Daniel Beahr

Augmentation and Hybridization of Conventional Forms of Process Control with Advanced Control Methods

The goals of my research is to broadly integrate novel advanced control methods into the field of process control. This is largely focused on the implementation of reinforcement learning. New advanced computational techniques have allowed the use of RL in conjunction with continuous systems, but developments have failed to accommodate the often stringent learning and performance requirements necessary for control of a plant environment. This work seeks to bridge that gap, establishing RL as a viable control method while also ensuring the safety and performance expected of any conventional process control. 

1. Beahr D, Bhattacharyya D, “Application of Unsupervised Machine Learning Methods to Actor-Critic Structures in Reinforcement Learning for Training and Online Implementation”, 204, 109392, Computers & Chemical Engineering, 2026

2. Beahr D, Hedrick E, Bhattacharyya D, “Continuous Learning of the Value Function Utilizing Deep Reinforcement Learning to be Used As the Objective in Model Predictive Control”, 201, 109262, Computers & Chemical Engineering, 2025

3. Beahr D, Saini V, Bhattacharyya D, Seachman S, Boohaker C, "Estimation-based Model Predictive Control with Objective Prioritization for Mutually Exclusive Objectives: Application to a Power Plant", 141, 103268, Journal of Process Control, 2024

4. Beahr D, Bhattaharyya D, Allan D A, Zitney S E, "Development of Algorithms for Augmenting and Replacing Conventional Process Control using Reinforcement Learning", 190, 108826, Computers & Chemical Engineering, 2024

• Beahr D, Bhattacharyya D, “Application of Unsupervised Machine Learning Methods to Reinforcement Learning Actor-Critic Structures for Training and Online Implementation”, Paper 594d, AIChE Annual Meeting, Boston, MA, Nov 2-6, 2025

• Beahr D, Hedrick E, Bhattacharyya D, “Continuous Learning of the Value Function Utilizing Deep Reinforcement Learning and Its Use as the Objective in Model Predictive Control”, Paper 259a, AIChE Annual Meeting, Boston, MA, Nov 2-6, 2025

• Beahr D, Bhattacharyya D, “Synergistic Integration of Reinforcement Learning with Conventional Process Control”, Paper 732e, AIChE Annual Meeting, San Diego, CA, October 27-31, 2024 

• Beahr D, Hedrick E, Bhattacharyya D, “Continuous Learning of the Value Function Utilizing Deep Reinforcement Learning to be Used As the Objective in Model Predictive Control”, Paper 457c, AIChE Annual Meeting, San Diego, CA, October 27-31, 2024 

• Beahr D, Bhattacharyya D, Allan D, Zitney S E, “Augmented Control Using Reinforcement Learning and Conventional Process Control”, Paper 59ao, AIChE Annual Meeting, Orlando, FL, November 5-10, 2023 

• Beahr D, Bhattacharyya D, Saini V, Hedrick E, Hong S M C, “Estimation-Based Model Predictive Control with State-Dependent Objective Prioritization; An Application to a Natural Gas Combined Cycle Power Plant”, Paper 689c, AIChE Annual Meeting, Phoenix, AZ, November 13-18, 2022

• Beahr D, Alastanos M, Hedrick E, Bhattacharyya D, “Development of Algorithms for Augmenting and Replacing Conventional Process Control Using Reinforcement Learning”, Paper 106g, AIChE Annual Meeting, Phoenix, AZ, November 13-18, 2022