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This study uses molecular spectroscopy and genetic algorithms to estimate wastewater pollution levels like chemical oxygen demand (COD) and total suspended solids (TSS) in real-time. The findings pave the way for enhanced wastewater treatment plant monitoring.

Keywords:
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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Water Resource Management

Background:

  • Real-time monitoring of pollution loads in sewer networks is crucial for effective water resource management and environmental protection.
  • Molecular spectroscopy offers a non-invasive method for wastewater characterization by correlating physical-chemical conditions with spectral data.
  • Existing methods often require chemical analysis and pre-treatment, limiting real-time application.

Purpose of the Study:

  • To evaluate symbolic regression models, derived from evolutionary genetic algorithms, for estimating key wastewater parameters.
  • To determine the most suitable models and spectral wavelengths for correlating spectroscopic responses with pollutant concentrations.
  • To assess the feasibility of real-time monitoring of wastewater quality using spectroscopy.

Main Methods:

  • Utilized molecular spectroscopy to measure wastewater samples across a wavelength range of 380-700 nm.
  • Employed evolutionary genetic algorithms (Classical Genetic Algorithm, Age-Layered Population Structure, Offspring Selection) via HeuristicLab software to develop 18 distinct symbolic regression models.
  • Analyzed approximately 650 wastewater samples from 43 wastewater treatment plants, examining both raw/influent and treated/effluent stages.

Main Results:

  • Achieved Pearson correlation coefficients ranging from 67-91% for test data using combined models (raw and treated wastewater).
  • Identified specific models and wavelengths that show strong correlations for estimating chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), total phosphorus (TP), and total nitrogen (TN).
  • Demonstrated the potential of spectroscopic analysis without chemical pre-treatment for wastewater quality assessment.

Conclusions:

  • The developed symbolic regression models show significant promise for the real-time estimation of critical wastewater quality parameters.
  • This research represents a foundational step towards implementing real-time monitoring systems in wastewater treatment plants (WWTPs).
  • The non-invasive spectroscopic approach combined with advanced modeling offers an efficient alternative to traditional chemical analysis methods.