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In the pharmaceutical industry, it is common to determine the maximum allowable set of uncertain parameters—called the design space—that guarantees that the product quality attributes obtained by a manufacturing process are within specified limits. In industrial practice, design spaces are constructed without considering the effects of process dynamics or feedback control, which results in poor characterization...
In the Quality-by-Design (QbD) paradigm for pharmaceutical processes, critical quality attributes (CQAs) must meet specifications for all possible realizations of critical process parameters (CPPs) within a design space. During the startup of such a process, it is desired to begin meeting CQA specifications as quickly as possible and then robustly guarantee that specifications will continue to be...
A commonly applied procedure in the pharmaceutical industry is to determine the maximum allowable set of uncertain parameters—called the design space—that guarantees that the product quality attributes obtained by a manufacturing process are within the specific limits. Design spaces in industry are constructed without considering the effects of process dynamics or feedback control, which results in...
An algorithm is proposed for the characterization of the set of allowable real parametric uncertainties that achieve output specifications for nonlinear systems. The approach first expands the system output by a multivariate polynomial or rational function of perturbations of the real parameters, which is then written as a linear fractional transformation. Bounds on the uncertainty set are computed...
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