关键词: 红外热成像, 自动控温, 人工神经网络, 反应和分离耦合技术

Abstract: While conventional thermal catalytic CO₂ conversion requires harsh high-temperature and high-pressure conditions, plasma technology enables CO₂ hydrogenation to methanol under ambient conditions, representing a crucial approach for energy conservation, carbon reduction, and achieving “double carbon” goals. This study develops an innovative dielectric barrier discharge (DBD) plasma-liquid film reactor that successfully couples plasma reaction with separation technology, overcoming the traditional trade-off between high CO₂ conversion and methanol selectivity. Addressing two key challenges in plasma processes—precise temperature control and complex parameter optimization—we implement digital solutions: Infrared thermography provides real-time, comprehensive temperature monitoring of discharge zones, surpassing conventional thermocouples that suffer from localized measurement and interference from high-voltage electrode discharge; An automated control module dynamically adjusts coolant parameters to maintain thermal equilibrium between plasma generation and dissipation. Furthermore, we employ artificial neural network (ANN) modeling for intelligent prediction of optimal conditions and process optimization. This interdisciplinary experiment integrates digital technologies with AI methodologies, fostering students’ innovative capabilities in alignment with emerging engineering education requirements.

Key words: Infrared thermography, Automatic temperature control, Artificial neural network, Coupling technology of reaction and separation