Work

Q-learning-based stochastic model predictive control for green ammonia production

Year: 2026

Type: article

Abstract: Green ammonia production systems powered by intermittent renewable energy must meet periodic demand under tight unit and storage constraints. We propose Q-learning-based stochastic model predictive control, a methodology integrating a stochastic model predictive control framework with a Q-function as the terminal cost. The proposed method explicitly enforces hard constraints, effectively manages both short-term and long-term disturbances, and offers significant advantages in terms of on-line computational speed. Simulation results show that the proposed method outperforms Nonlinear Model Predictive Control, Double Deep Q-Network, and Q-learning-based Model Predictive Control baselines. The proposed method achieves the lowest total cost, minimal soft constraint penalties, and eliminates both tank overflow and ammonia demand shortfall, enabling practical, real-time operation.

Source: Journal of Process Control

DOI: 10.1016/j.jprocont.2026.103672

Authors: Hyun Min Park, Tae Hoon Oh, Jong Min Lee

Institutions: Seoul National University, Ulsan National Institute of Science and Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,700