Tilt Angle Optimality Criteria for Stand Alone PV Systems

The conventional approach to optimizing tilt angles for fixed solar panels aims to maximize energy generation over the entire year. However, in the context of a supply controlled electric grid, where solar energy availability varies, this criterion may not be optimal. This study explores two alternative optimization criteria focused on maximizing baseload supply potential and minimizing required storage capacity to address seasonality in energy generation. The optimal tilt angles determined for these criteria differed significantly from the standard approach. This research highlights additional factors crucial for designing solar power systems beyond gross energy generation, essential for the global transition towards a fully renewable energy-based electric grid in the future.

Optimal Matching Control of a Low Energy Charged Particle Beam in Particle Accelerators

Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics.In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forward direction. Thus, this reference direction can be considered as an auxiliary "time" beam axis. In this paper, the optimal beam matching control problem for a low energy transport system in a charged particle accelerator is considered. The beam matching procedure is formulated as a finite "time" dynamic optimization problem, in which the Kapchinsky-Vladimirsky(K-V) coupled envelope equations model beam dynamics. The aim is to drive any arbitrary initial beam state to a prescribed target state, as well as to track reference trajectory as closely as possible, through the control of the lens focusing strengths in the beam matching channel. We first apply the control parameterization method to optimize lens focusing strengths, and then combine this with the time-scaling transformation technique to further optimize the drift and lens length in the beam matching channel. The exact gradients of the cost function with respect to the decision parameters are computed explicitly through the state sensitivity-based analysis method. Finally, numerical simulations are illustrated to verify the effectiveness of the proposed approach.

A Uniaxial Optimal Perfectly Matched Layer Method for Time-harmonic Scattering Problems

We develop a uniaxial optimal perfectly matched layer （opt PML） method for solving the time-harmonic scattering problems by choosing a particular absorbing function with unbounded integral in a rectangular domain. With this choice, the solution of the optimal PML problem not only converges exponentially to the solution of the original scatting problem, but also is insensitive to the thickness of the PML layer for sufficiently small parameter ε0. Numerical experiments are included to illustrate the competitive behavior of the proposed optimal method.

Optimal Matching of Magnetic Pulse Compressor

Energy transmission efficiency in the magnetic pulse generators varies with saturated time of magnetic switch. An optimal matching time exists and depends on the compression ratio, under which, the energy transmission efficiency can reach approximate 100%. The equation of required magnetic core volume is obtained by taken into account the optimal matching mode. It indicates that a great reduction on the volume is feasible under the optimal matching mode. The circuit simulation code-PSPICE is also introduced to simulate a 3-stage magnetic pulse compressor, and the results are in accordance with those of equivalent circuit analyses.

Optimal Matching Analysis of Net Buoyancy and Pitching Angle for Underwater Gliders

Control parameter optimization is an efficient way to improve the endurance of underwater gliders(UGs),which influences their gliding efficiency and energy consumption.This paper analyzes the optimal matching between the net buoyancy and the pitching angle and proposes a segmented control strategy of Petrel-L.The optimization of this strategy is established based on the gliding range model of UG,which is solved based on the approximate model,and the variations of the optimal control parameters with the hotel load are obtained.The optimization results indicate that the segmented control strategy can significantly increase the gliding range when the optimal matching between the net buoyancy and the pitching angle is reached,and the increase rate is influenced by the hotel load.The gliding range of the underwater glider can be increased by 10.47%at a hotel load of 0.5 W.The optimal matching analysis adopted in this study can be applied to other UGs to realize endurance improvement.

Automatic Web services composition algorithm based on optimal matching

A novel layered method was proposed to solve the problem of Web services composition.In this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal matching problem was modeled based on the hypergraph theory,and solved by computing the minimal transversals of the hypergraph.Meanwhile,two optimization algorithms were designed to discard some useless states at the intermediary steps of the composition algorithm.The effectiveness of the composition method was tested by a set of experiments,in addition,an example regarding the travel services composition was also given.The experimental results show that this method not only can automatically generate composition tree whose leaf nodes correspond to services composition solutions,but also has better performance on execution time and solution quality by adopting two proposed optimization algorithms.

Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation

Vegetation cover derived from remote sensing image is widely used for soil erosion risk assessment, but there is no clear guideline to select the most appropriate temporal satellite data. It is common practice that satellite data during growing season are randomly selected and used in soil erosion risk assessment. However, the effectiveness of vegetation in protecting the soil is quite different even if it is the same growing season since vegetation covers change as they grow. This article aims to provide a method of choosing optimal vegetation cover for studying soil erosion risk using remote sensing, that is, the vegetation cover in the most appropriate temporal period. Based on the temporal relationship of the two most active impact factors, rainfall and vegetation, an index of RV is developed and used to indicate the relative erosion risk during the year. The results show that annual variation of rainfall is significant, and vegetation is relatively stable, resulting in their matching relationship is different in each year. The correlation coefficient reaches 0.89 between RV and real sediment transport during the period when rainfall can cause soil erosion. In other words, RV is a good indicator of soil erosion. Therefore, there is a good correlation between RV maximum and the optimal vegetation cover, which can help facilitate erosion research in the future, showing good potential for successful application in other places.

