The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality ...The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.展开更多
以云南某锌厂提供的复杂挥发窑渣为研究对象,在理论分析的基础上,采用H_2O_2-H_2SO_4水溶液体系常压条件下协同浸出其中的有价金属。以In、Cu及Zn浸出率为考察指标,探讨了H_2O_2用量、硫酸浓度、反应温度、反应时间、液固比等因素对In...以云南某锌厂提供的复杂挥发窑渣为研究对象,在理论分析的基础上,采用H_2O_2-H_2SO_4水溶液体系常压条件下协同浸出其中的有价金属。以In、Cu及Zn浸出率为考察指标,探讨了H_2O_2用量、硫酸浓度、反应温度、反应时间、液固比等因素对In、Cu、Zn浸出率的影响。结果表明,在H_2O_2(30%)用量0.6 m L/g、硫酸浓度3 mol/L、反应温度80℃、反应时间2 h、液固比6∶1条件下,In浸出率93.92%、Cu浸出率89.84%、Zn浸出率66.49%。浸出渣中贵金属Ag含量大于0.01%,富集比3.23,初步实现了窑渣中有价金属的分离与综合利用。展开更多
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(61988101,62073340,and 61860206014)+2 种基金in part by the Major Key Project of Peng Cheng Laboratory(PCL)(PCL2021A09)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083,2021RC3018,and 2021RC4054)in part by the Innovation-Driven Project of Central South University,China(2019CX020)。
文摘The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.
文摘以云南某锌厂提供的复杂挥发窑渣为研究对象,在理论分析的基础上,采用H_2O_2-H_2SO_4水溶液体系常压条件下协同浸出其中的有价金属。以In、Cu及Zn浸出率为考察指标,探讨了H_2O_2用量、硫酸浓度、反应温度、反应时间、液固比等因素对In、Cu、Zn浸出率的影响。结果表明,在H_2O_2(30%)用量0.6 m L/g、硫酸浓度3 mol/L、反应温度80℃、反应时间2 h、液固比6∶1条件下,In浸出率93.92%、Cu浸出率89.84%、Zn浸出率66.49%。浸出渣中贵金属Ag含量大于0.01%,富集比3.23,初步实现了窑渣中有价金属的分离与综合利用。
