High-selectivity phenol detection in cumene process wastewater via bromination and dynamic optical path

Area of Science:

• Analytical Chemistry
• Environmental Science
• Chemical Engineering

Background:

• Phenol is a key industrial chemical, but its production generates complex wastewater.
• Traditional methods struggle to detect phenol accurately due to spectral overlap.
• Accurate phenol detection is crucial for managing industrial effluents.

Purpose of the Study:

• To develop a novel framework for selective and sensitive phenol monitoring in complex industrial wastewater.
• To overcome the limitations of traditional spectroscopic methods in analyzing industrial effluents.
• To enhance the detection limit and accuracy of phenol analysis.

Main Methods:

• Integration of chemical modulation with a custom-built, multidimensional sensor array.
• Utilized a U-shaped cuvette for dynamic adjustment of optical path length (1-10 cm).
• Employed a selective bromination reaction to eliminate phenol's spectral signature, creating a differential dataset.
• Applied machine learning algorithms, specifically Random Forest (RF), for data modeling.

Main Results:

• The Random Forest model achieved a high coefficient of determination (R² = 0.99826).
• The dynamic path length strategy reduced the limit of detection (LOD) to 0.0709 mg/L, a >5-fold improvement.
• Validated in river and marine water matrices with recovery rates between 95%-105%.

Conclusions:

• The novel framework offers a highly selective and sensitive method for phenol detection in complex industrial wastewater.
• Dynamic path length adjustment combined with chemical modulation and machine learning significantly enhances analytical performance.
• This approach provides a robust and accurate solution for environmental monitoring of industrial effluents.