In this paper, electrocoagulation (EC) treatment for the removal of chemical oxygen demand (COD) from cold meat industry wastewater is modeled and optimized using computational techniques. Methods such as artificial neural networks (ANNs) and response surface methodology (RSM), based on the Box–Behnken design using three levels, were employed to calculate the best control parameters for pH (5–9), current density (2–6 mA/cm²) and EC time (20–60 min). Analysis of variance (ANOVA) and 3D graphs revealed that pH and current density are the main parameters used for depicting the EC process. The developed models successfully describe the process with a correlation coefficient of R² = 0.96 for RSM and R² = 0.99 for ANN. The models obtained were optimized applying the moth-flame optimization (MFO) algorithm to find the best operating conditions for COD removal. ANN-MFO was used and showed superior COD removal (92.91%) under conditions of pH = 8.9, current density = 6.6 mA/cm² and an EC time of 38.62 min. The energy consumption with these optimal conditions was 6.92 kWh/m³, with an operational cost of $3.14 (USD)/m³. These results suggest that the proposed computational model can be used to obtain more effective and economical treatments for this type of effluent.

Green materials are emerging as sustainable alternatives in water and wastewater treatment. Due to their biodegradability, renewable origin and low toxicity characteristics, green materials are an alternative to conventional synthetic materials. Green materials include nanomaterials of natural origin, biopolymers and composites that optimize the adsorption and removal of contaminants. The applications of cellulose nanofibers, alginates, chitosan and lignin stand out, as well as functionalized hydrogels and aerogels for the removal of heavy metals, dyes and organic contaminants. The analysis of the mechanisms and processes of contaminant removal and modeling and optimization techniques are included as key emerging tools for the design and optimization of these materials, allowing one to predict properties, simulate interactions and customize solutions. Despite the sustainability benefits of green materials, they face technical and economic challenges, such as scalability, synthesis costs and experimental validation. This work concluded that green materials, combined with modeling and optimization tools, are essential to move towards more sustainable, efficient and environmentally friendly water treatment technologies, aligned with global objectives of sustainable development and climate change mitigation.

In this study, titanium oxide TiO₂ nanoparticles were produced using the sol–gel approach of green synthesis with pectin as the reducing agent. The synthetized TiO₂ nanoparticles with pectin were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), visible light absorption (UV–Vis) and the BET method. The structure and morphology of the TiO₂ powder were described with SEM, revealing uniform monodisperse grains with a distribution of 80% regarding sizes < 250 nm; the resulting crystal phase of synthetized TiO₂ was identified as an anatase and rutile phase with a crystallinity size estimated between 27 and 40 nm. Also, the surface area was determined by nitrogen adsorption–desorption using the Brown–Emmet–Teller method, with a surface area calculated as 19.56 m²/g, typical of an IV type isotherm, indicating mesoporous NPs. UV–Vis spectra showed that sol–gel synthesis reduced the band gap from the 3.2 eV common value to 2.22 eV after estimating the optical band gap energy using the adsorption coefficient; this translates to a possible extended photo response to the visible region, improving photoactivity. In addition, the power conversion of the photoelectrode was compared based on similar assembly techniques of TiO₂ electrode deposition. Quantum dot crystals were deposited ionically on the electrode surface, as two different paste formulations based on a pectin emulsifier were studied for layer deposition. The results confirm that the TiO₂ paste with TiO₂-synthesized powder maintained good connections between the nanocrystalline mesoporous grains and the deposited layers, with an efficiency of 1.23% with the transparent paste and 2.27% with the opaque paste. These results suggest that pectin could be used as a low-cost, functional sol–gel catalysis agent for the synthesis of controlled NPs of metal oxide. It demonstrates interesting optical properties, such as an increase in photo response, suggesting further applications to photocatalysts and biomedical features.

In this paper, a model for Cr (VI) removal and optimization was made using a novel aerogel material, chitosan-resole CS/R aerogel, where a freeze-drying and final thermal treatment was employed to fabricate the aerogel. This processing ensures a network structure and stability for the CS, despite the non-uniform ice growth promoted by this process. Morphological analysis indicated a successful aerogel elaboration process., FTIR spectroscopy corroborated the aerogel precursor’s identity and ascertained chemical bonding after adsorption. Owing to the variability of formulations, the adsorption capacity was modeled and optimized using computational techniques. The response surface methodology (RSM), based on the Box–Behnken design using three levels, was used to calculate the best control parameters for the CS/R aerogel: the concentration at %vol (50–90%), the initial concentration of Cr (VI) (25–100 mg/L), and adsorption time (0.3–4 h). Analysis of variance (ANOVA) and 3D graphs reveal that the CS/R aerogel concentration and adsorption time are the main parameters that influence the initial concentration of CS/R aerogel metal-ion uptake. The developed model successfully describes the process with a correlation coefficient of R² = 0.96 for the RSM. The model obtained was optimized to find the best material design proposal for Cr (VI) removal. Numerical optimization was used and showed superior Cr (VI) removal (94.4%) under conditions of a CS/R aerogel concentration of 87/13 %vol, with an initial concentration of Cr (VI) of 31 mg/L, and an adsorption time of 3.02 h. These results suggest that the proposed computational model can obtain an effective and viable model for CS material processing and for optimization of the uptake of this metal.

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.

In this study, a novel chitosan–resole–pectin aerogel (CS–R–P) was created from a sol–gel reaction with a solution of Cs and P with resole by a freeze-drying technique, and this adsorbent was proposed for the removal of methylene blue (MB). In addition, with the use of an artificial intelligence technique known as an artificial neural network (ANN), this material was modeled and optimized. Its physical morphology and chemical composition were also characterized with FTIR and XPS, and its adsorption properties were analyzed. For modeling the adsorption process, three main parameters were used: the chitosan–resole–pectin concentration (45–75%), thermal treatment (6–36 h), and known concentrations of methylene blue (25–50 and 100 mg/L), established on the Box–Behnken design. The ANN was coupled with the improved gray wolf optimization (IWGO) metaheuristic algorithm, achieving a correlation coefficient of R² = 0.99. The characterization indicates that the surface of the aerogels was micro- and mesoporous, the resole gave physical stability, and the polysaccharide base delivered the functional groups necessary for dye adsorption; the aerogels were successful dye adsorbents with a q_e of 12.44 mg/g. Finally, the physical and chemical sorption was ascertainable with an adsorption that followed pseudo-second-order kinetics. The MB adsorption was clearly occurring though cation exchange and hydrogen binding as observed in the chemical composition. The ANN with the gray wolf optimizer was used for the prediction of the best operating parameters for MB removal, applying the following conditions—the CS–R–P aerogel concentration (52/30/18), the thermal treatment (9.12 h), and the initial concentration of methylene blue (37 mg/L)—achieving a 94.6% removal. These conclusions suggest that using artificial intelligence such as an ANN can provide an efficient and practical model for maximizing the removal action of new aerogels based on chitosan.