This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

The transformation toward renewable energy and feedstock supply in the chemical industry
requires new conceptual process design approaches. Recently, deep reinforcement …

The intensification of catalytic reactors is expected to play a crucial role to address the
challenges that the chemical industry is facing in the transition to more sustainable …

The disposal of high concentrated Tequila vinasses is an environmental threat. An
alternative to solve this problem is through anaerobic digestion processes to reduce organic …

Process synthesis experiences a disruptive transformation accelerated by artificial
intelligence. We propose a reinforcement learning algorithm for chemical process design …

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes
pose a challenge for process control. Due to the absence of accurate models and resulting …

This study introduces a Reinforcement Learning (RL) approach for synthesis, design, and
control of chemical process flowsheets (CPFs). The proposed RL framework makes use of …

We investigate the use of Random Forest (RF) regression to estimate viscoelastic
constitutive model parameters using Large Amplitude Oscillatory Shear (LAOS). We deploy …

The use of nonlinear model predictive control (NMPC) for the integration of design and
control remains as an open area of research. When NMPC is incorporated into the …

Distillation remains the leading and most frequently adopted technique for the separation
and purification of condensable mixtures in numerous industries. However, the inherently …