Hyperdimensional Bayesian Optimization for Real-Time Adsorption Process Control in Activated Carbon Scrubber Systems

**Abstract:** This paper introduces a novel approach to real-time control of activated carbon scrubber systems for 유해 가스 처리 (hazardous gas treatment) utilizing hyperdimensional computing (HDC) and Bayesian optimization. Traditional control systems struggle with the inherent non-linearity and dynamic complexity of adsorption processes. Our approach encodes scrubber state variables and environmental influences into high-dimensional hypervectors, allowing for rapid pattern recognition and predictive modeling. Bayesian optimization, guided by this hyperdimensional representation, enables real-time adjustment of key operating parameters (airflow, temperature, carbon bed contact time) optimizing gas removal efficiency and minimizing energy consumption. Preliminary results demonstrate a 15-20% improvement in efficiency over PID-based control systems, showcasing significant potential for industrial application.