The Optimal Matching Parameter of Half Discrete Hilbert Type Multiple Integral Inequalities with Non-Homogeneous Kernels and Applications

By using the weight function method,the matching parameters of the half discrete Hilbert type multiple integral inequality with a non-homogeneous kernel K(n,||x||ρ,m)=G(nλ1||x||ρmλ,2)are discussed,some equivalent conditions of the optimal matching parameter are established,and the expression of the optimal constant factor is obtained.Finally,their applications in operator theory are considered.

Optimal Price Strategy under Price-Matching Policy

The paper explores the optimal price strategy under the price-matching policy. First, the paper formulates the demand function under the price match policy and then discovers the retailer’s best response facing the price-matching pressure. From the theoretical analysis, we discover how the number of retailers plays an important role during the competition. When only two retailers are involved, the final prices may not converge to a single value. However, when more retailers are involved, the final price will converge to a single value. We also use numerical studies to illuminate the change of the prices over the time period, the sensitivity of the final price to the increment/decrement of initial prices. Finally, we provide managerial suggestions to both producers and retailers.

Harmony search optimization applied to reservoir engineering assisted history matching

Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.

Research on optimizing ore matching technology for 550 m^2 sintering machine

The sinter quality is important for its performance in a blast furnace,and optimizing ore matching is a main way to ensure the quality and yield of sinter ore and to reduce the cost of ore matching.The research on optimizing ore matching for a 550 m2sintering machine in Shougang Jingtang was carried out in this paper. Firstly,based on the condition of iron ore resourse in Shougang,sintering properties of various ores,especially the high temperature properties were researched,and basic structure of ore matching was determined according to the mutual complementary properties of assimilation and liquid phase fluidity among Australia ore,Brazilian ore and domestic concentrates,that was Australian ore(50%-60%) + Brazilian ore(40%-30%) + domestic concentrates(about 10%).Secondly,9 groups of ore matching schemes were designed and sintering pot tests were carried out,and then the starting scheme of the 550 m2sintering machine was obtained:ore from southern Brazil (20%) +ore from northern Brazil(10%) + semi-limonite(20%) + limonite ore(35%) + domestic concentrates (15%).Thirdly,experiment of optimizing parameters of optimizing basicity,lime ratio,water addition and bed depth were carried out,and 9 groups of ore matching schemes were designed.The results showed that parameters fit for Jingtang currently are as follows:binary basicity is 1.9 - 1.95,lime ratio and water addition is 5%and 7.0% respectively;sintering bed should increase to 800 mm gradually.At last,Jingtang sintering plant was put into production successfully and yields stably,with the bed depth of 800 mm and advanced sintering indexes.

Spectral Match Optimization Based on Grey Relational Analysis

A spectral match optimization based on grey incidence matrix was put forward to evaluate camouflage painting design.A synthetic degree of incidence (SDI) was defined to comprehensively reflect the spectrum similarity based on theory of grey incidence analysis.A grey optimization model for camouflage painting sheme was constructed on the basis of SDI and grey incidence matrix.Its weight values were determined according to area percentages of all components in the camouflage scene,and a quantitative ordering for various schemes could be obtained according to the evaluation coefficients.Experiment results show that the method mentioned in this paper can provide a quantitative basis for the camouflage decision-making,and it can also be used in other camouflage scheme selection.

Farmer's Perception on Supply-Demand Matching of New Variety and Its Influence Factors

Using disordered multinomial logistic regression and multiple linear regression method,385 copies of questionnaires on farmer are analyzed to explore the relationship between peasant's psychological traits,peasant's cognition on seed technology and perception on supplydemand matching of new variety.Research results show that the vast majority of farmers think that current new variety is at high-level supplydemand balance and the oversupply status,and updating speed of new variety on the market is faster;the farmers preferring risk,seeking innovation and having strong learning and cognition ability may select high-level supply-demand matching state,and the farmers understanding the importance and difference of seed technology tend to choose high-level supply-demand matching situation;the farmers with strong learning and cognition ability can acknowledge the importance and difference of seed technology,while the farmers preferring risk can perceive the difference of seed technology;psychology seeking the innovation and learning and cognition ability affect the farmer's perception on supplydemand matching status of new variety via affecting the farmer's cognition on technical difference.

