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The main aim of the work was to produce scale-up methods for the design of aerated stirred tanks using a combined computational fluid dynamics (CFD) and population balance approach. First a modeling study of single phase stirred tanks was performed to evaluate the best model features (turbulence model, impeller's model, discretisation, grid etc). Good agreement was obtained between the CFD simulation and the LDA measurement on the time-averaged mean velocities and turbulence quantities. The angle-resolved mean velocities and turbulence quantities were also predicted very well as were the power number and the positions of the vortex cores. The next stage involved the development of a population balance model (PBM) which was carried out first using a well-mixed single compartment implemented in MATLAB to reduce the modeling complexity. The algorithm was validated for various mechanisms, namely breakage, aggregation, nucleation and growth which have an analytical solution available from literature. Tests using realistic models for bubble coalescence and breakage were also carried out with the results showing a reasonable agreement with the Sauter mean bubble sizes obtained from empirical correlations. The algorithm also responded well to changes in the turbulence dissipation rate, the initial bubble size distribution and the local gas hold-up, which suggest that the final bubble size is not affected by the initial bubble size. A fully predictive model must combine both the fluid mechanics and bubble dynamics models which can be performed either by a four-way or three-way coupling simulation. The disadvantage of the latter is that is does not consider the effect of the bubble dynamics in- the two-phase modelling. A four-way coupling (CFD-PBM) method was carried out by implementing the PBM within the CFD code. Various drag models which take into account the effect of distorted bubbles and dense gas dispersion are also considered. Mass transfer models are also implemented using the bubble sizes obtained from the PBM. The CFD-PBM model showed a reasonable prediction of the power number, local bubble sizes, gas hold-up, dissolved oxygen concentration and the mean velocities of the two-phase flow in comparison to experimental data taken from the literature. Finally, the CFD-PBM model was employed to evaluate the consequences of scale-up on the mass transfer rate in aerated stirred tanks agitated either by Rushton turbine or CD-6 impeller with operating volume ranged from 14L to 1500L. Three scale-up rules, namely a constant P IV combined with either constant Fig, Vg and VVM were studied. The simulation results suggest, that a successful scale-up may be achieved by keeping the P IV and VVM constant, which led to a slightly higher (kLa) representing a more conservative approach. In contrast, constant P/V and Vg led to a slight reduction in the rate of mass transfer at larger scale which is in agreement with experimental measurement . from the literature. Results from the CFD-PBM simulation also suggest a similar scale-up rule may be applicable for an advanced gas dispersion impeller such as the CD-6 which yielded a similar scale-up trend to that of a Rushton turbine.

The chemical processing of binary mixtures of benzene and acetylene with ammonia or carbon monoxide has been studied in the continuous flow microwave discharge reactor at 2450 MHz. The microwave discharge was modelled as a CSTR reactor, and the volume was assumed to be proportional to the power absorbed. Reynolds numbers in the lamina region were calculated for the gas flows in the silica reactor tube. The operating variables investigated were reactant ratios, reactor pressures, the effect of nickel in the discharge zone, flowrate through the reactor and the power absorption. Two microwave cavities were used. No reaction occurred in either of the carbon monoxide binary systems. The prime product of the reaction between benzene or acetylene with ammonia was hydrogen cyanide. No reaction or decomposition occurred below a critical mole ratio.in either system. Hydrogen and nitrogen were also formed on reaction in both systems together with small quantities of polymer. Acetylene, butadiene and aniline were also formed in the benzene/ammonia system. No azo-benzenes were produced. The selectivity of the hydrogen cyanide formation was ~ 90% in the acetylene/ammonia system and ~ 50% in the . benzene/ammonia system. The maximum yield of hydrogen cyanide was ·27.5 gm/kW.h in the acetylene/ammonia system and 35.0 gm/kW.h in the benzene/ammonia system. The rate of production of hydrogen cyanide was found to have an order of 1.5 with respect to the acetylene and uammonia in the acetylene/ammonia system in one set of experimental runs and was zero order with respect to the benzene and ammonia in the benzene/ammonia system, The values of the rate constants were dependent upon the cavity and the power absorbed. Comparison with other discharge processes indicates that increased frequency increases the production of hydrogen cyanide. In conclusion it was proposed that as the difference in ionization potential between the two compounds in the binary mixture increases it becomes progressively more difficult for reactive species to be produced in the microwave discharge, This was thought likely to be due to a change in the electron energy distribution in the microwave discharge .

The use of batch processes is widespread across the manufacturing industries, dominating sectors such as pharmaceuticals, speciality chemicals and biochemicals. The main goal in batch production is to manufacture consistent, high quality batches with minimum rework or spoilage and also to achieve the optimum energy and feedstock usage. A common approach to monitoring a batch process to achieve this goal is to use a recipe-driven approach coupled with off-line laboratory analysis of the product. However, the large amount of data generated during batch manufacture mean that it is possible to monitor batch processes using a statistical model. Traditional multivariate statistical techniques such as principal component analysis and partial least squares were originally developed for use on continuous processes, which means they are less able to cope with the non-linear and dynamic behaviours inherent within a batch process without being adapted. Several approaches to dealing with batch behaviour in a multivariate framework have been proposed including multi-way principal component analysis. A more advanced approach designed to handle the typical characteristics of batch data is that of model-based principal component. It comprises of a mechanistic model combined with a multivariate statistical technique. More specifically, the technique uses a mechanistic model of the process to generate a set of residuals from the measured process variables. The theory being that the non-linear behaviour and the serial correlation in the process will be captured by the model, leaving a set of unstructured residuals to which principal component analysis (PCA) can be applied. This approach is benchmarked against the more standard approaches including multiway principal components analysis, batch observation level analysis. One limitation identified of the model-based approach is that if the mechanistic model of the process is of reduced complexity then the monitoring and fault detection abilities of the technique will be compromised. To address this issue, the model-based PCA technique has been extended to incorporate an additional error model which captures the differences between the mechanistic model and the process. This approach has been termed super model-based PCA (SMBPCA). A number of different error models are considered including partial least squares (linear, non-linear and dynamic), autoregressive with exogenous (ARX) variables model and dynamic canonical correlation analysis. Through the use of an exothermic batch reactor simulation, the SMBPCA approach has been investigated with respect to fault detection and capturing the non-linear and dynamic behaviour in the batch process. The robustness of the technique for application in an industrial situation is also discussed.

Dynamic optimisation of semi-continuous bioprocesses aims at finding the optimal control of process conditions (such us feed rate, temperature, pH, etc.) through time. These processes can be generally modelled by sets of non-linear differential and algebraic equations (DAEs). Prior to optimisation, understanding insights of modelling general bioprocesses is essential. The impact of the substrate and product inhibitions, as well as the degradation of bio-substance due to metabolism inside the living cells, results in highly nonlinear models where both the control and state variables are narrowly constrained. The consequence of this problem is challenging in terms of two issues: the construction of the unknown controls through time and the development of an algorithm for dynamic optimization for the constrained model.

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