Reinforcement learning is a branch of machine learning, where the machines gradually
learn control behaviors via self-exploration of the environment. In this paper, we present a …

Improvements in software, instrumentation, and feedback control as well as deepening
linkages between fundamental aspects of process technology have vastly changed the …

We devise a novel technique to control the shape of polymer molecular weight distributions
(MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent …

This paper utilizes artificial intelligence (AI) techniques to address the trajectory tracking
problem of a continuous polymerization reactor with unknown dynamics and unknown …

Abstract Knowledge of the average composition and molecular weights of copolymers
containing functional groups is often not enough to ensure product quality, as the distribution …

Fully automatic, simultaneous control of polymer composition and molecular weight
trajectories during free radical copolymer synthesis was achieved by coupling the …

A framework that embraces a state-of-the-art sensor, multi-objective dynamic optimization,
nonlinear state estimation and control, is designed and implemented to achieve target …

Polymers and polymer composites possess a wide range of applications in chemical,
material, and biomedical fields. Although conventional techniques to design and process …

This paper proposes an adaptive neural network based output feedback backstepping fast
terminal sliding mode control (NN-BFTSMC) for continuous polymerization reactor with …

In this work, a geometric observer (GO) is formulated and tested using experimental data
from the Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) …