6 papers
B3O: Scalable Boltzmann Batch Bayesian Optimization
Maximilian Bloor, Liyuan Xu, Hrvoje Stojic +1
Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO). Existing batch BO methods, how…
Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona +1
Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisf…
Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona +1
Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controllin…
Hierarchical RL-MPC for Demand Response Scheduling
Maximilian Bloor, Ehecatl Antonio Del Rio Chanona, Calvin Tsay
This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive…
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…
Control-Informed Reinforcement Learning for Chemical Processes
Maximilian Bloor, Akhil Ahmed, Niki Kotecha +3
This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of dee…