Process Optimization and Scale-up

Recommended Prerequisites

Basic knowledge of algebra and calculus.

Teaching Methods

Theoretical presentations by the instructor, with participation of the students. Practical and computational demonstrations and exercises.

Learning Outcomes

Design and optimization of processes in the manufacture of pharmaceutical forms. Identification and hierarchization of factors. Setting up the physical nature of the processes and the respective description through adimensional numbers. Scale-up: laboratory, pilot and industrial facilities.

Work Placement(s)

No

Syllabus

General statistics: Probability distributions; measurement; the experimental error; uncertainty propagation; confidence intervals; tests; Anova, notions of sampling.
Regression: Linear regression; least squares; multiple regression; residuals; least squares with weights; nonlinear least squares.
Optimization: Direct search; locating roots; simplex; numerical calculation of integrals: the trapezoidal rule.
Experimental design: Scheme 2^k; main effects and interaction; the algorithm. Other schemes. Applications.
Transposition of scale: Dimensional analysis; homogeneity; the simple pendulum and the droplet; pi theorem; general procedure; dimensionless numbers; examples; from the laboratory to industry: additional questions.
Multivariate techniques: The object, hierarchical cluster analysis; K-means. Reduction of dimensionality: principal component analysis; loadings and scores; analysis of factors. Classification: the learning set, discriminant analysis, k-nearest neighbors.

Head Lecturer(s)

Alberto António Caria Canelas Pais

Assessment Methods

Assessment
Synthesis work: 100.0%

Bibliography

Pharmaceutical Process Scale-Up (2nd ed.), M. Levin (Ed.), CRC, Boca Raton (2006);
Pharmaceutical Experimental Design, G.A. Lewis, D. Mathieu, R. Phan-Tan-Luu, Dekker, New York (1999);
Scale-Up in Chemical Engineering, M. Zlokarnik, Wiley, Weinheim (2002).