Fundamental research on selective arsenic removal from high-salinity alkaline wastewater.

The alkaline leaching process of arsenic-containing solid waste discharged during nonferrous metal smelting affords typical high-salinity alkaline arsenic-containing wastewater (HSAW). In this study, for the first time, Me (Ca2+ and Mg2+)-AsO43--OH--H2O and Me (Ca2+ and Mg2+)-AsO43--CO32--H2O systems are studied based on a thermodynamic equilibrium diagram and an arsenic removal experiment, proving that the removal of arsenic using single metal ions in the presence of CO32- is infeasible because of carbonate coprecipitation. Based on this observation, a new method that uses magnesium ammonium complex salts (MACSs) for HSAW treatment is proposed. Based on the thermodynamic calculations of the Mg2+-AsO43--NH4+-CO32--H2O system and the arsenic removal experiment, carbonate and arsenate can be selectively separated by the formation of magnesium ammonium arsenate (NH4MgAsO4·6H2O). In an arsenic solution containing 150-g/L Na2CO3, the arsenic removal rate and the arsenic grade of the precipitation product reach 90.16% and 27.13%, respectively, when the molar ratios of Mg2+/NH4+:As(V) are 1.8:1 and 2:1, respectively. The proposed method is successfully employed for treating a leaching solution of alkaline arsenic slag discharged during antimony smelting. The findings of this study will broaden the basic theory of HSAW treatment and lay a foundation for the resource treatment of arsenic-containing solid waste.

Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging.

Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68-1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter-based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD-RC-LSSVM model obtained the best prediction accuracy for TSS, with R p 2 $R_p^2$ of 0.93, with R M S E P $RMSEP$ of 0.6 %, with R E R $RER$ of 18.14 and R P D p $RP{D_p}$ of 3.69. The overall results indicated that the VMD-RC algorithm can be used as an alternative to handle high-dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. PRACTICAL APPLICATION: Traditional methods of evaluating grape quality attributes are destructive, time-consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.

Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.

Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study. First, the hyperspectral data of grape varieties are collected by using HSI instrument, and denoised by the proposed EEMD-DWT and other denoising algorithms. CARS-SPA (competitive adaptive reweighed sampling coupled with successive projections algorithm) is introduced to select the effective wavelengths and a discriminative model is constructed by using support vector machine (SVM). Finally, Monte Carlo experiments verified that EEMD-DWT was an effective and powerful spectra denoising method, and the SVM model constructed by combining with CARS-SPA had an excellent identification accuracy (99.3125%). The results suggested that the key wavelengths selected by using CARS-SPA and EEMD-DWT could be an alternative to the deal with HSI, and its potential to become a method for identifying grape varieties. PRACTICAL APPLICATION: Traditional grape varieties identification methods are destructive and time consuming. Therefore, HSI technology is applied to realize fast and nondestructive identification of grape varieties in this study. The research results indicate that it is feasible to combine HSI technology with machine learning algorithm to discriminate grape varieties. It is of great significance for grape grading and the promotion of excellent varieties, and also provides reference for grape industry producers to identify grape varieties quickly and accurately.