超高性能混凝土国内外研究与应用进展*

介绍了超高性能混凝土（UHPC）的制备原理和性能特点，对UHPC国内外研究和应用情况进行了综述，指出了我国UHPC研究和应用中存在的问题。结果表明：UHPC是一种具有优异的力学性能、耐久性能和环保效益的新型水泥基复合材料。国外在UHPC理论研究和应用研究方面都取得了大量成果，在实际工程中已经获得了广泛的应用；近年来我国在理论研究和应用方面也得到了快速发展；如何简化UHPC制备工艺、降低生产成本、补偿自收缩是今后的主要研究方向，完善相关规范标准以更好地指导UHPC现浇工程应用是目前首要解决的问题。随着环保和可持续发展理念的日益重视，UHPC这种低碳环保材料将有广阔的发展前景。     

Levelized cost of energy definition – An economic paradox

Using mean value theorem for integrals we show that the well-known LCOE definition is incorrect.     

矿浆多参数在线检测系统设计

针对现有矿浆浓度检测系统存在的测量数据单一、取样过程易沉降、难以检验所取样品均匀性等缺点,设计了一种矿浆多参数在线检测系统。基于称重法测量原理实现对矿浆浓度、体积流量、质量流量等多参数的在线检测。使用基于旋流消能法的取样器实现对原矿浆均匀化及防沉降处理。基于水力学孔流原理实现对取样矿浆的均值化处理及均匀性检验。在云南某选矿厂的长期应用结果表明:检测系统所测数据与浓度壶所测数据差值在2%以内,满足工业精度要求。由取样器取出样品的不均匀度在1%以内,取样过程未出现沉降、堵塞等现象,取样效果良好。     

Optimal completed work dependent loading of components in cold standby systems

This paper considers the modelling and optimization of 1-out-of-N: G cold standby (CS) systems with non-repairable components functioning at different levels of productivity or load. The productivity heterogeneity leads to difference in component failure behaviour as well as in operational and replacement costs. Thus, the choice of load or productivity of components can greatly affect the system reliability and mission cost. To make the optimal choice of component loading, we first suggest a method for analysing the reliability and expected mission cost of 1-out-of-N: G non-repairable CS systems with heterogeneous components. The optimal dynamic load distribution problem is then formulated and solved, in which the component loading is chosen depending on the amount of work completed prior to the component activation. The optimal loading is aimed at minimizing the expected mission cost, while meeting a certain system reliability constraint. Examples are given to demonstrate the proposed methodology and the improvement in the optimal design solution through introducing the component productivity’s dependence on the completed work.     

Profit maximization of TSP through a hybrid algorithm

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company’s product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one’s result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented.     

Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids

This paper addresses a new distributed cost optimization (DCO) method for load shedding (LS) of an islanded microgrid considering cost. A two-layer improved average consensus algorithm (IACA) of multi-agent system (MAS) is proposed, and the consensus characteristic of which is analyzed in detail. With the global information discovered in the first layer of the IACA, the DCO of LS can be solved by using the synchronization processing of the IACA in the second layer. PSCAD/EMTDC-based simulation models are built to study the value settings of consensus constants and the performances of the proposed DCO method. Simulation results verified the convergence improvement of the IACA and the effectiveness of the proposed DCO.     

Marginal cost and congestion in the Italian electricity market: An indirect estimation approach

In this paper we construct an indirect measure of the supply marginal cost function for the main generators from the observed bid data in the Italian electricity market in the period 2004–2007. We compute the residual demand function for each generator, taking explicitly into account the issue of transmission line congestion. This procedure allows recovering correct zonal Lerner index and the implied measure of the marginal cost function. We find evidence of a stable U-shaped marginal cost function for three main Italian generators, but a flat function for ENEL, the former national monopolist. The policy relevance of our approach lies in the possibility to offer some empirical knowledge of the marginal cost function of each generator to the regulator to design appropriate policy measures geared to the promotion of competitive market conditions. We propose a new market surveillance mechanism, which is based on the principle of sanctioning excessive deviations from the estimated measure of the marginal cost function presented in this work.     

Potential for rice straw ethanol production in the Mekong Delta,Vietnam

This study is to evaluate the potential for development of a cellulosic ethanol facility in Vietnam. Rice straw is abundant in Vietnam and highly concentrated in the Mekong Delta, where about 26 Mt year⁻¹ of rice straw has been yearly produced. To minimize the overall production cost (PC) of ethanol from rice straw, it is crucial to choose the optimal facility size. The delivered cost of rice straw varied from 20.5 to 65.4 $ dry t⁻¹ depending on transportation distance. The Mekong Delta has much lower rice straw prices compared with other regions in Vietnam because of high density and quantity of rice straw supply. Thus, this region has been considered as the most suitable location for deploying ethanol production in Vietnam. The optimal plant size of ethanol production in the region was estimated up to 200 ML year⁻¹. The improvement in solid concentration of material in the hydrothermal pre-treatment step and using residues for power generation could substantially reduce the PC in Vietnam, where energy costs account for the second largest contribution to the PC, following only enzyme costs. The potential for building larger ethanol plants with low rice straw costs can reduce ethanol production costs in Vietnam. The current estimated production cost for an optimal plant size of 200 ML year⁻¹ was 1.19 $ L⁻¹. For the future scenario, considering improvements in pre-treatment, enzyme hydrolysis steps, specific enzyme activity, and applying residues for energy generation, the ethanol production cost could reduce to 0.45 $ L⁻¹ for a plant size of 200 ML year⁻¹ in Vietnam. These data indicated that the cost-competitiveness of ethanol production could be realized in Vietnam with future improvements in production technologies.     

A derivative UKF for tightly coupled INS/GPS integrated navigation

The tightly coupled INS/GPS integration introduces nonlinearity to the measurement equation of the Kalman filter due to the use of raw GPS pseudorange measurements. The extended Kalman filter (EKF) is a typical method to address the nonlinearity by linearizing the pseudorange measurements. However, the linearization may cause large modeling error or even degraded navigation solution. To solve this problem, this paper constructs a nonlinear measurement equation by including the second-order term in the Taylor series of the pseudorange measurements. Nevertheless, when using the unscented Kalman filter (UKF) to the INS/GPS integration for navigation estimation, it causes a great amount of redundant computation in the prediction process due to the linear feature of system state equation, especially for the case with system state vector in much higher dimension than measurement vector. To overcome this drawback in computational burden, this paper further develops a derivative UKF based on the constructed nonlinear measurement equation. The derivative UKF adopts the concise form of the original Kalman filter (KF) to the prediction process and employs the unscented transformation technique to the update process. Theoretical analysis and simulation results demonstrate that the derivative UKF can achieve higher accuracy with a much smaller computational cost in comparison with the traditional UKF.     

Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products

This paper analyzes the model of the recycled products which are considered with the minimum quality level in manufacturing/remanufacturing system. In this model, a constant demand is satisfied by manufacturing raw materials and remanufacturing recycled products which are up to the quality level. It is assumed that functions of recycling rate, buyback cost and remanufacturing cost are depend on the minimum quality level. The quality level of recycled products is set to be exponential distribution and then the model is established. The results show that when the buyback cost is low (the quality of the recycled products is low), the average total cost is low, though the remanufacturing cost is high. Namely, the companies are willing to recycle the used products with low quality level. Meanwhile, the optimal strategy of recycling, manufacturing and remanufacturing is investigated here. Through construction of a solution process on Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and numerical examples with sensitive analysis, the validity of the model has been proved.