The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …

Hybrid semi-parametric models consist of model structures that combine parametric and
nonparametric submodels based on different knowledge sources. The development of a …

This article focuses on the design of model predictive control (MPC) systems for nonlinear
processes that utilize an ensemble of recurrent neural network (RNN) models to predict …

This study presents a broad perspective of hybrid process modeling combining the scientific
knowledge and data analytics in bioprocessing and chemical engineering with a science …

The pharmaceutical industry has witnessed exponential growth in transforming operations
towards continuous manufacturing to increase profitability, reduce waste and extend product …

Smart manufacturing (SM) is a new paradigm that allows manufacturing to enter its fourth
revolution by exploiting state-of-the art sensing, communication and computation as the …

A plethora of models have been developed quantifying the effect of diffusion‐controlled
phenomena on polymerization reactions. The most prominent approaches are reviewed …

Due to the lack of complete understanding of metabolic networks and reaction pathways,
establishing a universal mechanistic model for mammalian cell culture processes remains a …

In this work, we aim to introduce the concept of the degree of hybridization for cell culture
process modeling. We propose that a family of hybrid models can be created with varying …

A batch-to-batch model-based iterative optimal control strategy for batch processes is
proposed. To address the difficulties in developing detailed mechanistic models, recurrent …