A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2...A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.展开更多
Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real str...Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.展开更多
An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China.Based on the perspective...An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China.Based on the perspective of industrial structure,the expanded KAYA equation to measure the energy related carbon emissions of the primary industries(Resources and Agriculture)and secondary industries(Manufacturing and Construction)and tertiary industries(Retail and Service)was utilized in Shandong Province from 2011 to 2017.The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach.The results were follows:(1)Under the three industrial dimensions,the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries.(2)The development level effect and the employment scale effect play a pulling role in carbon emissions.(3)From the perspective of the employment structure effect of the primary industry,there is a restraining effect on carbon emissions,while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions,and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.展开更多
Industrial processes are mostly large-scale systems with high order.They use fully centralized control strategy,the parameters of which are difficult to tune.In the design of large-scale systems,the decomposition acco...Industrial processes are mostly large-scale systems with high order.They use fully centralized control strategy,the parameters of which are difficult to tune.In the design of large-scale systems,the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure.In this paper,the decomposition principle of processes in large-scale systems is proposed for the design of control structure.A new variable pairing method is presented,considering the steady-state information and dynamic response of large-scale system.By selecting threshold values,the related matrix can be transformed into the adjoining matrixes,which directly measure the couple among different loops.The optimal number of controllers can be obtained after decomposing the large-scale system.A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.展开更多
As China's energy intensity fluctuated in recent years, it is necessary to examine whether this fluctuation happened at a regional level. This paper establishes a decomposition model by using the structural decomp...As China's energy intensity fluctuated in recent years, it is necessary to examine whether this fluctuation happened at a regional level. This paper establishes a decomposition model by using the structural decomposition analysis (SDA) method at a regional level. Then this model is employed to empirically analyze the changes of Beijing's energy intensity. The conclusions are as follows: during 2002-2010, except petroleum, the energy intensity decreased and the changes were mostly attributed to the technology changes, while the final use variation actually increased the energy intensity; comparing different periods of 2002-2010, the decline rates of energy intensity for coal and hydropower were decreasing, resulting from the production technology being more energy-intensive than before; the energy intensity changes of petroleum firstly increased substantially and then decreased moderately.展开更多
Analysis of carbon emission mechanism based on regional perspectives is an im- portant research method capable of achieving energy savings and emission reductions. Xin- jiang, an important Chinese energy production ba...Analysis of carbon emission mechanism based on regional perspectives is an im- portant research method capable of achieving energy savings and emission reductions. Xin- jiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets, is the key issue currently facing the region. This paper is based on the input-output theory, and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007; this analysis employs a hybrid input-output analysis framework of ＂energy - economy - carbon emissions＂. （1） Xinjiang＇s carbon emissions from energy con- sumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy re- sources, the mining of mineral resources, and the processing industry. （2） The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP, the final demand structure, the population scale, and the production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions. This showed that while the sizes of Xinjiang＇s economy and population were growing, the economic structure had not been effectively optimized and the production technology had not been efficiently improved, resulting in a rapid growth of carbon emissions from energy consumption. （3） The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export, fixed capital formation, and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. （4） The growth of investments in fixed assets of carbon intensive industry sectors, in addition to the growth of inter-provincial exports ofenergy resource products, makes the transfer effect of inter-provincial ＂embodied carbon＂ very significant.展开更多
This paper creates an extended import-competitive economy-energy-environmental input/output model and employs a structural decomposition analysis （SDA） approach based on double-layer nested structural formulae to br...This paper creates an extended import-competitive economy-energy-environmental input/output model and employs a structural decomposition analysis （SDA） approach based on double-layer nested structural formulae to break down China＇s carbon dioxide emissions growth between 1992 and 2007from three perspectives： the overall economy, by-industry and by industrial sectors. Analysis results indicate that the energy intensity effect remains the biggest factor behind carbon emissions reduction. This paper also .found that between 2002 and 2007, China＇s carbon emissions growth obviously accelerated compared to the previous period, which indicates a ＂high carbon＂ tendency in the new round of industrialization. Therefore, in addition to developing a circular economy and clean production, accelerating the phasing out of backward capacities, and developing new energies, China should further encompass the positive role of energy intensity.展开更多
基金Project(2012GK2025)supported by Science-Technology Plan Foundation of Hunan Province,ChinaProject(2013zzts039)supported by the Fundamental Research Funds for Central South University,China
文摘A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.
基金Project (No. PIFI-2012 U. de Gto.) supported by the Secretariat of Public Education (SEP), Mexico
文摘Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.
