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.     

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.     

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.     

水煤浆燃烧技术及其发展

分析了水煤浆雾化燃烧的技术状况、应用方向和存在问题，介绍了水煤浆流化—悬浮高效洁净燃烧新技术及其优点，结合实践应用情况，分析了发展水煤浆流化—悬浮燃烧技术的意义和应用前景。     

沥青油毡瓦国内外标准对比分析

从范围、原材料要求、组分要求、物理力学性能等方面对比分析了我国现行油毡瓦标准与欧美标准的区别,提出了我国制订沥青油毡瓦国家标准的的原则。     

峡口水库沥青混凝土面板的施工与试验

以灌溉、防洪为主的峡口水库大坝，为沥青混凝土面板堆石坝，为研究低温抗裂性能，对沥青混凝土面板破损取样，进行了配比和多种力学性能试验，并与原面板设计指标进行了对比。由于面板经受住了－20℃以下的低温考验，其所用沥青指标、破损取样试验结果，特别是冻断低温指标，对确定水工沥青混凝土低温抗裂检验标准有重要参考价值。     

从关联分析法看如何改善沥青抗老化性能

用灰色关联分析法研究了沥青抗老化性能与沥青化学组成之间的关联程度，结果表明，影响再生沥青性质的主要因素是沥青中的芳烃和胶质的含量。通过添加芳香组分能改善沥青四组分之间的配伍性，从而可以改善再生沥青的抗老化性能。并通过对再生剂进行富芳处理，验证了上述结论。     

影响道路沥青低温抗裂性能的因素

用弯曲梁流变仪、直接拉伸试验机对7种国内外油源沥青样品进行了低温抗裂性能的评价，从沥青的四组分、相对分子质量及其分布来分析其对沥青低温抗裂性能的影响，结果表明，沥青的相对分子质量分布宽，对沥青的低温抗裂性能有利。