以R142b为工质的有机朗肯循环低温发电系统研究

研究了以R142b为循环介质、采用螺杆式膨胀机的低温热能有机朗肯循环发电系统.在热源温度低于80℃的条件下,膨胀机最大能量转化效率为6％,系统总效率5.16％.在系统膨胀效率达97％的情况下,传输能耗导致机械效率仅有48％-65％,因此系统总效率较低,但最大(火用)效率为32％.提高蒸发压力、降低冷凝压力是提升能量转化效率的根本途径.实验研究表明,降低膨胀机入口蒸汽干度对膨胀效率略有促进,主要由于少量液体参与膨胀减少了膨胀环节的渗漏,提高了膨胀效率.实验表明该低温热能发电系统可行,但系统效率较低,有待进一步优化提高.     

低温余热发电技术在煤化工项目上的应用

研究了采用有机朗肯循环(ORC)的低温余热发电技术。为了有效利用煤化工项目的低温凝液余热资源,在计算工艺凝液余热容量的基础上确定了合理的ORC余热利用方案,利用ORC余热发电机组代替原有的循环水冷却器,以便在完成对工艺凝液冷却的同时实现余热资源发电收益。分析结果表明:该余热发电项目的净发电功率为516k W,节约原有冷却塔循环水泵消耗的功率为110k W,年收益电量达500.8万度,年节约标准煤1812.4吨。     

过冷度对地热发电非共沸工质有机朗肯循环热力性能的影响

有机朗肯循环（organic Rankine cycle,ORC）是利用中低温地热能（< 150℃）发电的主要途径,在实际运行中,非共沸工质往往会冷凝至过冷状态。分析了冷凝过冷度对非共沸工质ORC热力性能的影响,建立了ORC、内回热（internal heat exchanger,IHE）ORC的热力学模型,以净输出功最大为目标函数优化了工质的蒸发压力,并开展了系统的㶲分析。结果表明：过冷度影响了工质与冷源换热流体间的温度匹配特性,受夹点温差的限制,随着过冷度的增加,工质的冷凝压力上升;过冷度亦改变了预热器和蒸发器的热量分摊,随着过冷度的增加,最佳蒸发压力亦上升。混合工质异丁烷/异戊烷的质量配比为0.4：0.6时,净输出功受过冷度的影响最大,当过冷度为2℃时,净输出功下降了4.36%。IHE回收膨胀机排汽的余热,提高了预热器入口温度,可提高过冷ORC系统净输出功0.55%。过冷度增大了冷凝器的㶲损失;采用内回热冷凝器的㶲损失降低了24.7%。     

不同太阳能热源下混合工质ORC系统性能分析

工质性质是影响有机朗肯循环(organic Rankine cycle,ORC)系统性能的重要因素之一。在不同热源温度下,对采用R601、R245fa作为组分的9种不同质量配比工质的ORC系统热力性和经济性进行计算,然后采用灰色关联法对系统性能进行分析及综合评价,并与纯工质的性能进行对比。研究表明,混合工质系统的热力学指标与温度滑移大小近似成反比。当温度滑移大于5K时,纯工质性能更优,且经济性也优于混合工质系统。综合来看,热源温度一定时,灰色关联度随着蒸发温度的升高呈现先增大后减小的趋势;混合工质R601/R245fa(0.1/0.9)的温度滑移最小,其性能也最优。采用R601和R245fa混合作为工质的系统在热源温度为160℃时,灰色关联度最大,系统性能更优。     

Two-scale evaluation of remediation technologies for a contaminated site by applying economic input-output life cycle assessment: risk-cost, risk-energy consumption and risk-CO2 emission

A two-scale evaluation concept of remediation technologies for a contaminated site was expanded by introducing life cycle costing (LCC) and economic input-output life cycle assessment (EIO-LCA). The expanded evaluation index, the rescue number for soil (RN_SOIL) with LCC and EIO-LCA, comprises two scales, such as risk-cost, risk-energy consumption or risk-CO₂ emission of a remediation. The effectiveness of RN_SOIL with LCC and EIO-LCA was examined in a typical contamination and remediation scenario in which dieldrin contaminated an agricultural field. Remediation was simulated using four technologies: disposal, high temperature thermal desorption, biopile and landfarming. Energy consumption and CO₂ emission were determined from a life cycle inventory analysis using monetary-based intensity based on an input-output table. The values of RN_SOIL based on risk-cost, risk-energy consumption and risk-CO₂ emission were calculated, and then rankings of the candidates were compiled according to RN_SOIL values. A comparison between three rankings showed the different ranking orders. The existence of differences in ranking order indicates that the scales would not have reciprocal compatibility for two-scale evaluation and that each scale should be used independently. The RN_SOIL with LCA will be helpful in selecting a technology, provided an appropriate scale is determined.     

