大气颗粒物及其组成研究进展(上)

颗粒物是空气中最重要的污染物之一,由于颗粒物表面的吸附作用,其组分非常复杂,其中含有多种有毒有害的化学成分,对大气环境造成不良影响,并危及人体健康。借助大气颗粒物及其组成成分的测定,通过污染源解析分析其来源及影响因素,控制大气颗粒物的排放,对于保证大气环境质量、改善人们的生存环境具有重大意义。本文对此进行综述。着重介绍大气颗粒物及其组成成分、源解析及测量技术的最新进展。     

Modeling of compressive strength of cemented sandy soil

This paper attempted to show the application of particle swarm optimization in the prediction of the compressive strength of cement sandy soil from the curing period, porosity of sample and percentage of cement. The results of the study show that the unconfined compressive strength of the cement stabilized sandy soil increases with an increasing cement content curing time period. Moreover the compressive strength decreases with an increasing porosity. The compressive strength improvement due to cement treatment has a larger increase in samples with less porosity. In addition, particle swarm optimization algorithm is and accurate technique in estimation of compressive strength of cement stabilized sandy soil. In order to compare of existing correlations, a total number of 100 unconfined compressive tests and 15 scanning electron microscope tests have been conducted on cemented Babolsar sand. It can be concluded that compared to existing correlations models, particle swarm optimization algorithm models give more reliable prediction about compressive strength of cement satblized sandy soil. Moreover, the sensitivity analysis of the polynomial model shows that cement content and porosity have significant impact on predicting unconfined compressive strength.     

硼颗粒的包覆机理及工艺研究进展

阐述了不同包覆材料对硼颗粒的包覆机理,从5个方面总结了硼颗粒包覆材料的选取原则,包括:去除硼颗粒表面氧化膜、提高燃烧温度、降低硼的点火温度、提高表面相容性、催化硼颗粒的氧化反应。总结了沉淀法、表面反应包覆法、高分子吸附聚合法、气相包覆法和机械球磨法等多种硼颗粒包覆工艺的研究状况,分析并比较了不同工艺的作用机理和实际应用效果。介绍了现代硼颗粒表面包覆效果测试技术的特点和应用范围。评述了目前硼颗粒包覆技术的研究现状和不足,并对未来的研究方向进行了展望。     

Coupling of an innovative small PWR and advanced sodium‐cooled fast reactor for incineration of TRU from once‐through PWRs

In the nuclear industry, safely managing spent fuels discharged from PWRs (pressurized water reactors) is an ongoing challenge. In this paper, a synergistic coupling of innovative small long‐cycle PWRs and advanced sodium‐cooled fast reactors is considered to reduce the accumulated TRUs (transuranics) by transmuting them with electricity production. In the coupling strategy, the innovative small PWRs employing UO₂–ThO₂ and fully ceramic micro‐encapsulated fuels are used to deeply burn TRUs from commercial PWRs, while advanced SFRs (sodium‐cooled fast reactors) with actinide recycling are designed to further transmute the TRUs discharged from innovative small PWRs. This work focuses on the core physics analysis of new SFR burner cores using different TRU feeds discharged from small PWRs. Additionally, quasi‐static reactivity balance analyses are performed to understand the safety of the SFR burner cores. The mass flows of TRUs in the nuclear park, which is composed of PWRs, small long‐cycle PWRs, and SFR burners, are analyzed to evaluate TRU inventory reduction. The results of this study show that the advanced SFR burners with all the TRU feed types discharged from the small long‐cycle PWRs have a high TRU consumption rate. They satisfy all of the conditions for self‐controllability under unprotected accidents with a reasonable number of control rods. This coupling strategy requires ~35% less power in conjunction with the advanced SFR burners in the nuclear park and increases the support ratio of SFR burners by ~42% than does the coupling of commercial PWRs and SFR burners. Copyright © 2015 John Wiley & Sons, Ltd.     

Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management

Abstract

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.     

Effects of particle sizes on performances of the multi-zone steam generator using waste heat in a bio-oil steam reforming hydrogen production system

Hydrogen production by bio-oil steam reforming is an advanced production technology. It is a good method of coupling waste heat utilization with bio-oil steam reforming to produce hydrogen, which increases the cleaning ability of the bio-oil steam reforming system. A multi-zone steam generator using waste heat has been proposed, which can produce the heat source and steam source of the hydrogen system. The DEM model of the multi-zone steam generator was set up. The model has been used to investigate the effects of particle sizes (40 mm–80 mm). With increasing particle size, the flow index and the flow uniformity gradually decrease, the vertical velocity gradient increases in the area on both side with the zone steam generator, and the vertical velocity fluctuation amplitude gradually increases. So, the hydrogen production decreases from the particle size increasing.     

Physicochemical properties of nanopowdered eggshell

This study was carried out to investigate the physicochemical properties of nanopowdered eggshell (NPES). These characteristics were determined based on the particle size, particle morphology, zeta potential, moisture sorption behaviour, purity and crystallinity. Homogeneous, aggregated and spherical crystals of NPES particles were observed through scanning electron microscope and transmission electron microscope. The average particle sizes of NPES and powdered eggshell (PES) were 202.3 ± 28.9 nm and 113.89 ± 79.37 μm, respectively. Zeta potentials of NPES (?15.41 mV) suggested an incipient instability of the colloidal system. Moisture sorption analysis indicated a higher water adsorption capacity of NPES than that of PES. X‐ray diffractometer (XRD) analysis confirmed the presence of calcite in both NPES and PES. The chemical compositions of the NPES and PES particles were strongly associated with the presence of calcium carbonate, as determined by the Fourier transform infrared (FTIR) spectra. Therefore, eggshell nanopowder has a great potential to be utilised as a component for biomedical applications.     

Enhanced fatigue behavior of a glass fiber reinforced hybrid particles modified epoxy nanocomposite under WISPERX spectrum load sequence

Two types of glass fiber reinforced plastic (GFRP) composites were fabricated viz., GFRP with neat epoxy matrix (GFRP-neat) and GFRP with hybrid modified epoxy matrix (GFRP-hybrid) containing 9 wt.% of rubber microparticles and 10 wt.% of silica nanoparticles. Fatigue tests were conducted on both the composites under WISPERX load sequence. The fatigue life of the GFRP-hybrid composite was about 4–5 times higher than that of GFRP-neat composite. The underlying mechanisms for improved fatigue performance are discussed. A reasonably good correlation was observed between the experimental fatigue life and the fatigue life predicted under spectrum loads.     

A modified support vector regression: Integrated selection of training subset and model

In recent years, support vector regression (SVR) has become an emerging and popular forecasting technique in the field of machine learning. However, it is subjected to the model selection and learning complexity O(K * N³), especially for a massive data set (N is the size of training dataset, and K is the number of search). How to simultaneously reduce K and N can give us insight and inspiration on designing an effective and accurate selection algorithm. To this end, this paper tries to integrate the selection of training subset and model for SVR, and proposes a nested particle swarm optimization (NPSO) by inheriting the model selection of the existing training subset based SVR (TS-SVR). This nested algorithm is achieved by adaptively and periodically estimating the search region of the optimal parameter setting for TS-SVR. Complex SVR, involving large-scale training data, can be seen as extensions of TS-SVRs, yielding a nested sequence of TS-SVRs with increasing sample size. The uniform design idea is transplanted to the above modeling process, and the convergence for the proposed model is proofed. By using two artificial regression problems, Boston housing and electric load in New South Wales as empirical data, the proposed approach is compared with the standard ones, the APSO-OTS-SVR, and other existing approaches. Empirical results show that the proposed approach not only can select proper training subset and parameter, but also has better generalization performance and fewer processing time.