Among the features of this release are an expanded model library, full compatibility with Windows Vista and Office 2007 and an array of tools to support Quality by Design (QbD).
You can now explore a factor space and assess process robustness / sensitivity by driving a DynoChem model from Excel. The impact of parameter uncertainty on predicted responses can be quantified to describe the 'response volume' (rather than response surface) within which actual process results are expected to lie. Experimental error, whether pure or systematic, is captured as part of determining uncertainty.
The graphics below show how uncertainty, expressed as a fractional relative error in the endpoint predictions for an impurity (CQA), varies across a factor space in which temperature and equivalents are varied.
Impurity detection is a challenge for measurement techniques, with a low signal to noise ratio; in this case, the measured impurity levels have typical noise levels and these are factored into the uncertainty levels indicated by the model.
Uncertainty is minimized near the points at which experiments were carried out and parameters were fitted. A feature typical of a good first principles / mechanistic model is that a small number of experiments and some replicates enable a reduction in uncertainty over a wide area of the factor space. In the above example, two additional experiments reduced uncertainty overall and broadened the region in which uncertainty is at a minimum.
Figure 1: Pressures in the range 22.8 to 3.6 bar achieve the target CQA when they occur in combination with kLa values defined by the curve ranging from 0.004 to 0.48 1/s. This is because high pressure raises hydrogen solubility, increasing the mass transfer driving force and requiring a lower kLa to achieve a given hydrogenation rate. This illustrates the use of high pressure to compensate for lack of mass transfer performance.
Figure 2: It should not be too surprising that the required kLa is directly related to the catalyst charge (shown here for lab scale conditions). High catalyst loading makes the chemistry faster and depletes the liquid of H2, which is supplemented by mass transfer from the headspace at a rate proportional to kLa. Catalyst charges from 0.07 to 1 g achieve the CQA when combined according to the curve with kLa values from 0.02 to 0.34 1/s.
Figure 3: Engineers will be interested in the required link between heat transfer and kLa shown here; once again a wide range of both parameters is acceptable but the ranges are related by the fact that high kLa leads to short reaction times and higher rates of heat release which must be compensated for quality and safety purposes by corresponding higher heat transfer coefficients. The relationship is not linear; at high kLa values (dissolved H2 nearing saturation) the reaction rate approaches the kinetically limited rate, i.e. does not continue to increase linearly with kLa.