Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Flexural behaviour of RC slabs strengthened with prestressed CFRP strips using different anchorage systems

The Externally Bonded Reinforcement (EBR) technique using Carbon Fiber-Reinforced Polymers (CFRP) has been commonly used to strengthen concrete structures in flexure. The use of prestressed CFRP material offers several advantages well-reported in the literature. Regardless of such as benefits, several studies on different topics are missing. The present work intends to contribute to the knowledge of two commercially available systems that differ on the type of anchorage: (i) the Mechanical Anchorage (MA), and (ii) the Gradient Anchorage (GA). For that purpose, an experimental program was carried out with twelve slabs monotonically tested under displacement control up to failure by using a four-point bending test configuration. The effect of type of anchorage system (MA and GA), prestrain level (0 and 0.4%), width (50 mm and 80 mm) and thickness (1.2 mm and 1.4 mm) of the CFRP laminate, and the surface preparation (grinded and sandblasted) on the flexural response were the main studied parameters. Better performance was observed for the slabs: (i) with prestressed laminates, (ii) for the MA system, and (iii) with sandblasted surface preparation.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation

Heuristic algorithms (HAs) are widely used in multi-objective reservoir optimal operation (MOROO) due to the rapidity of the calculation and simplicity of their design. The literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and a specific comparison of HAs in MOROO, differential evolution (DE), particle swarm optimisation (PSO), and artificial physics optimisation (APO), which serve as typical examples of the three categories of HAs, are compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solution process using a hybrid feasible domain restoration method and penalty function method are also presented. Taking a designed experiment with multiple scenarios, the mean average of the optimal objective function values, the standard deviation of optimal objective function values, the mean average of the computational time, and population diversity are used for comparisons. Results of the comparisons show that (a) the problem of optimal multipurpose reservoir long-term operation is a mathematic programming problem with narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value, but different optimal solutions using HAs; and (c) comparisons do not result in a clear winner, but DE can be more appropriate for MOROO.

     

硫酸盐含量对全固废材料固化盐渍土抗压强度的影响

为探讨硫酸盐含量对全固废材料固化盐渍土抗压强度及微观结构的影响,采用击实试验和无侧限抗压强度试验,并结合扫描电镜、EDS、X射线衍射和热重分析等微观测试结果,从固化盐渍土的火山灰反应产物及其数量等角度分析含盐量对固化盐渍土抗压强度变化的影响。结果表明:硫酸盐含量低于2.7%时,固化盐渍土抗压强度随含盐量的增加呈先增大后减小,抗压强度峰值对应的含盐量为1.8%;固化盐渍土的火山灰反应产物主要为C-S-H和AFt,硫酸盐含量从0.3%增至1.8%时,反应产物明显增多,致使固化土抗压强度增大;但当硫酸盐含量从1.8%增至2.7%时,膨胀性AFt将试件内部孔隙完全填充并产生胀裂破坏,造成抗压强度降低。研究成果为全固废材料固化硫酸盐渍土的工程应用奠定了基础,对环境保护也有积极贡献。     

不同养护方式对水泥-锂渣浆体水化程度影响

为了分析水泥-锂渣浆体的水化程度, 采用高温煅烧法测试各龄期的化学结合水, 结果发现:水泥-锂渣浆体的化学结合水量随龄期的延长而增加, 水化3 d和7 d时能达到水化90 d时的60%和80%。高温养护、碱激发、高温和碱激发均能提高锂渣复合水泥基材料早期的化学结合水量, 最高可达3~4倍, 提高的幅度依次为碱激发和高温养护＞碱激发＞高温养护＞标准养护。高温和复合环境养护也能提高水泥的水化程度, 1~28 d内, 锂渣掺量在40%以内时, 水泥水化程度相对指数(ψ值)均大于1;掺量为60%时, ψ值均小于1。综上, 高温养护、碱激发、高温和碱激发均能提高锂渣和水泥的水化程度, 高温和碱激发复合作用时较为显著。     

高温气候下沥青混凝土心墙连续碾压施工结合面温控试验研究

在夏季高温气候沥青混凝土心墙连续多层施工时,为减少等待结合面温度降至规范要求上限值90℃的时间,通过室内试验模拟结合面温度提高后心墙连续两层铺筑的情况,分别研究了结合面温度在90℃、100℃、110℃情况下沥青混凝土的压实性和变形情况,并进行了现场试验论证。室内试验结果表明:心墙沥青混凝土结合面温度降至100℃时,上层沥青混凝土的孔隙率为2.88%,下层沥青混凝土的最大侧向应变值为2.78%;结合面温度为110℃时,上层沥青混凝土的孔隙率为3.85%,下层沥青混凝土的最大侧向应变值为4.00%。现场试验结果表明:结合面温度为100℃时的上层沥青混凝土孔隙率和渗透系数均满足规范要求。因此,沥青混凝土心墙连续多层碾压时,结合面温度提高至100℃的施工质量可以得到保证。     

基于集对分析联系数的合肥市水资源承载力评价

为科学指导合肥市水资源综合规划与管理,在综合分析水资源承载力与水资源-社会经济-生态环境系统相互关系的基础上,从支撑力、压力、调控力子系统选取13项指标,建立合肥市水资源承载力评价指标体系,运用集对分析联系数评价模型对合肥市2011-2016年水资源承载力进行综合评价。结果表明:合肥市2011-2015年水资源承载力评价等级为3级,2016年为2级,整体形势严峻,承载能力较差,但已有所改善,发展趋势良好;运用减法集对势理论,确定指标人均水资源量、产水模数、植被覆盖率、人口密度、水资源开发利用率、生态用水率是导致合肥市水资源承载力不足的主要因素。