The operation of a chemical process plant has become more complex with the addition of process integration and intensification. A greater emphasis on producing goods with the lowest product variability in the safest manner possible and stringent environmental regulation limiting the quantity of effluent release have all put more constraints on the physical and economic performance of the chemical plant. The performance of a plant is quantified by the ability of the process system to achieve its objectives, which is governed by its process design and control. The conventional approach to process design and control selection starts sequentially by proposing a process flowsheet for the plant. The selection criteria for a flowsheet are normally based only on its environmental impact and economic merits. It is after a process flowsheet is deemed financially suitable that process control development commences. However, a more integrated approach to process design and control stage may thus lead to a plant that has better achievable performance. The aim of this project is to provide a new approach to quantitative dynamic controllability analysis for integration of process design and control by using the concept of passivity and passive systems. Passivity is an input/output property of processes. Passive processes are stable and minimum phase and therefore very easy to control. For a given process, its shortage of passivity, which reflects destabilizing effects of factors such as time delays and Right-Half Plane (RHP) zeros, can be used to indicate its controllability. The project focuses in developing the proposed controllability analysis by combining the idea of passivity and IMC invertibility, which is then formulated into an optimization problem that can be solved by either using Semi-Definite Programming or Non-Linear Optimization. The achievable performance of the plant is quantified in terms of the sensitivity function of the open-loop process. The selection of a process from four different heat-integrated distillation column schemes was used as a case study and the result had clearly shown that the passivity-based controllability analysis was able to select a process based on the plant achievable performance under the constraint of passivity and design parameters.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 175-187). Available also in a digital version from Dissertation Abstracts.

The purpose of this study is to design a real-time control and simulation system for a chemical mix facility. A simulation circuit board and software simulation in an IBM personal computer emulated the real-time chemical mix facility. A second personal computer controlled the plant. The parallel port in the IBM PC computer serves as a communication path between the controlled and controlling system. Results show that the simulation can assist the design of the actual system.

GE-model and equations of state are used to describe and predict phase equilibria. Current models have varying capabilities and some display selectivity for certain special mixtures. While many models are superior to others in their performance, all models share a common deficiency, the inability to simultaneously describe vapour-liquid (VLE) and liquid-liquid equilibria (LLE). Current models require separate parameters to describe the two equilibria. This formed the motivation for a non-linear transformation which was formulated by Rarey (2005). The transformation was applied to the concentration space. The clear advantage of such a transformation was that it could be easily applied to any model. The flexibility of the model was drastically increased. The effects were investigated on the local composition models, in particular the UNIQUAC model resulting in the FlexQUAC model. The model was used to regress a host of VLE and LLE data sets contained in the Dortmund Data Bank (DDB). The transformation had the desired effect on the flexibility of the model and the model was now able to describe VLE and LLE. However a symmetric transformation applied to the concentration space might not be effective in the description of systems exhibiting large difference in molecular size. This is a clear disadvantage of the proposed FlexQUAC model. In order to allow the model to cater to asymmetric systems, the transformation is now applied to the surface fraction of the residual contribution of the UNIQUAC model. The Guggenheim-Staverman expression in the combinatorial part was not transformed. Both the original combinatorial term and the more suitable modification of Weidlich and Gmehling (1987) were used. The newly formed model was called the FlexQUAC-Q model. The development of the FlexQUAC-Q model, derivation of activity coefficient expressions, model implementation and its performance analysis form the basis for this research study. The activity coefficient of the new model had to be re-derived due to the application of the transformation to the residual contribution of the UNIQUAC equation. The computation of the activity coefficient was programmed in FORTRAN and integrated into the regression tool (RECVAL) of the Dortmund Data Bank (DDB). The RECVAL tool was used to regress data sets contained in the DDB. Results obtained were comparable to those obtained using the GEQUAC model. The regression was also performed in EXCEL for the three models (UNIQUAC, FlexQUAC, FlexQUAC-Q). The regression in EXCEL was more rigorous and was used for the comparison of the objective functions and to obtain a set of unique model parameters for each data set. The performance of the FlexQUAC-Q model was assessed utilizing the same data sets used to analyse the performance of the FlexQUAC model. The model's performance was assessed in the regression of 4741 binary VLE data sets, 13 ternary VLE data sets and carefully select ternary LLE cases. The minor mean relative reduction of about 3% of the objective function using FlexQUAC-Q compared to FlexQUAC was observed compared to a reduction by about 53% relative to the UNIQUAC-results. It was necessary to illustrate that the new model does not degenerate the model's existing capabilities (e.g. ability to predict multi-component mixtures from binary data) and that the model performs as well as or superior to the UNIQUAC model. FlexQUAC-Q performed similarly to FlexQUAC. However the improvement in the qualitative description of data sets exhibiting asymmetry is apparent. Herein lies the justification of such a modification and this illustrates the preference of such a model when asymmetric systems are being considered. In addition, the FLEXQUAC-Q model can be adapted to be implemented into a group contribution method, a distinct advantage over the previous model FlexQUAC. The equations for the application of a non-linear transformation to a functional group activity coefficient model, UNIFAC are also explored in this study. The resulting model is referred to as FlexFaC. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2007.

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