| The recovery and reuse of zinc and other valuable metals in leaching residues is a key segment in the green recycling of resources in the zinc hydrometallurgy industry.The typical process of zinc leaching residues treatment in rotary kilns is large delays,therefore,unstable zinc volatilization rate,high carbon emissions,serious kiln slag accumulation and other problems arise,which is hard to be optimized rapidly and regulated immediately.The research object is about the recovery engineering of leaching slag in the large-scale rotary kiln of 300,000tons/year in China.Based on a qualitative analysis of the influence of zinc volatilization rate,carbon emissions and kiln slag production parameters,the influence factors were analyzed quantitatively using grey correlation analysis.A genetic algorithm optimization BP neural network and particle swarm optimization BP neural network to predict the zinc volatilization rate,carbon emissions and kiln slag production had been established as a prioritization scheme in conjunction with a grey relational analysis of the main process parameters.Based on the single factor scenario analysis method,three model scenarios such as coke powder,kiln tail temperature and mainly associated element of Fe content in the leaching slag had been set up,which were applied to analyze the trend and the impact mechanism of three aspects on the three.And the multi-objective cuckoo algorithm and fast non-dominated sorting algorithm were used for its multi-objective optimization to obtain the optimal regulation interval.The theoretical guidance and technical support for the energy-efficient recovery of zinc from leaching residues and the optimal regulation of prevention and control of secondary pollution was demonstrated in the research.The main contents and conclusions are as follows:（1）The result of the grey relational analysis encompassing major material ratio,key process parameters and optimization targets showed that the input intensity of coke powder had the largest effect on zinc volatilization rate,showing a correlation of 0.842,and the water content of the leaching residue had the lowest correlation of 0.628.The correlation between input intensity of coke powder and carbon emissions was the largest with a coefficient of 0.854,and the Pb content of leaching slag had the lowest correlation with carbon emissions with a coefficient of 0.736.The coke powder input intensity was closely correlated with kiln slag production and the water content of the leaching residue was least correlated with the kiln slag production.The results provided parametric support for the input variables of the prediction model.（2）The BP model,the BP model optimized by the genetic algorithm and the BP model optimized by the particle swarm optimization algorithm were respectively used to construct the prediction model of zinc volatilization rate,carbon emissions and kiln slag production.The results showed that the particle swarm optimization algorithm had the best optimization effect with prediction errors being±0.9%,±0.06 t and±0.17 t,as well as a considerably high prediction hit rate.（3）It could be known from the single-factor scenario analysis that when the input intensity of coke powder was 0.6,the zinc volatilization rate could be as high as 92.3%.At a temperature of 680℃,the zinc volatilization rate was 92.7%,reaching the optimal kiln tail temperature control value.The Fe content of leaching residue rose from 20.2%to 27%and the zinc volatilization rate dropped from 95.8%to 88%.The carbon emissions increased along with a rise in input intensity of coke powder.For every 1%increase in Fe content of leaching residue,the carbon emissions increased by 6.7×10-2 t.When the kiln tail temperature rose from 504℃to 686℃,the carbon emissions dropped from 1.733 t to 1.72 t.Every 1 t coke powder could reduce the output of kiln slag by 0.06 t.When the Fe content of leaching residue rose from 20%to 26%,the kiln slag production rose from 0.59 t to 0.64 t.When kiln tail temperature rose from 504℃to 686℃,the kiln slag output dropped from 0.7 t to 0.575 t.（4）The multi-objective cuckoo search algorithm and the fast non-dominated sorting algorithm were applied to optimize multiple objectives including the highest zinc volatilization rate and lowest carbon emissions and kiln slag production.A comparison of optimization results showed that the fast non-dominated sorting algorithm had the best optimization effect.Compared with real working conditions,the highest zinc volatilization rate increased by nearly10%,carbon emissions dropped by 0.1 t at most and kiln slag production dropped by 0.38 t.In the meantime,the major material ratio and regulatory range of key process parameters were obtained. |
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