A process-level hierarchical structural decomposition analysis (SDA) of energy consumption in an integrated steel plant

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.

Structural Decomposition Analysis on Energy Intensity Changes at Regional Level

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.

APPLICATION OF DOMAIN DECOMPOSITION IN ACOUSTIC AND STRUCTURAL ACOUSTIC ANALYSIS

Conventional element based methods for modeling acoustic problems are limited to low-frequency applications due to the huge computational efforts. For high-frequency applications, probabilistic techniques, such as statistical energy analysis （SEA）, are used. For mid-frequency range, currently no adequate and mature simulation methods exist. Recently, wave based method has been developed which is based on the indirect TREFFTZ approach and has shown to be able to tackle problems in the mid-frequency range. In contrast with the element based methods, no discretization is required. A sufficient, but not necessary, condition for convergence of this method is that the acoustic problem domain is convex. Non-convex domains have to be partitioned into a number of （convex） subdomains. At the interfaces between subdomains, specific coupling conditions have to be imposed. The considered two-dimensional coupled vibro-acoustic problem illustrates the beneficial convergence rate of the proposed wave based prediction technique with high accuracy. The results show the new technique can be applied up to much higher frequencies.

Interaction Analysis and Decomposition Principle for Control Structure Design of Large-scale Systems

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.

Factor Decomposition and Regression Analysis of the Energy Related Carbon Emissions in Shandong, China: A Perspective of Industrial Structure

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.

A SINGULAR VALUE DECOMPOSITION BASED TRUNCATION ALGORITHM IN SOLVING THE STRUCTURAL DAMAGE EQUATIONS

The structural damage identification through modal data often leads to solving a set of linear equations. Special numerical treatment is sometimes required for an accurate and stable solution owing to the ill conditioning of the equations. Based on the singular value decomposition (SVD) of the coefficient matrix, an error based truncation algorithm is proposed in this paper. By rejection of selected small singular values, the influence of noise can be reduced. A simply-supported beam is used as a simulation example to compare the results to other methods. Illustrative numerical examples demonstrate the good efficiency and stability of the algorithm in the nondestructive identification of structural damage through modal data.

An Analysis of the Increase of CO2 Emissions in China： Based on an SDA technique

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.

Structural Decomposition Analysis of the Decline in China's CO_2 Emission Intensity 2005–2010

Here we utilize input-output tables for 2005 and 2010 to calculate the change in carbon dioxide emission intensity. Results show that total carbon dioxide emissions were 6.79 and 9.30 billion tons, and carbon dioxide emission intensity was 0.37 and 0.33 ton per thousand CNY in 2005 and 2010, respectively. Carbon dioxide emission intensity declined 11% over these five years. We used structural decomposition analysis modeling to measure the effect of four factors on this reduction in intensity. We found that the contribution values of energy structure, energy efficiency, economic growth mode and economic structure were -0.001, -0.102, 0.050, and 0.013 ton per thousand CNY, respectively. Changes in energy efficiency and energy structure are major factors promoting decreases in carbon dioxide emission intensity; the effect of the former is more distinct than the latter. Economic growth mode and economic structure are major factors that increase carbon dioxide emission intensity, whereby the effect of the former is more distinct than the latter.

基于IO-SDA法的2020~2060年中国行业CO_(2)排放预测与分析

采用IO-SDA法测算2017~2020年CO_(2)排放,后结合国内权威预测报告,利用RAS法推导2025~2060年投入产出表和各行业能源消费数据,利用IO-SDA法定量评价各驱动因素在2020~2060年对CO_(2)排放总量和各行业CO_(2)排放量变化的贡献.结果表明:2020~2060年中国行业CO_(2)排放总量呈现先增后降的趋势,2030年左右达峰,后经历平台期,2035年开始快速减排,2050年后减排速度变慢;CO_(2)排放增长的驱动因素为最终需求规模,最主要的促降因素为碳排放强度;投入产出结构和最终需求结构只在个别时期促进CO_(2)排放增长,总体上对CO_(2)减排有积极作用,但作用有限,且伴随着经济结构趋于稳定,这两者的作用逐渐削弱;CO_(2)排放总量和4项因素的变化幅度在达峰平台期、快速减排期与全面中和期3个阶段由小变大再变小;从行业角度看,电力等公共品生产和供应业、金属产品制造业、交通运输仓储和邮政业、化学工业是CO_(2)减排的主要动力.为有效实现CO_(2)减排目标,政府实施CO_(2)减排政策的过程中,应在继续充分发挥碳排放强度积极效应的同时,更加注重释放重点行业技术创新、最终需求结构优化对CO_(2)减排的积极作用.

