, ranging from laboratory glassware to a full scale production plant. A complete batch sequencing and temperature control system is included, allowing the simulation of batch, semi-batch or continuous reactor operation with open or closed loop temperature control for a variety of heat transfer fluids. Any of the process parameters may be edited at any time during the simulation allowing all aspects of dynamic process design, operation, optimisation and safety to be explored.

Identifying Chemical Reaction Network Models

In this work, an automated chemical reaction network identification procedure using a genetic algorithm (GA) is introduced. The GA uses chemical species concentration data obtained from batch reactors during process experimentation to build ordinary differential equation (ODE) models that represent the chemical reactions occurring. This is achieved using a two-tiered optimization approach. The main tier is an integer optimization problem using GA where the stoichiometric coefficients of the network of chemical reactions are determined. Using these stoichiometric coefficients, the specific rate constants for each reaction are obtained by solving a non-linear optimization problem in the second tier of the proposed approach. The prediction accuracy of any potential reaction network is determined by comparing the results obtained from the ODE model generated by the GA and the measured values obtained from experimentation. More promising models are retained by the GA and are used to construct even better networks in subsequent steps (or generations) of the GA through its evolution process. After a number of generations, the GA is terminated and the best network (in terms of prediction capability) is extracted. Using simulated data, the proposed optimization procedure is demonstrated to be capable of accurately determining a chemical reaction network.