Effect of NaNO2 Reductant Pretreatment on Low-Rank Coal Flotation.

As the reserves of high-quality coal resources in China are decreasing, it is imperative to improve the processing and comprehensive utilization of low-rank coal. In this study, NaNO2 was used for the flotation pretreatment test of the low-rank coal obtained from Majialiang, and the mechanism was discussed by contact angle analysis, zeta potential measurements, and XPS peak fitting analysis. The results showed that when the dosage of NaNO2 was 2000 g/t and the pretreatment time was 5 min, the flotation effect was the best, the ash contents of concentrate ash and tailings and the combustible recovery were 17.15, 37.12, and 42.23%, respectively; the combustible recovery increased by 12%. The contact angle, surface functional group content, and zeta potential measurements showed that with the change of NaNO2 dosage, the content of the hydrophobic functional group and the zeta potential value were consistent with the change of combustible recovery. The increase of hydrophobic functional groups can effectively enhance the hydrophobic interaction on the surface of the coal, which is conducive to the combination of collector and coal, and improve the efficiency of the collector. The NaNO2 pretreatment test can promote flotation efficiency, and the addition of reductant is an effective method for the flotation efficiency of low-rank coal in reducing oxygen-containing functional groups on the surface of low-rank coal to improve the poor floatability. In this study, the method of chemical pretreatment is put forward to provide a new idea for slime flotation.

Physical and Chemical Properties of Coal Gasification Fine Slag and Its Carbon Products by Hydrophobic-Hydrophilic Separation.

Coal gasification fine slag is a kind of solid waste with low resource utilization rate. The complex embedding of residual carbon and inorganic minerals (ash materials) is the main reason restricting the efficient resource separation and utilization of residual carbon or ash materials. Hydrophobic-hydrophilic separation (HHS) is a separation technology in which mineral particles with different surface hydrophobicity values are enriched in the water phase and oil phase under the action of mechanical stirring. The water on the surface of hydrophobic particles is replaced by the oil phase to form flocs, which are enriched in the hydrophobic liquid phase, while hydrophilic particles are dispersed into the aqueous phase. In this study, the HHS process was used to separate the carbon/ash from the fine gasification slag produced by a Shenning gasifier, Texaco gasifier, and GSP gasifier of Ningxia Coal Industry Co., Ltd. The physicochemical properties of the original sample and the residual carbon products obtained by hydrophobic-hydrophilic separation were analyzed. The results show that HHS can separate the carbon/ash in the three kinds of fine slag to varying degrees. The carbon element is enriched into the hydrophobic phase to form the concentrates, while the silicon element, oxygen element, and metal element enter the tailings. The spherical ash with different particle sizes distributed on the surface of residual carbon and the gap of the matrix is basically removed, while the ash in the carbon-ash melt is difficult to remove. The ash contents of the concentrate and tailings of fine slag of the Shenning gasifier are 22.58 and 96.28%, respectively, which reach the best ash index compared with that of the other two gasifiers. From the change of mineral surface properties after HHS, the distribution of oxygen-containing groups, benzene rings, Si-O, and clay minerals or carbonate minerals in the three kinds of fine slag residual carbon products is basically similar. Compared with the other two gasifier products, the GSP gasifier concentrate has a larger specific surface area and less ash material, more amorphous carbon structures (less graphitic), and more active sites, resulting in a stronger combustion activity.

A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process.

Intelligent separation is a core technology in the transformation, upgradation, and high-quality development of coal. Realising the intelligent recognition and accurate classification of coal flotation froth is a key technology of intelligent separation. At present, the coal flotation process relies on artificial recognition of froth features for adjusting the reagent dosage. However, owing to the low accuracy and subjectivity of artificial recognition, some problems arise, such as reagent wastage and unqualified product quality. Thus, this paper proposes a new froth image classification method based on the maximal-relevance-minimal-redundancy (MR MR)-semi-supervised Gaussian mixture model (SSGMM) hybrid model for recognition of reagent dosage condition in the coal flotation process. First, the features of morphology, colour, and texture are extracted, and the optimal froth image features are screened out using the maximal-relevance-minimal-redundancy (MRMR) feature selection algorithm based on class information. Second, the traditional GMM clusterer is improved, called SSGMM, by introducing a small number of marked samples, the traditional GMM' problems of unclear training goals, invisible clustering results, and artificially judged clustering results are solved. Then a new hybrid classification model is proposed by combining the MRMR with the modified GMM (SSGMM) which can be named as (MRMR - SSGMM). The optimal froth image features are screened by MRMR to provide the SSGMM classifier. In the process of training and learning the feature samples, using the marked feature samples of froth images to guide the unmarked feature samples. The information of marked feature samples of froth images is mapped to the unmarked feature samples, the classification of the froth images were realised. Finally, the accuracy of the SSGMM classifier is used as the evaluation criterion for the screened features by MRMR. By automatically executing the entire learning process to find the best number of froth image features and the optimal image features, so that the classifier achieves the maximum classification accuracy. Experimental results show that the proposed classification method achieves the best results in accuracy and time, compared with other benchmark classification methods. Application results show that the method can provide reliable guidance for the adjustment of the reagent dosage, realize the accurate and timely control of the reagent dosage, reduce the consumption of the reagent and the incidence of production accidents, and stabilize the product quality in the coal flotation production process.

Switching and optimizing control for coal flotation process based on a hybrid model.

Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance.