Phenol Pollutant Cleanup: A New Approach with Genetic Programming

Traditional methods for breaking down pollutants can be tough to nail down. Scientists often rely on complex equations that need lots of details about how the organism works. This can lead to big differences between what’s expected and what actually happens. Enter Genetic Programming (GP), a new way to model how microbes clean up messes like phenol pollutants. In this study, GP was used with the microalgae Acutodesmus Obliquus to see how well it could break down phenol. After 216 hours, phenol degradation hit 98%. Both traditional biokinetic methods and GP were compared to find the maximum specific growth rate (µmax) and saturation constant (Ks).

Guess what? GP can find a mathematical model that fits the Monod kinetics, even without knowing the form of the model beforehand. This shows that data-driven models can quickly and effectively reveal the underlying principles of biokinetic models. The GP model was tested using root mean square error (RMSE) and the correlation coefficient (R). It scored big on predictive accuracy, outperforming traditional methods.

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