In the present study, biosynthesized ZnO nanoparticles in food wastewater extract (FWEZnO NPs) was used in the photocatalytic degradation of real samples of printing ink wastewater. FWEZnO NPs were prepared using green synthesis methods using a composite food waste sample (2 kg) consisted of rice 30%, bread 20 %, fruits 10 %, chicken 10 %, lamb 10%, and vegetable 20%. The photocatalysis process was optimized using response surface methodology (RSM) as a function of time (15-180 min), pH 2-10 and FWEZnO NP (20-120 mg/100 mL), while the print ink effluent after each treatment process was evaluated using UV-Vis-spectrophotometer. The behaviour of printing ink wastewater samples for photocatalytic degradation and responses for independent factors were simulated using feed-forward neural network (FFNN). FWEZnO NPs having 62.48 % of the purity with size between 18 and 25 nm semicrystalline nature. The main functional groups were -CH, CH2, and -OH, while lipid, carbon-hydrogen stretching, and amino acids were the main component in FWEZnO NP, which contributed to the adsorption of ink in the initial stage of photocatalysis. The optimal conditions for printing ink wastewater were recorded after 17 min, at pH 9 and with 20 mg/100 mL of FWEZnO NPs, at which the decolorization was 85.62 vs. 82.13% of the predicted and actual results, respectively, with R2 of 0.7777. The most significant factor in the photocatalytic degradation was time and FWEZnO NPs. The FFNN models revealed that FWEZnO NPs exhibit consistency in the next generation of data (large-scale application) with an low errors (R2 0.8693 with accuracy of 82.89%). The findings showing a small amount of catalyst is needed for effective breakdown of dyes in real samples of printing ink wastewater.
The optimal conditions of applied factors to reuse Aluminium AA6061 scraps are (450, 500, and 550) ⁰C preheating temperature, (1-15) % Boron Carbide (B4C), and Zirconium (ZrO2) hybrid reinforced particles at 120 min forging time via Hot Forging (HF) process. The response surface methodology (RSM) and machine learning (ML) were established for the optimisations and comparisons towards materials strength structure. The Ultimate Tensile Strength (UTS) strength and Microhardness (MH) were significantly increased by increasing the processed temperature and reinforced particles because of the material dispersion strengthening. The high melting point of particles caused impedance movements of aluminium ceramics dislocations which need higher plastic deformation force and hence increased the material's mechanical and physical properties. But, beyond Al/10 % B4C + 10 % ZrO2 the strength and hardness were decreased due to more particle agglomeration distribution. The optimisation tools of both RSM and ML show high agreement between the reported results of applied parameters towards the materials' strength characterisation. The microstructure analysis of Field Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscope (AFM) provides insights mapping behavioural characterisation supports related to strength and hardness properties. The distribution of different volumes of ceramic particle proportion was highlighted. The environmental impacts were also analysed by employing a life cycle assessment (LCA) to identify energy savings because of its fewer processing steps and produce excellent hybrid materials properties.