基于DEA的油田开发单元生产效率评价

油田开发单元生产效率评价是油田开发管理决策的一项重要内容。建立了油田开发单元生产效率评价的CCR模型、BCC模型、投影模型和超效率评价模型等DEA模型。选取采油井总开井数、含水上升率、注水开井数、新井投产井数、老井措施年累总井次、老井自然递减率和吨油操作成本等7个指标为输入指标,老井年产油、新井年产油、老井措施年增油和地质采油速度等4个指标为输出指标,运用DEA模型对某油田下辖的9个油田开发单元生产效率进行实证评价,得到各开发单元的技术效率、纯技术效率、规模效率、DEA投影、输入剩余和输出亏空。实例结果表明,CCR模型和BCC模型可以实现对油田开发单元在技术或规模方面的投入产出配置有效性的评价,帮助它们寻求技术或规模方面的改进建议;DEA投影模型为非DEA有效的油田开发单元实现投入产出最佳配置提供了改进方向和改进力度;DEA超效率模型则可实现不同开发单元在投入产出配置效率方面的评价排序。     

PIMS模型在炼油企业优化增效中的应用

流程工业模型系统(PIMS)操作简单、功能强大,可以模拟流程工业企业的生产经营过程,广泛用于世界各地的炼油和石化企业。炼油企业结合自身装置特点,开发企业级PIMS模型,能方便快捷地寻找到可操作性强的生产方案,在加工负荷优化、原油选择优化、产品结构优化等方面为生产经营提供决策依据,帮助企业优化增效。结合当前国内市场对成品油需求的持续低迷、炼油产能相对过剩和产品质量指标日益提升的需求,借助企业级PIMS模型,为炼厂优化加工负荷、优选原油品种、优化产品结构、改善经营局面、提升经济效益提供参考依据。中国石化R炼厂通过PIMS优化测算认为:提高炼厂原油加工负荷有利于增效;高酸重质原油性价比最优,适合装置加工,具有一定竞争力;适当增产燃料油(催化柴油)有利于提高加工量,同时增加企业经济效益。     

基于DEA的可再生能源发电技术经济效益评价

在发电行业中,由于发电厂具有较高的建设和运营成本,因此能效是衡量经济效益的关键因素。采用DEA评价方法比较风电、水电、光伏发电及生物质发电四种可再生能源发电技术的投入、产出情况,输入指标包括单位投资额、折旧率和运营及维护成本,输出指标为发电小时数。算例分析表明,DEA方法可有效评价可再生能源发电技术的经济效益并为其规模化发展提供依据。     

地方炼油企业能耗分析和节能措施实例

随着进口原油使用权的放开,地方炼油企业产能进一步释放,地炼企业装置规模小、产业链短、能耗高等问题突出。根据国家政策和企业节能减排需要,结合东营市地方炼油总体运行情况,以某炼油企业为例,充分调研该企业的能量流和物料流,以权重总和计分排序法确定节能重点表,找出用能关键装置,并同生产技术人员研究制定节能方案,促进企业的节能减排工作。运用全厂能流和物流分析的权重积分排序方法,可有效定位企业用能单元,对东营地炼企业有很好的借鉴和推广价值。     

我国炼油企业能量利用策略研究

炼油企业能量利用策略是指炼油企业在生产过程中如何合理地分配能量的策略。近年来,我国炼油加工能力快速增长,装置规模不断扩大,2008年原油加工量达3.42×108t,开工负荷率77.3%,生产汽煤柴成品油2.08×108t,千万吨级炼厂增加到11家。同时炼油技术水平不断提高,油品清洁化进程加快。但与国际先进水平相比,我国炼油企业普遍能耗较高,在能量利用方面存在观念落后、政策体制不够完善、主要生产装置能量利用率较低、装置间热联合程度较低等问题。要提高我国炼油企业能量利用效率,必须健全法规制度,加强主要装置和装置之间的能量利用策略,强化工艺过程之内、工艺与热力系统之间、工艺与储运系统之间及厂际之间的综合优化,大力推行炼化一体化和装置规模大型化,提高装置间热联合效率,应用热电联产技术,延长装置安全运行周期。     

