Work

Identification of Pressure‐Swing Separation Processes for Azeotropic Mixtures Using Deep Reinforcement Learning

Year: 2025

Type: article

Abstract: Our previously developed deep reinforcement learning (RL) framework for the conceptual design of fluid separation processes showed strong performance in generating flowsheets for multiple chemical systems under single‐pressure conditions. This work extends that framework by introducing distillation columns modeled at different pressures into the RL environment. The agent autonomously learns to synthesize flowsheets, uncovering pressure‐swing strategies for pressure‐sensitive azeotropes without prior knowledge or heuristics. It also continues to identify effective single‐pressure processes that rely on entrainers or liquid–liquid immiscibility for mixtures with less pressure sensitivity. This work advances RL‐based process synthesis toward a more general and versatile framework.

Source: Chemie Ingenieur Technik

DOI: 10.1002/cite.70032

Authors: Alexander B. Wolf, Jonathan Pirnay, Quirin Göttl, Dominik G. Grimm, Jakob Burger

Institutions: Technical University of Munich, Weihenstephan-Triesdorf University of Applied Sciences

Language: English

Topic: Process Optimization and Integration

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,030

Funder: Deutsche Forschungsgemeinschaft