Machine Learning Methods Applied for Wastewater pH Neutralization Process Modeling

In the present paper are compared six machine learning techniques (decision tree regression, linear regression, KN eighbors regression, support vector regression, gradient boosting regression, and random forest regression) using Python 3.9 software, in order to identify the most appropriate machine learning technique for wastewater pH process modeling. The purpose is to find a data-driven type model for such a process (with strong nonlinearity in dynamic behavior), a model that can be used by the plant human operator as support in the decision-making process regarding the reagent (acid or alkaline) dosing flow.