A random forest-optimized sensor fusion approach for non-invasive ammonia measurement: enhancing performance in jet impact-negative pressure reactors

Ammonia removal in jet impact negative pressure reactors requires reliable monitoring without compromising vacuum conditions. This study developed a non-invasive detection system using an MQ135 sensor combined with spectral analysis and adaptive filtering to isolate 20–90 Hz vibrational noise characteristics. A machine learning framework integrating random forest-optimized fuzzy clustering was trained against infrared spectrophotometric reference data. Quantitative residual analysis demonstrated progressive error reduction during training, with measurement uncertainty converging to within a 5 % tolerance interval – significantly surpassing conventional methods. The optimized model achieved real-time ammonia concentration monitoring while maintaining negative pressure integrity, establishing a robust, non-invasive measurement methodology that significantly enhances measurement accuracy and reliability for gas concentration determination in harsh, confined environments under negative pressure.