Cooperative effect of sodium lauryl sulfate collector and sodium pyrophosphate depressant on the flotation separation of lead oxide minerals from hematite

As a cornerstone of the national economy,the iron and steel industry generates a significant amount of sintering dust containing both valuable lead resources and deleterious elements.Flotation is a promising technique for lead recovery from sintering dust,but efficient separation from Fe_(2)O_(3) is still challenging.This study investigated the cooperative effect of sodium lauryl sulfate(SLS,C_(12)H_(25)SO_(4)Na)and sodium pyrophosphate(SPP,Na_(4)P_(2)O_(7))on the selective flotation of lead oxide minerals(PbOHCl and PbSO_(4))from hematite(Fe_(2)O_(3)).Optimal flotation conditions were first identified,resulting in high recovery of lead oxide minerals while inhibiting Fe_(2)O_(3) flotation.Zeta potential measurements,Fourier transform infrared spectroscopy(FT-IR)analysis,adsorption capacity analysis,and X-ray photoelectron spectroscopy(XPS)studies offer insights into the adsorption behaviors of the reagents on mineral surfaces,revealing strong adsorption of SLS on PbOHCl and PbSO_(4) surfaces and remarkable adsorption of SPP on Fe_(2)O_(3).The proposed model of reagent adsorption on mineral surfaces illustrates the selective adsorption behavior,highlighting the pivotal role of reagent adsorption in the separation process.These findings contribute to the efficient and environmentally friendly utilization of iron ore sintering dust for lead recovery,paving the way for sustainable resource management in the iron and steel industry.

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Online Public Opinion Guidance Strategy for College Students in the Era of We Media

In the era of We Media, online public opinion in colleges and universities has become an important front of ideological and political, with some characteristics such as concealment, diversity and sensitivity, interaction and immediacy of communication. It is urgent to carry out some researches about network public opinion analysis and guidance mechanism. It is a new challenge that the ideological and political education in colleges and universities, must face how to guide online public opinion effectively. The team building should be strengthened and the right of public opinion should be returned. The platform construction should be enriched, and it is important to give the chance to various media. Therefore, it should strengthen the mechanism construction, and public opinion guidance must be scientific and professional. Colleges and universities should build a clear network space from such aspects as the construction of network public opinion guidance institutions, the cultivation of opinion leaders, the perfection of early warning mechanism, the improvement of university network literacy and the guidance of campus media.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

铸牢中华民族共同体与构建人类命运共同体的“三个维度”

本文从历史、现实和发展三个维度分析铸牢中华民族共同体与构建人类命运共同体的提出和演变历程、面临的现实挑战以及未来的融合发展路径,站在中华民族伟大复兴战略全局和世界百年未有之大变局的历史交汇点,中华民族共同体必将为构建人类命运共同体提供中国智慧、中国方案、中国力量。

中华文化助力构建人类命运共同体的三个切入点

中华民族具有五千多年连续不断的文明历史,其蕴含的中庸之道、和而不同等中华传统文化与应对共同挑战、迈向美好未来的人类命运共同体之间高度契合。人类命运共同体理念植根于博大精深的中华文化,中华文化必将在人类命运共同体构建过程中做出历史性的贡献。