It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

Historical data collected from processes are readily available. This paper looks at recent
advances in the use of data-driven models built from such historical data for monitoring, fault …

The purpose of this paper is to provide a summary of the current state of the application of
process systems engineering tools in the pharmaceutical industry. In this paper, we present …

Recent trends in the pharmaceutical sector are changing the way processes are designed
and executed, moving from: allowing the process to operate in a fixed point, to: allowing a …

Latent variable regression model (LVRM) inversion is a useful tool to support the
development of new products and their manufacturing conditions. The objective of the model …

In recent years, data-driven approaches (eg, latent variable model) have excited the
development of new products and the control of product quality. To derive an input space …

Multivariate projection methods or latent variable methods play an integral part in empirical
and hybrid modeling. Such models can be developed to relate final quality properties to raw …

Product design problems often require finding the raw materials and/or operating conditions
(inputs) that are needed to achieve some pre-assigned quality specifications on the product …

Garcia-Munoz et al.[Garcia-Munoz, S.; Kourti, T.; MacGregor, JF Chemom. Intell. Lab. Syst.
2005, 79, 101–114] proposed a new latent variable regression methodology, joint-Y partial …

As with all of pharmaceutical production, the regulatory environment for the production of
therapeutics has been changing as a direct result of the US FDA-initiated Quality by Design …