Experimental Dataset for Machine Learning Modeling of Dye Removal by Moringa Oleifera Natural Coagulant

Published: 19 February 2026| Version 1 | DOI: 10.17632/f9pwy4mk5c.1
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Description

This dataset comprises 85 experimental runs focused on the optimization of dye removal from wastewater using Moringa oleifera as a natural coagulant. The experimental design follows a Rotational Central Composite Design (RCCD) to evaluate the influence of key operational parameters on treatment efficiency. The dataset includes: Input Variables: Initial dye concentration (Conc), Slow Mixing Time (SMT), Fast Mixing Time (FMT), and Coagulant Dosage. Output/Response Variables: Final Absorbance (Final ADS), Final Concentration, and Removal Efficiency (%). This data is intended for statistical optimization and the development of predictive Machine Learning models. It integrates both previously published data (used for validation) and original experimental results.

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Artificial Intelligence, Environmental Engineering, Water Science, Optimal Process Control

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