2 citations · 5 across the 17 of their papers we have counts for
20 papers
OperAItor: An Agentic Framework for Fault-Tolerant Operation of Chemical Processes
Javal Vyas, Mehmet Mercang{ö}z
OperAItor, an open-source framework for developing and evaluating large language model agents for supervisory fault management in chemical processes. The framework is demonstrated…
ADMITBench: A Safety-Governed Reference Framework for Evaluating the Admissibility of Industrial LLM Advisories
Yash Misra, Javal Vyas, Siddharth Gutta +1
This white paper presents ADMITBench, a reference framework for evaluating industrial LLM advisories at the level of the proposed action. The framework implements a versioned, safe…
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
Ari Luna Rueda, Eike Cramer, Klaus Hellgardt +1
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into c…
From P&ID Drawings to Process Graphs: A Multimodal Language Model Approach
Baikai Zhu, Samuel Duong, Javal Vyas +1
Piping and instrumentation diagrams (P&IDs) encode the functional structure of process plants and are a critical yet underutilised source of engineering knowledge for digital twins…
A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
Javal Vyas, Milapji Singh Gill, Artan Markaj +2
Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures,…
Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
Javal Vyas, Milapji Singh Gill, Mehmet Mercangöz
Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is…
Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction
Yuchen Wang, Javal Vyas, Tong Liu +1
A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no man…
Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
Liqiu Dong, Marta Zagórowska, Mehmet Mercangöz
We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance.…
A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
Samuel Stricker, Claus Wirnsperger, Alessandro Butté +4
This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-der…
Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction
Alex Durkin, Jasper Stolte, Matthew Jones +4
Offline reinforcement learning (offline RL) offers a promising framework for developing control strategies in chemical process systems using historical data, without the risks or c…
Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation
Javal Vyas, Mehmet Mercangoz
The increasing complexity of modern chemical processes, coupled with workforce shortages and intricate fault scenarios, demands novel automation paradigms that blend symbolic reaso…
PC-Gym: Benchmark Environments For Process Control Problems
Maximilian Bloor, José Torraca, Ilya Orson Sandoval +6
PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemi…
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Liqiu Dong, Marta Zagorowska, Tong Liu +2
Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems. However, in an active learning setting collectin…
Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors
Marta Zagorowska, Lukas Ortmann, Alisa Rupenyan +2
Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems. Implementation of OFO…
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…
Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors
Yilin Zhuang, Zhuobin Zhou, Burak Alakent +1
We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing applications. We motivate the us…
Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression
M. Zagorowska, M. Degner, L. Ortmann +4
Online Feedback Optimization is a method used to steer the operation of a process plant to its optimal operating point without explicitly solving a nonlinear constrained optimizati…
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework
Buse Sibel Korkmaz, Marta Zagórowska, Mehmet Mercangöz
Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes ch…
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangoz
The vast majority of systems of practical interest are characterised by nonlinear dynamics. This renders the control and optimization of such systems a complex task due to their no…
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…