Measuring how the pollution load evolves in real time along sewer networks is key for proper management of water resources and protecting the environment. The technique of molecular spectroscopy for water characterization has increasingly widespread use, as it is a non-invasive technique that leads to the correlation of the physical-chemical conditions of wastewater with spectroscopic surrogates by a series of mathematical estimation models. In the present research work, different symbolic regression models obtained with evolutive genetic algorithms are evaluated for the estimation of chemical oxygen demand (COD); five-day biochemical oxygen demand (BOD5); total suspended solids (TSS); total phosphorus (TP); and total nitrogen (TN), from the spectral response of samples measured between 380 and 700 nm and without the use of chemicals or pre-treatment. Around 650 wastewater samples were used in the campaign, from 43 different wastewater treatment plants (WWTP) in which both, raw/influent and treated/effluent, were examined through 18 models composed of Classical Genetic Algorithm (CGA), the Age-Layered Population Structure (ALPS), and Offspring Selection (OS) by mean of HeuristicLab software, to make a comparison among them and to determine which models and wavelengths are most suitable for the correlation. Models are proposed considering both raw and treated samples together (15) and only with tertiary treated wastewater reclaimed for agriculture irrigation effluent (3). The Pearson correlation coefficients were in the range of 67-91% for the test data in the case of the combined models. The results conform the first steps for a real-time monitoring of WWTP.
Keywords: Genetic algorithm; LED spectrophotometer; Symbolic regression; Wastewater pollutant characterization.
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