Value and Optimisation

The point of departure of the methodology is, as we introduced already in Chapter B4, to split the transition first a transition from the stakeholder requirements into the functional domain, and then a transition from the functional domain into the physical domain. And of these two, it is the first one that is relatively novel and needs to be documented the second transition is already quite extensively developed in the current state of systems engineering, as we saw in Chapter C2. The system concept is then applied in the functional domain. By describing the functionality of the plant as a system of functional elements, we expect to be able to handle the complexity in a more efficient and effective manner. 

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It was apparent that the factor characterising the friction between gas flow and wall needed to increase with increasing flow rate of solids, to yield the increasing bend pressure drop observed. Experiment suggested a linear relationship against suspension density, with an intercept when such a relationship was imposed on the value and optimisation of the slope and intercept values of the relationship was undertaken, the relationship yielding least discrepancy between model and data was as shown overleaf -... 

Value and optimisation are so central to the application of the system concept to engineering presented in this book that the whole of the last chapter is dedicated to them. 

Similar considerations apply to the selection of pressure drops where there is freedom of choice, although a full economic analysis is justified only in the case of very expensive units. For liquids, typical values in optimised units are 35 kN/m2 where the viscosity is less than 1 mN s/m2 and 50-70 kN/m2 where the viscosity is 1-10 mN /m2 for gases, 0.4-0.8 kN/m2 for high vacuum operation, 50 per cent of the system pressure at 100- 200 kN/m2, and 0 per cent of the system pressure above 1000 kN/m2. Whatever pressure drop is used, it is important that erosion and flow-induced tube vibration caused by high velocity fluids are avoided. 

A basic use of a process model is to analyse experimental data and to use this to characterise the process, by assigning numerical values to the important process variables. The model can then also be solved with appropriate numerical data values and the model predictions compared with actual practical results. This procedure is known as simulation and may be used to confirm that the model and the appropriate parameter values are "correct". Simulations, however, can also be used in a predictive manner to test probable behaviour under varying conditions, leading to process optimisation and advanced control strategies. 

The second group of recently developed ionic liquids is often referred to as task specific ionic liquids in the literature [15]. These ionic liquids are designed and optimised for the best performance in high-value-added applications. Functionalised [16], fluorinated [17], deuterated [18] and chiral ionic liquids [19] are expected to play a future role as special solvents for sophisticated synthetic applications, analytical tools (stationary or mobile phases for chromatography, matrixes for MS etc.), sensors and special electrolytes. 

Feed B at differing rates into each tank, but maintain the same total molar flow rate of B, as in Exercise 1. Carry out simulations for the kinetic case iibi < iiB2 and optimise the resultant selectivity. Show by simulation that high values for CP3 are given by low values of Cb-... 

The problem involved in the application which is the subject of this chapter is the optimisation of a property of a mixture of compounds (a common situation in pharmaceutical practice, where for example tablet formulations have to be optimised). This property has to be optimised with respect to a certain goal (maximum, minimum or target value) and also with respect to the robustness or ruggedness of the mixture property. This means that despite any variation in the response or the independent variables (mixture variables in our case) due to unknown variation the response values have to be as close as possible to a desired value (target value). 

In an optimisation procedure involving the minimal partition coefficient, the minimal partition coefficient is calculated using all compositions of the extraction liquid (all possible combinations of x, X2 and within the mixture space). The highest value calculated for this minimal partition coefficient (the maximal minimal partition coefficient) is the optimal value and hence the composition where the partition coefficient of the worst extractable substance is highest. 

The rate expressions contain a number of unknown parameters with a physical meaning. Their values are estimated by using on the one hand the experimental data and on the other hand the calculated values predicted from the rate expressions in the reactor and optimising a certain objective function. This is called data regression [8], Several techniques exist to achieve this goal. 

Prepare the samples and standards as described and optimise the instrumental parameters. Aspirate the standards then samples and record the absorbance readings. Aspirate the standards again and check that the absorbance values are unchanged. Prepare a calibration graph and compare sample absorbance values with this. 

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