RESUMO
Measuring the tensile strength, wear resistance, and impact strength of metals, particularly cast iron, is complex and more expensive than performing hardness tests. In the present study, owing to the ease of specimen preparation and low cost, the Hardness (HB) test was used to approximately predict Wear Rate (WR), Impact Energy (IE), and tensile strength (TS). The relation between Mg% and HB, tensile strength, WR, and IE was examined by using three experimental groups of compacted graphite cast iron (CGI) treated with a nodulizer (Fe-Si-Mg) alloy at different carbon equivalents (CEs) of 3.5, 4.0, and 4.5 %. The produced CGI exhibited HB, TS, WR, and IE of 191-226 HB, 402-455 MPa, 30.1-23.8 mg/cm2, and 22-15 J, respectively. The good results were taken at a CE of 4.5 % and Mg content of 0.0118-0.0155 %. the regression analysis and artificial neural network model (ANNs) were used in the hardness test, and the results indicated the possibility of predicting IE, WR, tensile strength, and high accuracy Mg% of the produced CGI. It could be observed that, the neural network algorithm model has a high prediction precision for determining the Mg% content and the properties of the prepared CGI based on hardness. In the case of CE = 4, the MSE calculated for the predicted and measured data taken from the used ANNs model is 3.7 E-8, 20.33, 0.3084, and 0.099 for Mg%, TS, WR, and IE, respectively.
RESUMO
The significant increase in lithium batteries consumption produces a significant quantity of discarded lithium-ion batteries (LIBs). On the one hand, the shortage of high-grade ores leads to the necessity of processing low-grade ores, which contain a low percentage of valuable metals in comparison to the discarded LIBs that contain a high percentage of these metals, which enhances the processing of the discarded LIBs. On the other hand, the processing of discarded LIBs reduces the negative environmental effects that result from their storage and the harmful elements contained in their composition. Hence, the current study aims at developing cost-effective and ecofriendly technology for cobalt and lithium metal ion recovery based on discarded LIBs. A novel synthesized solid-phase adsorbent (TZAB) was utilized for the selective removal of cobalt from synthetic solutions and spent LIBs. The synthesized TZAB adsorbent was characterized by using 13C-NMR, GC-MS, FT-IR, 1H-NMR, and TGA. The factors affecting the adsorption of cobalt and lithium ions from synthetic solutions and spent LIBs, including the sorbent dose, pH, contact time, temperature, and cobalt concentration were investigated. The conditions surrounding the recovery of cobalt and lithium from processing discarded LIBs, were investigated to optimize the maximum recovery. The Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherm models were used to study the kinetics of the adsorption process. The obtained results showed that high-purity CoC2O4 and Li3PO4 were obtained with a purity of 95% and 98.3% and a percent recovery of 93.48% and 95.76%, respectively. The maximum recovery of Co(II) from synthetic solutions was obtained at C0 = 500 mg·L-1, dose of 0.08 g, pH 7.5, T = 25 °C, and reaction time = 90 min. The collected data from Langmuir's isotherm and the adsorption processes of Co agree with the data predicted by the D-R isotherm models, which shows that the adsorption of Co(II) onto the TZAB seems to be chemisorption, and the results agree with the Langmuir and D-R isotherm models.
