Tannery industrial effluent is one of the most difficult wastewater types since it contains a huge concentration of organic, oil, and chrome (Cr). This study successfully prepared and applied bimetallic Fe/Cu nanoparticles (Fe/Cu NPs) for chrome removal. In the beginning, the Fe/Cu NPs was equilibrated by pure aqueous chrome solution at different operating conditions (lab scale), then the nanomaterial was applied in semi full scale. The operating conditions indicated that Fe/Cu NPs was able to adsorb 68% and 33% of Cr for initial concentrations of 1 and 9 mg/L, respectively. The removal occurred at pH 3 using 0.6 g/L Fe/Cu dose, stirring rate 200 r/min, contact time 20 min, and constant temperature 20 ± 2ºC. Adsorption isotherm proved that the Khan model is the most appropriate model for Cr removal using Fe/Cu NPs with the minimum error sum of 0.199. According to khan, the maximum uptakes was 20.5 mg/g Cr. Kinetic results proved that Pseudo Second Order mechanism with the least possible error of 0.098 indicated that the adsorption mechanism is chemisorption. Response surface methodology (RSM) equation was developed with a significant p-value = 0 to label the relations between Cr removal and different experimental parameters. Artificial neural networks (ANNs) were performed with a structure of 5-4-1 and the achieved results indicated that the effect of the dose is the most dominated variable for Cr removal. Application of Fe/Cu NPs in real tannery wastewater showed its ability to degrade and disinfect organic and biological contaminants in addition to chrome adsorption. The reduction in chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), Cr, hydrogen sulfide (H ₂ S), and oil reached 61.5%, 49.5%, 44.8%, 100%, 38.9%, 96.3%, 88.7%, and 29.4%, respectively.

Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD mg/L, mixing rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (: 0.972, Adj-: 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as . A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process.

Fungi are typically expressed as excellent microorganisms that produce extracellular enzymes used in the bioaccumulation phenomenon. In this study, laboratory-scale dual-chamber fungal fuel cells (FFCs) were applied as an alternate approach for the available degradation of complex organic pollutants represented in chemical oxygen demand (COD) and total nitrogen (TN), as well as inorganic pollutants represented as total chromium (Cr), and the generation of bioenergy represented in output voltages (V), power density (PD) and current density (CD), as applied to tannery effluent. Aspergillus niger strain, (A. niger), which makes up 40% of the fungal population in tannery effluent was examined in a training study for efficient hexavalent chromium bioaccumulation, especially in high concentrations. The trained A. niger showed a faster growth rate than the untrained one in broth media containing different loaded chromium concentrations. For an external resistance of 1000 Ω, two FFCs were utilized, one with electrolytic matrices including phosphate buffer solution (PBS) and bicarbonate buffer solution (BBS), and the other without electrolytic matrices, where the energy generation and treatment efficacy of the two dual-chamber FFCs were evaluated for a period of 165 h. At 15 h, the electrolytic FFCs showed a high voltage output of 0.814 V, a power density of 0.097 mW·m⁻², and a current density of 0.119 mAm⁻² compared to the non-electrolytic FFC. At 165 h, the electrolytic FFCs showed high removal efficiency percentages for COD, TN, and total Cr of up to 77.9%, 94.2%, and 73%, respectively, compared to the non-electrolytic FFC.