基于能量优化模型的无线传感器网络分簇算法

考虑到无线传感器分簇网络中簇的规模、簇头数量和节点剩余能量是能量有效型分簇路由算法关注的重要指标,提出了一种基于能量优化模型(EOM)的分布式分簇算法——EOMC,该算法通过建立网络能耗优化模型,以最优簇头数构建分簇通信规模,并结合功率控制将候选簇头限制在一定宽度的选举环带,使得簇头分布均衡,同时兼顾到节点剩余能量进行分簇,以达到均衡节点能耗,延长网络生存期的目的。与低能耗自适应分簇分层(LEACH)协议的对比仿真的结果表明,该算法能够达到预期指标,算法的开销相对较小。     

A study on the application of material selection optimization approach for structural-acoustic optimization

Issues of application of the material selection optimization approach for structural-acoustic optimization is investigated herein. By introducing the stacking sequence hypothesis of metal material, the mechanical properties parameters and plies’ numbers of the metal material or composite material are defined as design variables; the mathematical formulation about material selection optimization approach is established. Finally, a hexahedral box structure is taken as an example, and the material selection optimization is conducted. By introducing genetic algorithm (GA), the optimization problem is solved. The numerical example shows the effectiveness of the proposed stacking sequence hypothesis of metal material.     

考虑风电消纳的电-热综合能源系统经济运行研究

为解决能源短缺等问题,各国为可再生能源的发展给予大力支持.但由于可再生能源具有随机性、波动性等特点,难以进行运行调控.如何增大可再生能源的消纳成为当下的研究热点.该文针对风电消纳问题进行研究,将电锅炉、电转气技术及储能设备引入综合能源系统中,并考虑电力需求响应负荷.系统总成本中考虑设备投资与运维成本、购电与购气成本,建...     

低温热源有机工质带叶片盘式透平设计及性能分析

Tesla盘式透平是一种以流体边界层摩擦力带动转子旋转的非传统透平,具有结构简单、成本低廉、工质适应性好等优点,尤其适用于低品位热源余热回收系统。但由于其特有的做功方式,盘式透平的运行转矩与效率均远低于常规透平形式,这也是制约其商业应用的主要因素。在传统盘式透平的相邻盘片间增加导流叶片,并针对增加叶片后的透平结构进行了数学建模以及计算流体力学（computational fluid dynamics,CFD）验证。CFD验证表明,所建立的数学模型能够计算流体通道内的流体速度场、压力场分布,并能够预测透平效率在不同工况下的变化趋势。针对余热回收有机朗肯循环（organic rankine cycle,ORC）系统,利用所建立的数学模型,使用不同有机工质进行计算并进行对比。结果表明,在使用不同种类工质做功时,增加叶片均可以大幅提升盘式透平转矩与效率。     

Emergency strategy optimization for the environmental control system in manned spacecraft

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.     

冷凝法甲苯回收系统设计优化

针对典型工业挥发性有机物（VOC）甲苯设计一套冷凝法回收系统。该系统制冷部分采用冰箱系统常用的分立多循环结构,将回收流程分为预冷级和冷却级。运用物性软件REFPROP及通用流程模拟软件Aspen Plus对甲苯负荷及制冷流程进行模拟。通过对系统的模拟运行,研究该冷凝法甲苯回收系统的工作性能以及能耗影响因素,为系统的进一步设计优化提供理论依据。     

A data mining approach to process optimization without an explicit quality function

In process optimization, the setting of the process variables is usually determined by estimating a function that relates the quality to the process variables and then optimizing this estimated function. However, it is difficult to build an accurate function from process data in industrial settings because the process variables are correlated, outliers are included in the data, and the form of the functional relation between the quality and process variables may be unknown. A solution derived from an inaccurate function is normally far from being optimal. To overcome this problem, we use a data mining approach. First, a partial least squares model is used to reduce the dimensionality of the process and quality variables. Then the process settings that yield the best output are identified by sequentially partitioning the reduced process variable space using a rule induction method. The proposed method finds an optimal setting from historical data without constructing an explicit quality function. The proposed method is illustrated with two examples obtained from steel making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.     

