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Analysis and Characterization of Sludge Produced by Natural Extract-Facilitated Electrocoagulation for Hardness Removal
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DOI：10.3390/w18161983.png)
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Abstract
En 中文
This study focused on the generation and characterization of sludge produced by electrocoagulation (EC) combined with Moringa oleifera seed extract (MOSE) to remove water hardness. First, an experimental data set was generated and used as the baseline data for mathematical modeling to identify the effects of different parameters on Ca2+ and Mg2+ ion hardness removal. Then, using the generated data set, operational conditions were optimized using neural networks integrated with a genetic algorithm, resulting in the selection of Fe electrodes, 12.5 mL of MOSE per 100 mL of water, a current density (j) of 49.16 mA cm−2, and a reaction time of 5.3 min, considering Ca2+ ions as the sample contaminant. Additionally, machine learning analysis identified contaminant type, reaction time, and cathode material as the most influential variables affecting sludge formation, with optimal conditions identified for both Ca2+ and Mg2+ ion systems. For all the mathematical models, experimental validation was performed. The MOSE extract was characterized for the presence of proteins, polyphenols, flavonoids, and polysaccharides, which provide functional groups that promote aggregation and floc development. Sludge characterization by FT-IR, TGA, and TEM revealed the formation of organic–inorganic hybrid matrices composed of biomolecules interacting with electrochemically generated Fe3+ and Al3+ species, as well as Ca2+ and Mg2+ ions. These results highlight the role of plant-derived biomolecules in modulating the sludge structure and composition, providing insight into the mechanisms of sludge formation and the implications for handling and valorization of EC-based water treatment systems.
Keywords:
<i>Moringa oleifera</i>
electrocoagulation
water hardness
machine learning
artificial neural networks
genetic algorithm
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3.1W
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7.4W
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