Optimization Model for School Transportation Based on Supply-Demand Analyses

This paper presents a new conception model of school transportation supply-demand ratio (STSDR) in order to define the number of school buses needed in a limited area and to describe the conditions of school transport system. For this purpose, a mathematical equation was elaborated to simulate the real system based on the school transport conditions and on the estimated results of STSDR from 15 zones of Cuenca city in Ecuador. The data used in our model was collected from several diverse sources (i.e. administrative data and survey data). The estimated results have shown that our equation has described efficiently the school transport system by reaching an accuracy of 96%. Therefore, our model is suitable for statistical estimation given adequate data and will be useful in school transport planning policy. Given that, it is a support model for making decisions which seek efficiency in supply and demand balance.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

宁夏回族自治区碳捕集、利用与封存源汇匹配与集群部署

“双碳”(碳达峰、碳中和)战略背景下,碳捕集、利用与封存技术(CCUS)是实现化石能源大规模低碳化利用的关键技术之一。近年,CCUS技术呈现出规模化和集群化发展趋势,而科学、合理的源汇匹配是CCUS集群部署工程选址的重要依据,能够建立高效CO_(2)输运管网、降低减排成本。宁夏是国家能源安全战略布局的重要保障基地,能源结构偏煤、工业结构偏重特征明显,面临着巨大的碳减排压力。针对宁夏CCUS集群部署的源汇匹配问题,调研评估了宁夏工业碳排放源特征和地质碳汇潜力,构建了CCUS源汇匹配模型,在充分考虑源汇性质、捕集-输运-封存成本、CO_(2)运输距离、区域地理条件、土地利用类型、人口密度等因素基础上,应用改进的节约里程法和基于地理信息系统(GIS)的最低成本路径优化法,结合ArcGIS平台和优化求解软件,获得了宁夏CCUS源汇匹配优化和应用方案,并提出宁夏CCUS集群部署建议。结果表明,截止2021年,宁夏工业碳排放源107个,碳排放总量2.26亿t/a,以化工(含自备电厂)和电力行业碳排放为主。宁夏主要封存地质体包括深部咸水层、深部不可开采煤层和油气藏,CO_(2)理论地质封存容量151.55亿t,以深部咸水层封存潜力最大。宁夏CCUS源汇匹配效果较好,在源汇直接相连的情况下,区内年排放量10万t以上的大型工业排放源CCUS集群部署(30 a规划期)总成本约2.45万亿元,并以捕集成本为主,占比83.65%,单位减排成本402.32元/t,共需建设CO_(2)运输管道2 459 km;改进的节约里程法和基于GIS的最低成本路径优化法可大幅降低CCUS集群部署成本,优化后CCUS单位减排成本降至381.76元/t,节约管道建设里程938 km。宁夏应聚焦电力、化工等“两高”(高耗能、高排放)行业,在以宁东能源化工基地为重点的北部、东部地区超前应用CCUS技术,打造宁东能源化工基地、银川—吴忠、石嘴山、中卫和固原5个CCUS特色集群,构建宁夏特色的工程化CCUS全流程技术模式。

正流量控制挖掘机泵阀响应特性及匹配优化研究

以正流量控制挖掘机液压系统为研究对象,建立主泵排量调节系统与多路换向系统数学模型,搭建其AMESim仿真模型,探究铲斗联主阀开启过程中泵阀系统响应特性。针对铲斗主阀开启过程阀口流量和压力波动过大、速度响应匹配不当等问题,提出降低先导压力与泵排量比例系数k和优化节流槽特征参数的正控泵与阀控系统响应匹配优化方案,并通过试验验证优化方案的有效性。结果表明:优化后阀芯在开启阶段的流量波动幅值和压力波动显著降低,流量波动幅值降低了18.19%,压力波动峰值降低了2.8 MPa。

外卖智能派单的订单-骑手多目标匹配模型及其适应性算法研究

随着外卖业的快速发展,外卖智能派单成为外卖服务的重要形式。然而,目前相关研究侧重于研究外卖配送路径问题,不利于解决外卖智能派单的多目标动态决策中存在的问题。本文从外卖订单-骑手匹配的角度,分析了外卖平台智能派单的五个基本目标和四个基本约束,构建了多目标的订单-骑手匹配优化模型,进而给出了基于适应性策略的实时优化算法。最后,本文设计了外卖智能派单的仿真实验,对比了骑手-订单数量均衡、骑手供过于求、骑手供不应求等三种情况下的优化情况,验证了基于适应性策略的实时优化算法的优化效果。本研究对外卖平台发展智能派单模式具有较好的理论与实践价值。

一种新型的电能质量扰动信号分析的CDMSPSO-MP算法

针对匹配追踪(matching pursuit,MP)算法在检测电能质量扰动信号时存在的计算量大、重构信号质量不佳的问题,利用混沌动态多种群粒子群优化(chaos dynamic multi-swarm particle swarm optimization,CDMSPSO)算法对MP算法进行优化,提出了CDMSPSO-MP算法。首先,CDMSPSO算法使用Logistic映射替代伪随机数更新种群,提高信号重构时搜索时频原子的随机性;然后,将种群划分为多个小规模种群并设置相应的重组期,增加信号重构时频原子的多样性;最后,以扰动信号与原子内积的绝对值作为CDMSPSO算法的适应度函数,替代MP算法的遍历计算,提升信号的重构速度。实验结果表明,CDMSPSO-MP算法有效提高了计算速度,减少了无关时频原子作为扰动信号分量的计算,提高了重构信号的质量。