对不同粒度的超细煤粉进行了粒度粉细和煤质分析测定,研究了超细煤粉的煤质分析特性随粒度的变化规律。对超细煤粉的工业分析表明,水分含量基本不随煤粉粒度的变化而变化;随着煤粉颗粒粒度的减小,灰分含量增大,挥发分含量减小。对超细...对不同粒度的超细煤粉进行了粒度粉细和煤质分析测定,研究了超细煤粉的煤质分析特性随粒度的变化规律。对超细煤粉的工业分析表明,水分含量基本不随煤粉粒度的变化而变化;随着煤粉颗粒粒度的减小,灰分含量增大,挥发分含量减小。对超细煤粉的元素分析表明,由于煤粉偏析,随着煤粉颗粒粒度的减小,C,H 和 N 含量降低,O 和 S 含量增大。展开更多
Three industrial spent S Zorb sorbents extracted from production line were studied with XRD, TPR-MS and XPS. The characterization results of XPS and TPR-MS identified the existence of amorphous Ni_xS_y on industrial s...Three industrial spent S Zorb sorbents extracted from production line were studied with XRD, TPR-MS and XPS. The characterization results of XPS and TPR-MS identified the existence of amorphous Ni_xS_y on industrial spent S Zorb sorbents, while the existing XRD quantitative analysis methods can only provide the long-range order in phase information and the grain size of Ni metal. XPS is a powerful tool to investigate the chemical states of nickel atom and the depthwise distribution of nickel species on S Zorb sorbent. Ni_xS_y and Ni metal species coexist on the industrial spent sorbents, and their percentages to total nickel slightly change with the operating conditions in the surface layer. It proves that Ni_xS_y is a stable intermediate product rather than a transition state. The information can contribute to the better elucidation of S Zorb desulfurization mechanism and offer a new direction for selectivity optimization of industrial S Zorb sorbents.展开更多
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t...A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.展开更多
文摘对不同粒度的超细煤粉进行了粒度粉细和煤质分析测定,研究了超细煤粉的煤质分析特性随粒度的变化规律。对超细煤粉的工业分析表明,水分含量基本不随煤粉粒度的变化而变化;随着煤粉颗粒粒度的减小,灰分含量增大,挥发分含量减小。对超细煤粉的元素分析表明,由于煤粉偏析,随着煤粉颗粒粒度的减小,C,H 和 N 含量降低,O 和 S 含量增大。
基金the funding of the project by SINOPEC(No.114138)
文摘Three industrial spent S Zorb sorbents extracted from production line were studied with XRD, TPR-MS and XPS. The characterization results of XPS and TPR-MS identified the existence of amorphous Ni_xS_y on industrial spent S Zorb sorbents, while the existing XRD quantitative analysis methods can only provide the long-range order in phase information and the grain size of Ni metal. XPS is a powerful tool to investigate the chemical states of nickel atom and the depthwise distribution of nickel species on S Zorb sorbent. Ni_xS_y and Ni metal species coexist on the industrial spent sorbents, and their percentages to total nickel slightly change with the operating conditions in the surface layer. It proves that Ni_xS_y is a stable intermediate product rather than a transition state. The information can contribute to the better elucidation of S Zorb desulfurization mechanism and offer a new direction for selectivity optimization of industrial S Zorb sorbents.
基金Project(217/s/458)supported by Azarbaijan Shahid Madani University,Iran
文摘A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.
