Global Sensitivity Analysis of a Spray Drying Process

Abstract

Spray drying is a key unit operation used to achieve particulate products of required properties. Despite its widespread use, the product and process design, as well as the process control remain highly empirical and depend on trial and error experiments. Studying the effect of operational parameters experimentally is tedious, time consuming, and expensive. In this paper, we carry out a model-based global sensitivity analysis (GSA) of the process. Such an exercise allows us to quantify the impact of different process parameters, many of which interact with each other, on the product properties and conditions that have an impact on the functionality of the final drug product. Moreover, classical sensitivity analysis using the Sobol-based sensitivity indices was supplemented by a polynomial chaos-based sensitivity analysis, which proved to be an efficient method to reduce the computational cost of the GSA. The results obtained demonstrate the different response dependencies of the studied variables, which helps to identify possible control strategies that can result in major robustness for the spray drying process.

Mathematical Modeling of the Manufacturing Sector’s Dominant Part as a Base for Automation

“…Findings manifested the interval of confidence on behalf of investigation and probabilistic performance as well. Another area using polynomial chaos is pharmaceutical manufacturing [35]. Within pharmaceutical manufacturing processes, spray-drying was disputed.…”

Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

“…The Wiener-Askey scheme [37] lists some commonly used probability distributions that are associated with certain univariate orthogonal polynomials. Other approaches based on statistical moments [38,39] or the Gram-Schmidt orthogonalisation [40,41] are available to generate the monovariate polynomials when the parameters are correlated or do not follow a distribution in the Wiener-Askey scheme.…”

A Novel Global Sensitivity Measure Based on Probability Weighted Moments

“…The most direct method of estimating presented measures is to use the double-loop numerical estimation method. Equations ( 8) and (9) show the estimator of PWMs, so the key issue is how to estimate conditional PWMs. The main procedures are summarized as follows:…”

“…Besides, there are several mature techniques available to solve the above indices, for instance, Monte Carlo simulation, quasi-Monte Carlo simulation, high-dimensional model representation (HDMR), Markovian integration [6], and the surrogate-assisted method [7,8]. Variance-based global sensitivity measures have been widely applied in various fields such as the spray-drying process [9], heap leaching [10], the rainfall-runoff model [11], and polymeric material [12]. However, the variance is not fully representative of uncertainty, especially in some problems where higher moments have more of an effect.…”