A DECOMPOSITION METHOD OF STRUCTURAL DECOMPOSITION ANALYSIS

Over the past two decades, structural decomposition analysis (SDA) has developed into a major analytical tool in the field of input-output (IO) techniques, but the method was found to suffer from one or more of the following problems. The decomposition forms, which are used to measure the contribution of a specific determinant, are not unique due to the existence of a multitude of equivalent forms, irrational due to the weights of different determinants not matching, inexact due to the existence of large interaction terms.In this paper, a decomposition method is derived to overcome these deficiencies, and we prove that the result of this approach is equal to the Shapley value in cooperative games,and so some properties of the method are obtained. Beyond that, the two approaches that have been used predominantly in the literature have been proved to be the approximate solutions of the method.

Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis

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.

Structural Decomposition Analysis to Investigate the Changes in Energy Consumption in Pakistan

The world energy demand is increasing due to rapid growth in the global economy,industrialization,and urbanization.Pakistan is also confronted with increasing energy demand on one hand and is confronted with the challenge of energy demand-supply gap on the other hand.Since energy is the major driver for growth,it becomes important to investigate the trends of energy consumption in a country and the factors that are most affecting the changes in the use of energy.This particular study aims to investigate the use of energy by all the economic sectors of Pakistan during 2000–2012.The major contribution is the first time application of structural decomposition analysi for energy usage along with using Input-Output data for the period of 2002–2012.The results show the fluctuation of the energy intensity of the sectors throughout the study period.Also,the overall effect of energy intensity is negative on energy consumption and it shows a negative contribution value of-80.90%for the study period.Furthermore,the focus on more energy-intensive products like cement,automobiles,iron,steel products and the increasing final demand of the economy contributes to the growth of energy consumption in Pakistan during 2000–2012.

Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis

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.

基于IO-SDA模型的广东省外贸隐含碳的分解分析

本文运用1997—2011年投入产出的数据,通过结构分解分析方法,将广东省对外贸易隐含碳排放的影响分解为规模效应、结构效应和技术效应。研究结果表明:①1997—2011年,规模效应对出口隐含碳增长的贡献高达4.27亿吨;进口碳减排量增加了3.23亿吨。技术效应对出口隐含碳的贡献值为-3.42亿吨;进口碳减排量减少了2.87亿吨。结构效应对进出口隐含碳的作用较弱,总体来说都是有利于其碳排放量的减少。②机械设备制造业是出口隐含碳增加最多的部门,纺织服装业是出口隐含碳减少最多的部门,进口碳减排量增长最多的部门是机械设备制造业,进口碳减排量减少最多的部门是化学工业。

二氧化碳排放的IO-SDA模型及其实证研究

本文在现有的中国投入产出表的基础上编制了其可比价格的"碳排"投入产出表,运用IO-SDA模型(投入产出结构分解模型),对我国各部门二氧化碳排放总量进行了分解研究。首先设计二氧化碳排放的IO-SDA模型,提出一个基本分析框架;然后分解分析二氧化碳排放总量,研究影响这一指标的主要因素;对各部门的二氧化碳完全排放强度进行分解分析,利用已经得到的各年的二氧化碳排放系数矩阵、能耗结构矩阵、直接能耗强度对角矩阵和列昂惕夫逆矩阵,采用结构分解方法研究各部门完全二氧化碳排放强度;最后根据研究结论提出调整能源供给结构、调整产业结构、促进生产技术创新、实现最终产品低碳化等政策建议。

