Synergistic integration of multidimensional differential spectroscopy and deep learning for robust phenol monitoring...

Phenol is a key chemical intermediate and a common, toxic pollutant found in industrial wastewater from processes like coking and oil refining. Its leakage and discharge lead to significant contamination, posing a dual threat to ecological systems and human health, making its accurate, real-time monitoring paramount. Current detection methods have significant drawbacks. Chemical sensors can have long response times or insufficient accuracy. Standard optical methods like chromatography are cumbersome and ill-suited for rapid, on-site use. Simple UV-Vis spectroscopy is severely compromised by overlapping spectral interference and environmental instability in complex water samples.

The strategy first leverages phenol's specific bromination to selectively eliminate its spectral signature. A custom dual-channel, variable-path (1-10 cm) system was built to capture this transformation, generating high-dimensional (3D) differential spectral matrices. This 3D data approach reduced the Mean Absolute Error (MAE) by 45.4 % compared to conventional 1D spectra (when using the same CNN model). For analysis, a novel Parallel Associative Neural Network (PSNN) was developed, which substantially outperformed a standard CNN by achieving a further 66.7 % reduction in MAE. This integrated strategy (3D + PSNN) yielded a final prediction error of 72.09 μg/L under laboratory conditions and an MAE of 172.9 μg/L in field tests against HPLC benchmarks.

This study presents a novel strategy integrating chemical selectivity, multidimensional optical sensing, and a custom Parallel Associative Neural Network (PSNN). The work provides a robust framework for developing highly selective and sensitive intelligent systems for on-line water quality monitoring. The practicality and stability of this integrated approach in complex environments were validated through temperature control experiments and field tests on real industrial wastewater.