安庆石化走油-化-煤一体化路线的探讨

运用SWOT分析法对中国石化安庆分公司的发展优势、劣势、发展机遇和面临的挑战进行了分析。结合分析结果,按照“依靠内部优势,克服内部劣势,利用外部机会,回避外部威胁”的原则制定了发展策略：将炼油规模扩大到800×10^4t/a,实现炼油基地化;准确定位未来加工原油的品质,完善重油加工手段,实现产品清洁化;淘汰规模偏小装置,逐步实现单套装置大型化,降低运行成本;提高重整装置处理能力,增产石化原料,适时发展加氢裂化;坚持走油-化-煤一体化发展路线,获取最大经济效益。     

炼化企业氢气系统优化

随着氢气消耗量的日益增大,氢气成本已经成为炼油企业原料成本中仅次于原油成本的第二位成本要素。炼化企业的氢气系统目前基本是基于经验进行管理,不平衡现象时有发生,管理水平有待提高。针对炼化企业氢气系统存在的问题,建立相应的优化模型,可有效提高氢气利用率,降低氢气系统运行成本。氢气系统优化模型综合考虑了氢气系统的氢阱约束、氢源约束、压缩机约束、流股约束等约束条件,采用非线性数学规划法(MINLP)建模,优化计算得到氢气系统优化方案。优化效果表明,该模型可有效降低氢气系统的运行成本,通过汽柴油加氢装置氢油比优化,每年可获得经济效益23万元;通过制氢装置原料气优化,每年可获得经济效益260万元;通过制氢装置中变反应优化,每年可获得经济效益346万元;通过新建PSA装置回收氢气,每年可获得经济效益915万元。几项合计,每年可实现总效益达1544万元。     

基于数据包络分析法的发电商运营效率测算及影响因素

基于数据包络分析法效率评价模型,在考虑传统技术、规模因素的基础上,将影响发电商运营效率的发电可靠性和环境约束等因素纳入分析,建立了输入/输出变量体系,对发电商运营效率实施评估。算例分析表明,数据包络分析法能有效地评估发电商运营效率,并为发电企业的未来发展提供相应的决策支持。     

利用流程模拟软件优选进口原油品种

随着国内石油资源的日益枯竭,为满足目前生产需要,主流大型炼化企业引进国外进口高硫、高酸类原油。卡斯蒂利亚劣质原油因具有明显的成本优势,被炼厂作为降低企业生产成本、提高经济效益的掺炼油种。掺炼劣质原油对生产设备及二次加工有着一定的影响,以某炼厂目前加工的典型进口原油为研究对象,借助Aspen Hysys流程模拟软件对进口原油混炼工况进行计算,得出多原油混炼状态下组分性质,通过一次加工模型对混炼原油进行切割分析,得出轻质油收率为46.2%,总拔出率为74.8%。针对卡斯蒂利亚原油不同掺炼比例进行计算,分析减压渣油残炭沥青比,研究表明,卡斯蒂利亚原油掺炼量与产生弹丸焦的风险呈正相关。通过Aspen Hysys对减压渣油分析,当卡斯蒂利亚原油掺炼比达15%时,延迟焦化装置容易产生弹丸焦。Aspen Hysys不仅可以对混炼原油进行动态分析,还可以对单装置模型及联合装置互供进行优化计算,真正实现智能化工厂。     

采用节能技术,精心设计中国节能型炼油企业

A、B是两个新建的炼油型企业,以技术先进、投资合理、资源节约、能耗节省和经济效益好作为建设原则,采取优化加工方案与流程、优化主要公用工程方案、采用先进节能技术和节能设备等措施进行炼厂项目设计,不仅使生产装置的设计能耗达到先进水平,而且全厂的设计能耗也有了明显降低,其中A炼厂的能量因数实际生产统计数据为7.18kg标油/（t原油·因数）,达到世界先进能耗指标。优化总加工方案,调整装置构成是炼油企业节能降耗的关键所在,优化蒸汽和电力平衡是节能降耗的有效措施,而全厂热联合优化则是炼油系统有效的节能方向。     

Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis

In this study, efficiency analyses of the eleven lignite-fired, one hard coal-fired and three natural gas-fired state-owned thermal power plants used for electricity generation were conducted through data envelopment analysis (DEA). Two efficiency indexes, operational and environmental performance, were defined and pursued. In the calculation of the operational performance, main production indicators were used as input, and fuel cost per actual production (Y) was used as output (Model 1). On the other hand, in the calculation of the environmental performance, gases emitted to the environment were used as output (Model 2). Data envelopment analysis (DEA) is the main instrument for the measurement of relative performances of the decision making units with multiple inputs and outputs. Constant returns to scale (CRS or CCR) and variable returns to scale (VRS or BCC) type DEA models were used in the analyses. The relationship between efficiency scores and input/output factors was investigated. Employing the obtained results, the power plants were evaluated with respect to both the cost of electricity generation and the environmental effects.     

Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement

Nowadays, majority of organizations are seeking to achieve sustainable development with respect to “green” concept. One of the main criteria for assessing green performance is eco-efficiency. To identify all aspects of the eco-efficiency, inputs should be divided into energy and non-energy and outputs should be divided into good and bad outputs. To deal with this issue, a data envelopment analysis (DEA) model is developed to divide inputs into both energy and non-energy and outputs into both desirable (good) and undesirable (bad) outputs. Likewise, variables are separated into both discretionary and non-discretionary factors. Accordingly, a bounded adjusted measure (BAM) based on green indicators is developed to calculate the eco-efficiency of decision making units (DMUs). Besides, energy saving potentials and undesirable output abatement potentials are calculated to show correlation coefficient between energy consumption and undesirable output. Finally, proposed model is validated by assessing the eco-efficiency of some selected members of organization for economic cooperation and development (OECD). Australia, Finland, Ireland, New Zealand, and Switzerland are recognized as eco-efficient countries and the rest of countries are inefficient in terms of the eco-efficiency. High and positive Spearman correlation coefficient between energy consumption and undesirable outputs addresses that the more use of energy inputs, the more undesirable outputs.     

A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of homogenous decision making units (DMUs) with multiple inputs and outputs. In this paper, we present a dynamic multi-stage DEA (DMS-DEA) approach to evaluate the efficiency of cotton production energy consumption. In the proposed model, the farms which consume resources (i.e., fertilizers, seeds, and pesticides) to produce cotton are assumed to be the DMUs. Inputs not consumed during a planning period are carried over to the next period in the planning horizon. Initially, a DMS-DEA model is used to determine the overall efficiency of the DMUs with dynamic inputs. Next, the efficiency score of each DMU is calculated for each time period in the planning horizon. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms with a real-life case study of energy consumption in the cotton industry.     

Linear programming models for measuring economy-wide energy efficiency performance

Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as by-products of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring economy-wide energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 21 OECD countries and the results obtained are presented.     

An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors.     

Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy

Data envelopment analysis (DEA) has been widely used in energy efficiency and environment efficiency analysis in recent years. Based on the existing environment DEA technology, this paper presents several DEA models for estimating the aggregated efficiency of resource and environment. These models can evaluate DMUs’ energy efficiencies and environment efficiencies simultaneously. However, efficiency ranking results obtained from these models are not the same, and each model can provide some valuable information of DMUs’ efficiencies, which we could not ignore. Under this situation, it may be hard for us to choose a specific model in practice. To address this kind of performance evaluation problem, the current paper extends Shannon-DEA procedure to establish a comprehensive efficiency measure for appraising DMUs’ resource and environment efficiencies. In the proposed approach, the measure for evaluating a model's importance degree is provided, and the targets setting approach of inputs/outputs for DMU managers to improve DMUs’ energy and environmental efficiencies is also discussed. We illustrate the proposed approach using real data set of 30 provinces in China.     

DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation

This study discusses a new DEA (Data Envelopment Analysis) approach to measure the unified (operational and environmental) efficiency of energy firms. It is widely known that they produce not only desirable (good) outputs (e.g., electricity) but also undesirable (bad) outputs (e.g., CO₂) as a result of their plant operations. The proposed approach incorporates an output separation (desirable and undesirable outputs) for the performance evaluation of energy firms. In addition to the output separation, this study separates inputs into energy and non-energy inputs. Consequently, the proposed approach incorporates not only the output separation but also the input separation within a computational framework of DEA non-radial measurement. This study compares the proposed approach with other previous DEA approaches used for the performance evaluation of energy firms. After the methodological comparison, this study applies the proposed approach for measuring the unified efficiency of Japanese fossil fuel power generation. This empirical study confirms that the implementation of Kyoto Protocol (2005) has not been effective on the unified efficiency of Japanese fossil fuel power generation during the observed period (2004-2008). Although the empirical result is inconsistent with the current Japanese environmental policy under Kyoto Protocol, it contains policy implications for guiding the future direction of Japanese environmental policy on the electric power industry.     