北京市物流业融合发展的低碳效应——基于全产业链的SDA分解技术

运用投入产出分解技术推导物流业贡献值完耗表及影子技术经济矩阵,测算全产业链下物流业融合系数,比较分析1995～2010年北京市物流业融合发展状况及结构减排潜力。研究结果表明：北京市物流业产业融合度普遍高于全国平均水平,若物流业与物流业自身、制造业等行业融合度上升1%,可促使北京市碳排放强度下降0.08和0.06个百分点。为科学评价我国物流业发展水平及产业地位提供了新思路,为促进北京市产业结构的低碳升级提供一定的支持。

广东省能源消费碳排放影响机理分析——基于IO-SDA模型

通过构建"能源-经济-碳排放"混合型投入产出分析框架,利用扩展的结构分解模型,对广东省2002-2010年能源消费碳排放的影响因素进行结构分解分析。结果显示:1)广东省能源消费碳排放从2002年的5 820.55万t增长到2010年的12 097.91万t。2)碳排放影响因素的直接效应分析,经济规模和人口增长是广东省碳排放增长的主要驱动因素,同时生产结构在当前仍然是碳排放增长的正向驱动因素,但是生产结构对于广东省碳排放增长的贡献率逐步降低。碳排放强度是遏制广东省碳排放增长的最主要贡献因子,最终需求结构对于广东省碳排放总量变化由正效应转变为负效应,逐渐成为遏制碳排放增长的主要贡献因子。3)碳排放影响因素的间接效应分析,国际出口、进口贸易和省域间调进、调出贸易对于广东省能源消费碳排放的变化影响显著。同时,固定资本形成和城镇居民消费对于广东省碳排放的增长有较强影响。4)不同最终需求对产业部门碳排放增长的分析表明,出口贸易引起的碳排放增长主要集中在电子与机械类行业和纺织服装业;省域间调出引起的碳排放增长主要集中在典型的能源密集型行业;固定资本形成引起的碳排放增长主要集中在建筑业;城镇居民消费引起的碳排放增长主要集中在交通运输业。

SDA模型的加权平均分解法及在中国第三产业经济发展分析中的应用

近年来结构分解分析(SDA)已经发展成为投入产出技术领域的一种主流经济分析工具。指出SDA模型分解方法理论上存在的三个基本问题,提出加权平均分解法,并研究了这种方法的相关性质,证明了在文献中广泛应用的两种分解方法——两极分解法和中点权分解法是这种分解方法的近似解。作为实证,应用加权平均分解法,对影响中国经济结构中第三产业变动的因素做出了定量分析,发现第三产业经济增长主要依赖国内消费的拉动,其次是投资和出口,主要阻碍来自于直接消耗系数和进口替代。

基于SDA法的能源消费影响关键要素分析

基于投入产出结构因数分解法,本文分行业研究了影响中国能源消费的关键要素,将影响能源消费变动的因素分解为能源强度变化、技术系数变化、国内最终消费比例变化、资本形成总额比例变化、出口比例和进口比例变化以及最终需求总量变化等6个指标。研究结果显示,技术系数变化是影响中国能源需求量变动的最主要因素;能源强度变化、出口比例和进口比例变化也是影响能源消费量变动的主要因素。在影响中国能源消费变化的各行业中,电力、蒸气及水的生产和供应业,金属产品制造业等6个行业对中国能源消费具有重要影响。

基于SDA模型的生态工业园资源环境压力与结构分解研究--以大连经济技术开发区生态工业园区为例

本文建立结构分解分析(SDA)模型,以大连经济技术开发区生态工业园区为例,定量分析了2001-2010年资源与环境压力的驱动因子和效应。结果表明,土地因子对生态工业园区资源环境压力的效应为正,相对较小且稳定,而效应随园区经济发展逐渐减弱则说明土地开发不再是造成资源环境压力的主要因素;经济因子的效应为正且逐年增大,是园区资源环境压力的主要动因,表明典型园区在经济高速增长阶段对资源和环境造成了破坏;技术因子的效应为负,相对较大且稳定,但随着园区经济因子正效应的逐渐增大,技术因子负效应开始抵消不了经济因子的正效应,表明园区在后期的发展会遇到节能减排技术创新的瓶颈,技术因子减小园区的资源环境压力的效应呈边际递减。