基金supported by the National Natural Science Foundation of China under Grants 71804089 and 71771138Humanities and Social Sciences Youth Foundation of Ministry of Education of China under Grants 18YJCZH034 and 19YJC790128+2 种基金Jiangsu Post-doctoral Research Funding Plan(2018K195C)Natural Science Foundation of Shandong Province,China under Grant ZR2018LG003Social Science Planning Project Foundation of Shandong Province,China under Grant 16CGLJ09.
文摘An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China.Based on the perspective of industrial structure,the expanded KAYA equation to measure the energy related carbon emissions of the primary industries(Resources and Agriculture)and secondary industries(Manufacturing and Construction)and tertiary industries(Retail and Service)was utilized in Shandong Province from 2011 to 2017.The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach.The results were follows:(1)Under the three industrial dimensions,the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries.(2)The development level effect and the employment scale effect play a pulling role in carbon emissions.(3)From the perspective of the employment structure effect of the primary industry,there is a restraining effect on carbon emissions,while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions,and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.
基金Supported by the National Natural Science Foundation of China(21006127)the National Basic Research Program of China(2012CB720500)
文摘Industrial processes are mostly large-scale systems with high order.They use fully centralized control strategy,the parameters of which are difficult to tune.In the design of large-scale systems,the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure.In this paper,the decomposition principle of processes in large-scale systems is proposed for the design of control structure.A new variable pairing method is presented,considering the steady-state information and dynamic response of large-scale system.By selecting threshold values,the related matrix can be transformed into the adjoining matrixes,which directly measure the couple among different loops.The optimal number of controllers can be obtained after decomposing the large-scale system.A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.
基金Supported by Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA05150600)National Natural Science Foundation of China (No. 71273027 and No. 70903066)Beijing Planning Office of Philosophy and Social Science (No. 11JGC105)
文摘As China's energy intensity fluctuated in recent years, it is necessary to examine whether this fluctuation happened at a regional level. This paper establishes a decomposition model by using the structural decomposition analysis (SDA) method at a regional level. Then this model is employed to empirically analyze the changes of Beijing's energy intensity. The conclusions are as follows: during 2002-2010, except petroleum, the energy intensity decreased and the changes were mostly attributed to the technology changes, while the final use variation actually increased the energy intensity; comparing different periods of 2002-2010, the decline rates of energy intensity for coal and hydropower were decreasing, resulting from the production technology being more energy-intensive than before; the energy intensity changes of petroleum firstly increased substantially and then decreased moderately.
基金National Natural Science Foundation of China, No.41501144 National Key Research and Development Program, No.2016YFA0602801+2 种基金 Guangdong Academy of Sciences Youth Science Foundation, No.qn.ij201501 High-level Leading Talent Introduction Program of GDAS, No.2016GDASRC-0101 Scientific Platform and Innovation Capability Construction Program of GDAS, No.2016GDASPT-0210.
文摘Analysis of carbon emission mechanism based on regional perspectives is an im- portant research method capable of achieving energy savings and emission reductions. Xin- jiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets, is the key issue currently facing the region. This paper is based on the input-output theory, and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007; this analysis employs a hybrid input-output analysis framework of ＂energy - economy - carbon emissions＂. （1） Xinjiang＇s carbon emissions from energy con- sumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy re- sources, the mining of mineral resources, and the processing industry. （2） The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP, the final demand structure, the population scale, and the production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions. This showed that while the sizes of Xinjiang＇s economy and population were growing, the economic structure had not been effectively optimized and the production technology had not been efficiently improved, resulting in a rapid growth of carbon emissions from energy consumption. （3） The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export, fixed capital formation, and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. （4） The growth of investments in fixed assets of carbon intensive industry sectors, in addition to the growth of inter-provincial exports ofenergy resource products, makes the transfer effect of inter-provincial ＂embodied carbon＂ very significant.
文摘This paper creates an extended import-competitive economy-energy-environmental input/output model and employs a structural decomposition analysis （SDA） approach based on double-layer nested structural formulae to break down China＇s carbon dioxide emissions growth between 1992 and 2007from three perspectives： the overall economy, by-industry and by industrial sectors. Analysis results indicate that the energy intensity effect remains the biggest factor behind carbon emissions reduction. This paper also .found that between 2002 and 2007, China＇s carbon emissions growth obviously accelerated compared to the previous period, which indicates a ＂high carbon＂ tendency in the new round of industrialization. Therefore, in addition to developing a circular economy and clean production, accelerating the phasing out of backward capacities, and developing new energies, China should further encompass the positive role of energy intensity.