Measuring the relative efficiency of hydrogen energy technologies for implementing the hydrogen economy: An integrated fuzzy AHP/DEA approach

To provide and improve national energy security and low-carbon green energy economy, as a government-supported research institute related to developing new and renewable energy technologies, including energy efficiency, Korea Institute of Energy Research (KIER) needs to establish a long-term strategic energy technology roadmap (ETRM) in the hydrogen economy sector for sustainable economic development. In this paper, we establish a strategic ETRM for hydrogen energy technologies in the hydrogen economy considering five criteria: economic impact (EI), commercial potential (CP), inner capacity (IC), technical spin-off (TS), and development cost (DC). As an extended research, we apply the integrated two-stage multi-criteria decision-making approach, including the hybrid fuzzy analytic hierarchy process (AHP) and data envelopment analysis (DEA) model, to assess the relative efficiency of hydrogen energy technologies in order to scientifically implement the hydrogen economy. Fuzzy AHP reflects the vagueness of human thought with interval values, and allocates the relative importance and weights of four criteria: EI, CP, IC, and TS. The DEA approach measures the relative efficiency of hydrogen energy technologies for the hydrogen economy with a ratio of outputs over inputs.The result of measuring the relative efficiency of hydrogen energy technologies focuses on 4 hydrogen technologies out of 13 hydrogen energy technologies. KIER has to focus on developing 4 strategic hydrogen energy technologies from economic view point in the first phase with limited resources. In addition, if energy policy makers consider as some candidates for strategic hydrogen technologies of the other 9 hydrogen energy technology, the performance and productivity of 9 hydrogen energy technologies should be increased and the input values of them have to be decreased.With a scientific decision-making approach, we can assess the relative efficiency of hydrogen energy technologies efficiently and allocate limited research and development (R&D) resources effectively for well-focused R&D.     

Performance analysis of US coal-fired power plants by measuring three DEA efficiencies

Data Envelopment Analysis (DEA) has been widely used for performance evaluation of many organizations in private and public sectors. This study proposes a new DEA approach to evaluate the operational, environmental and both-unified performance of coal-fired power plants that are currently operating under the US Clean Air Act (CAA). The economic activities of power plants examined by this study are characterized by four inputs, a desirable (good) output and three undesirable (bad) outputs. This study uses Range-Adjusted Measure (RAM) because it can easily incorporate both desirable and undesirable outputs in the unified analytical structure. The output unification proposed in this study has been never investigated in the previous DEA studies even though such a unified measure is essential in guiding policy makers and corporate leaders. Using the proposed DEA approach, this study finds three important policy implications. First, the CAA has been increasingly effective on their environmental protection. The increased environmental performance leads to the enhancement of the unified efficiency. Second, the market liberalization/deregulation was an important business trend in the electric power industry. Such a business trend was legally prepared by US Energy Policy Act (EPAct). According to the level of the market liberalization, the United States is classified into regulated and deregulated states. This study finds that the operational and unified performance of coal-fired power plants in the regulated states outperforms those of the deregulated states because the investment on coal-fired power plants in the regulated states can be utilized as a financial tool under the rate-of-return criterion of regulation. The power plants in the deregulated states do not have such a regulation premium. Finally, plant managers need to balance between their environmental performance and operational efficiency.     

Biohydrogen yield efficiency and the benefits of dark,photo and dark-photo fermentative production technology in circular Asian economies

Twenty-six new data envelopment analysis (DEA) models with 55 biohydrogen production experiments categorized into three groups including dark fermentation (DF), photo fermentation (PF), and dark-photo sequential fermentation (DF-PF) technologies, are used to evaluate their biohydrogen yield efficiency. The results reveal the average yield efficiencies of DF, PF and DF-PF are 0.2844, 0.3460 and 0.7040, respectively. The most efficient overall combination of biohydrogen inputs is PhBR1/Rhodobacter capsulatus B10/Rhodobacter capsulatus in DF-PF. Statistical tests demonstrate DF-PF has statistically double the efficiency of PF and DF, and the efficiency of PF significantly exceeds that of DF, supporting some of the literature findings. A flexible DEA model must be carefully chosen when evaluating biohydrogen production. All inputs and outputs of biohydrogen statistically influenced yield efficiency to a significant level. India and Japan are the top two economies benefitting from improved biohydrogen yield efficiency. Improving biohydrogen yield efficiency can improve macroeconomic growth and develop the renewable hydrogen and biohydrogen industry.