| Summary: | Surface treatment and tanning industries use huge quantities of heavy metals—especially Chromium (III) and (VI)—in their processes thanks to its physical proprieties. It is used in the composition of special steels and refractory alloys. By dint of using this metal, an enormous quantity of rejects is produced each year and discharged into the oceans. As this is very dangerous for our environment, it is very important to treat these discharges before getting rid of them. This study treats chromium removal as a special type of heavy metals that can be a component of industrial discharges. Electrocoagulation is considered among the best methods used in this kind of treatment. However, it requires a lot of time, energy and remains expensive. This paper presents a predictive model in order to classify the chromium removal efficiency using electrocoagulation method. The proposed model is a logistic regression (LR) that consumes four parameters that we call predictors: pH, time, current, and stirring speed. After the training and validation process, we obtained 88% as classification precision, recall and F-Score metrics values while the use of the 10-Folds cross-validation method gave a minimal area under curve (AUC) value of 97% while the best value attempts 100%. Classification report states that the model performs well comparing to similar experimentation efficiencies.
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