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The cold meat industry is considered to be one of the main sources of organic pollutants in the wastewater of the meat sector due to the complex mixture of protein, fats, and dyes present. This study describes electrochemical coagulation (EC) treatment for the adsorption of organic pollutants reported in cold meat industry wastewater, and an artificial neural network (ANN) was employed to model the adsorption of chemical oxygen demand (COD). To depict the adsorption process, the parameters analyzed were current density (2–6 mA cm−2), initial pH (5–9), temperature (288–308 K), and EC time (0–180 min). The experimental results were fit to the Langmuir and Freundlich isotherm equations, while the modeling of the adsorption kinetics was evaluated by means of pseudo-first and pseudo-second-order rate laws. The data reveal that current density is the main control parameter in EC treatment, and 60 min are required for an effective adsorption process. The maximum removal of COD was 2875 mg L−1 (82%) when the following conditions were employed: pH = 7, current density = 6 mA cm−2, and temperature of 298 K. Experimental results obey second-order kinetics with values of the constant in the range of 1.176 × 10−5 ≤ k2 (mg COD adsorbed/g-Al.min) ≤ 1.284 × 10−5. The ANN applied in this research established that better COD removal, 3262.70 mg L−1 (93.22%) with R2 = 0.98, was found using the following conditions: EC time of 30.22 min, initial pH = 7.80, and current density = 6 mA cm−2. The maximum adsorption capacity of 621.11 mg g−1 indicates a notable affinity between the organic pollutants and coagulant metallic ions.

Adsorption of Organic Pollutants from Cold Meat Industry Wastewater by Electrochemical Coagulation: Application of Artificial Neural Networks