Thermo-economic investigation on the hydrogen production through the stored solar energy in a salinity gradient solar pond: A comparative study by employing APC and ORC with zeotropic mixture

In this paper, a salinity gradient solar pond (SGSP) is used to harness the solar energy for hydrogen production through two cycles. The first cycle includes an absorption power cycle (APC), a proton exchange membrane (PEM) electrolyzer, and a thermoelectric generator (TEG) unit; in the second one, an organic Rankine cycle (ORC) with the zeotropic mixture is used instead of APC. The cycles are analyzed through the thermoeconomic vantage point to discover the effect of key decision variables on the cycles’ performance. Finally, NSGA-II is used to optimize both cycles. The results indicate that employing ORC with zeotropic mixture leads to a better performance in comparison to utilizing APC. For the base mode, unit cost product (UCP), exergy, and energy efficiency when APC is employed are 59.9 $/GJ, 23.73%, and 3.84%, respectively. These amounts are 47.27 $/GJ, 29.48%, and 5.86% if ORC with the zeotropic mixture is utilized. The APC and ORC generators have the highest exergy destruction rate which is equal to 6.18 and 10.91 kW. In both cycles, the highest investment cost is related to the turbine and is 0.8275 $/h and 0.976 $/h for the first and second cycles, respectively. In the optimum state the energy efficiency, exergy efficiency, UCP, and H₂ production rate of the system enhances 42.44%, 27.54%,15.95%, and 38.24% when ORC with the zeotropic mixture is used. The maximum H₂ production is 0.47 kg/h, and is obtained when the mass fraction of R142b, LCZ temperature, pumps pressure ratio, generator bubble point temperature are 0.603, 364.35 K, 2.12, 337.67 K, respectively.     

道路用聚氨酯改性沥青的制备工艺探讨

结合不同改性剂掺量单因素试验,确定了采用88mm叶轮、115mm容器、圆盘锯齿式搅拌器(转速1400r/min)、175℃共混温度、改性剂掺量4.27%、单次搅拌300g的制备工艺参数。在此条件下制备的聚氨酯改性沥青具有优异的水稳定性、储存稳定性且耐老化、耐高温,拥有比普通聚合物改性沥青更高的车辙因子G*/sinδ和15℃动态模量,基本满足高模量沥青要求。     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

Torsional statics of two-dimensionally functionally graded microtubes

A size-dependent governing equation is derived to investigate the torsional static behaviors of two-dimensionally functionally graded microtubes based on the modified couple stress theory. The shear modulus is assumed to vary along the tube’s length direction according to an exponential distribute function, and varies along the tube’s radius direction according to a power-law function. A generalized differential quadrature method is developed to determine the rotational angle and shear stresses. Some illustrative examples are given to investigate the effects of applied torques, the length scale parameter and various material compositions on the torsional angle and shear stresses.     

一种5 重real-time PCR筛查转基因水稻方法的建立

以转基因水稻中最常用的CaMV35S启动子、NOS终止子、Cry1Ab/Ac基因、HPT基因及SPS水稻内标基因为研究对象，利用5 种不同的荧光信号（FAM、HEX、Taxas Red、Cy5、Cy5.5）进行多重实时聚合酶链式反应（real-time polymerase chain reaction，real-time PCR）检测方法的研究。通过引物组合筛选、反应体系优化、特异性测试、灵敏度测试、适用性测试等一系列实验，建立了5 重real-time PCR方法，灵敏度可达0.032%。此方法具有灵敏度高、结果准确、通量大等优点，可实现水稻中转基因成分的快速、高效检测。     

Multiplex PCR system to track authorized and unauthorized genetically modified soybean events in food and feed

A diverse range of genetic elements has been used to develop genetically modified organisms (GMOs) over the last 18 years. Screening methods that target few elements, such as the Cauliflower Mosaic Virus 35S promoter (P-35S) and Agrobacterium tumefaciens nopaline terminator (T-nos), are not sufficient to screen GMOs. In the present study, a multiplex PCR system for all globally commercialized GM soybean events was developed to easily trace the events. For this purpose, screening elements of 24 GM soybean events were investigated and 9 screening targets were selected and divided into three individual triplex PCR systems: P-35S, ribulose-1,5-bisphosphate carboxylase small subunit promoter of Arabidopsis thaliana, T-nos, T-35S, pea E9 terminator, open reading frame 23 terminator of A. tumefaciens, proteinase inhibitor II terminator of potato, acetohydroxy acid synthase large subunit terminator of A. thaliana, and the revealed 3′ flanking sequences of DP-305423-1. The specificity of the assays was confirmed using thirteen GM soybean events as the respective positive/negative controls. The limit of detection of each multiplex set, as determined using certified reference materials of specific GM events, ranged from 0.03 to 0.5%, depending upon target. Furthermore, 26 food samples that contained soybean ingredients, which were purchased from the USA, China, Japan, and Korea, were analyzed, 17 of which contained one or more GM soybean events. These results suggest that the developed screening method can be used to efficiently track and identify 24 GM soybean events in food and feed.     

邢钢180 m2烧结机提高混合料温度的实践

介绍了邢钢180 m^2烧结机投产以来,在提高混合料温度方面所采取的主要措施.该厂通过提高生石灰质量、采用蒸汽预热混合料、在混合机中加热红泥浆等,改善了混合料的透气性,进而提高了烧结矿产、质量.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.