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08/17/2023
Machine Learning-Assisted Discovery of Novel Reactor Designs
Additive manufacturing has enabled the fabrication of advanced reactor g...
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07/21/2023
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Most solutions to the inventory management problem assume a centralizati...
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05/01/2023
Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations
The development of new manufacturing techniques such as 3D printing have...
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01/26/2023
The Automated Discovery of Kinetic Rate Models – Methodological Frameworks
The industrialization of catalytic processes is of far more importance t...
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12/08/2022
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning
Ongoing risks from climate change have impacted the livelihood of global...
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10/31/2022
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors
New manufacturing techniques such as 3D printing have recently enabled t...
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10/20/2022
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ordinary differential equations (Neural ODEs) model continuous ti...
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04/03/2022
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning
The vast majority of systems of practical interest are characterised by ...
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03/01/2022
Distributional Reinforcement Learning for Scheduling of (Bio)chemical Production Processes
Reinforcement Learning (RL) has recently received significant attention ...
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11/23/2021
Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station
Devising optimal operating strategies for a compressor station relies on...
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11/10/2021
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
This paper proposes a new class of real-time optimization schemes to ove...
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04/23/2021
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Reinforcement Learning (RL) controllers have generated excitement within...
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09/18/2020
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization
This paper investigates a new class of modifier-adaptation schemes to ov...
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07/30/2020
Chance Constrained Policy Optimization for Process Control and Optimization
Chemical process optimization and control are affected by 1) plant-model...
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06/04/2020
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Dynamic real-time optimization (DRTO) is a challenging task due to the f...
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08/05/2019
