Thiol-lignocellulose sodium bentonite (TLSB) nanocomposites can effectively remove heavy metals from aqueous solutions. TLSB was formed by using –SH group-modified lignocellulose as a raw material, which was intercalated into the interlayers of hierarchical sodium bentonite. Characterization of TLSB was then performed with BET, FTIR, XRD, TGA, PZC, SEM, and TEM analyses. The results indicated that thiol-lignocellulose molecules may have different influences on the physicochemical properties of sodium bentonite, and an intercalated–exfoliated structure was successfully formed. The TLSB nanocomposite was subsequently investigated to validate its adsorption and desorption capacities for the zinc subgroup ions Zn(II), Cd(II) and Hg(II). The optimum adsorption parameters were determined based on the TLSB nanocomposite dosage, concentration of zinc subgroup ions, solution pH, adsorption temperature and adsorption time. The results revealed that the maximum adsorption capacity onto TLSB was 357.29 mg/g for Zn(II), 458.32 mg/g for Cd(II) and 208.12 mg/g for Hg(II). The adsorption kinetics were explained by the pseudo-second-order model, and the adsorption isotherm conformed to the Langmuir model, implying that the dominant chemical adsorption mechanism on TLSB is monolayer coverage. Thermodynamic studies suggested that the adsorption is spontaneous and endothermic. Desorption and regeneration experiments revealed that TLSB could be desorbed with HCl to recover Zn(II) and Cd(II) and with HNO3 to recover Hg(II) after several consecutive adsorption/desorption cycles. The adsorption mechanism was investigated through FTIR, EDX and SEM, which demonstrated that the introduction of thiol groups improved the adsorption capacity. All of these results suggested that TLSB is an eco-friendly and sustainable adsorbent for the extraction of Zn(II), Cd(II) and Hg(II) ions in aqueous media.

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Lignocellulose (LCE) was ultrasonically treated and intercalated into magnesium aluminum silicate (MOT) clay to prepare a nano-lignocellulose magnesium aluminum silicate polymer gel (nano-LCE-MOT) for the removal of Zn (II) from aqueous solution. The product was characterised using nitrogen adsorption/desorption isotherm measurements, Fourier-transform infrared spectroscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. The conditions for the adsorption of Zn (II) on nano-LCE-MOT were screened, and adsorption kinetics and isotherm model analysis were carried out to explore the adsorption mechanism and achieve the optimal adsorption of Zn (II). Optimal adsorption was achieved at an initial Zn (II) concentration of 800 mg/L at 60 °C in 160 min at a pH of 4.52. The adsorption kinetics were explored using a pseudo-second-order model, with the isotherm adsorption equilibrium found to conform to the Langmuir model. The maximum adsorption capacity of the nano-LCE-MOT polymer gel toward Zn (II) is 513.48 mg/g. The materials with adsorbed Zn (II) were desorbed using different media, with HCl found to be the most ideal medium to desorb Zn (II). The optimal desorption of Zn (II) was achieved in 0.08 mol/L HCl solution at 65 °C in 60 min. Under these conditions, Zn (II) was almost completely desorbed from the adsorbents, with the adsorption effect after cycling being slightly different from that of the initial adsorption.

Some procedures or functions had be added to an ESTA (Expert System Shell for Text Animation) so that the ESTA and MATLAB can communicate via some data files.On this basis,a deep learning-DBN(Deep Belief Network) and two BP(back propagation) artificial neural network based on the MATLAB programming were researched by using directly DGA (Dissolved Gas Analysis) and characteristic gas method in transformer oil chromatographic analysis.The transformer fault diagnosis expert system based on a three ratio and characteristic gas method of DGA and ESTA including the DBN and two BP artificial neural network programmed in MATLAB had be created.The basic application shows the effectiveness of the expert system.

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