4 citations · 9 across the 9 of their papers we have counts for
10 papers
Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Matthew Marsh, Benoit Chachuat, Antonio del Rio Chanona
Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanisms to enforce physical constr…
Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation
Julius H Ramlau, Friedrich Hastedt, Tolga Birdal +3
Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR)…
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
Tom Savage, Ehecatl Antonio del Rio Chanona
Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply hig…
Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona
Planning, scheduling, and control typically constitute separate decision-making units within chemical companies. Traditionally, their integration is modelled sequentially, but rece…
ARRTOC: Adversarially Robust Real-Time Optimization and Control
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangoz
Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, at the control laye…
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Marwan Mousa, Damien van de Berg, Niki Kotecha +2
Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational constraints in real supply chain networks. The in…
The Automated Discovery of Kinetic Rate Models -- Methodological Frameworks
Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, Klaus Hellgardt +4
The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, w…
Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station
Akhil Ahmed, Marta Zagorowska, Ehecatl Antonio del Rio-Chanona +1
Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it…
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Río Chanona +1
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their abilit…
Reinforcement Learning for Batch Bioprocess Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to thei…