求解复杂优化问题的双层多种群粒子群优化算法

为解决粒子群优化算法存在的易早熟和精度低问题,提出了一种双层多种群粒子群优化算法.此算法采用上下两层,即下层N个基础种群和上层一个精英种群.各个基础种群相互独立进化,并从精英种群中得到优良信息指导自己的进化.上层精英种群首先通过接受各基础种群的当前最优粒子来更新自己的粒子集合,然后执行自适应变异操作,最后随机地向每一个基础种群输送出本次进化后的一个最优粒子来改进其下一轮搜索.该算法的并行双进化机制增加了群体的随机性和多样性,提高了全局搜索能力和收敛精度.实例仿真表明该算法具有较好的性能,尤其对于复杂多峰函数优化,成功率显著提高.     

A comparative study of expected improvement-assisted global optimization with different surrogates

Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.     

A demonstration of the improved efficiency of the canonical coordinates method using nonlinear combined heat and power economic dispatch problems

Economic dispatch is the short-term determination of the optimal output from a number of electricity generation facilities to meet the system load while providing power. As such, it represents one of the main optimization problems in the operation of electrical power systems. This article presents techniques to substantially improve the efficiency of the canonical coordinates method (CCM) algorithm when applied to nonlinear combined heat and power economic dispatch (CHPED) problems. The improvement is to eliminate the need to solve a system of nonlinear differential equations, which appears in the line search process in the CCM algorithm. The modified algorithm was tested and the analytical solution was verified using nonlinear CHPED optimization problems, thereby demonstrating the effectiveness of the algorithm. The CCM methods proved numerically stable and, in the case of nonlinear programs, produced solutions with unprecedented accuracy within a reasonable time.     

Switchgrass as an alternate feedstock for power generation: an integrated environmental, energy and economic life-cycle assessment

An environmental biocomplexity analysis is done on the environmental, energy, economic and technological implications of using switchgrass (Panicum virgatum) to replace coal in power generation. We evaluate cost, environmental impact and net greenhouse gas emissions. In the analysis, alternatives for production and transport are considered. The analysis shows that the most effective technologies for switchgrass preparation are harvesting loose material for hauling and chopping and then compressing it into modules and transporting. The GHG emission mitigation is found to be substantial with the mitigation contribution under cofiring found to be greater per ton of switchgrass than for switchgrass fired alone. This paper also analyzes the implications of switchgrass use under alternative cofiring ratios, coal prices, hauling distances and per acre yields.     

Port-of-entry safety via the reliability optimization of container inspection strategy through an evolutionary approach

Up to now, of all the containers received in USA ports, roughly between 2% and 5% are scrutinized to determine if they could cause some type of danger or contain suspicious goods. Recently, concerns have been raised regarding the type of attack that could happen via container cargo leading to devastating economic, psychological and sociological effects. Overall, this paper is concerned with developing an inspection strategy that minimizes the total cost of inspection while maintaining a user-specified detection rate for “suspicious” containers. In this respect, a general model for describing an inspection strategy is proposed. The strategy is regarded as an (n+1)-echelon decision tree where at each of these echelons, a decision has to be taken, regarding which sensor to be used, if at all. Second, based on the general decision-tree model, this paper presents a minimum cost container inspection strategy that conforms to a pre-specified user detection rate under the assumption that different sensors with different reliability and cost characteristics can be used. To generate an optimal inspection strategy, an evolutionary optimization approach known as probabilistic solution discovery algorithm has been used.     

Thermoeconomic evaluation and optimization of an aqua-ammonia vapour-absorption refrigeration system

In this paper, the thermoeconomic concept is applied to the optimization of an aqua-ammonia vapour-absorption refrigeration (VAR) system—aimed at minimizing its overall product cost. The thermoeconomic concept based simplified cost minimization methodology calculates the economic costs of all the internal flows and products of the system by formulating thermoeconomic cost balances. The system is then thermoeconomically evaluated to identify the effects of design variables on costs and thereby enables to suggest values of design variables that would make the overall system cost-effective. Based on these suggestions, the optimization of the system is carried out through an iterative procedure. The results show a significant improvement in the system performance without any additional investment. Finally, sensitivity analysis is carried out to study the effect of the changes in fuel cost to the system parameters.     

A framework for the selection of the right nuclear power plant

Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35–45?MWe up to 1600–1700?MWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called “external factors” like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40–60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700?MWe.