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.     

Rectangular Section Control Technology for Silicon Steel Rolling

Rectangular section control technology(RSCT)was introduced to achieve high-precision profile control during silicon steel rolling.The RSCT principle and method were designed,and the whole RSCT control strategy was developed.Specifically,RSCT included roll contour design,rolling technology optimization,and control strategy development,aiming at both hot strip mills(HSMs)and cold strip mills(CSMs).Firstly,through the high-performance variable crown(HVC)work roll optimization design in the upper-stream stands and the limited shifting technology for schedule-free rolling in the downstream stands of HSMs,a hot strip with a stable crown and limited wedge,local spot,and single wave was obtained,which was suitable for cold rolling.Secondly,an approximately rectangular section was obtained by edge varying contact(EVC)work roll contour design,edge-drop setting control,and closed loop control in the upper-stream stands of CSMs.Moreover,complex-mode flatness control was realized by coordinating multiple shape-control methods in the downstream stands of CSMs.In addition,the RSCT approach was applied in several silicon-steel production plants,where an outstanding performance and remarkable economic benefits were observed.     

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.     

Characterizing physical habitat preferences and thermal refuge occupancy of brook trout (Salvelinus fontinalis) and Atlantic salmon (Salmo salar) at high river temperatures

Anthropogenic influences, including climate change, are increasing river temperatures in northern and temperate regions and threatening the thermal habitats of native salmonids. When river temperatures exceed the tolerance levels of brook trout and Atlantic salmon, individuals exhibit behavioural thermoregulation by seeking out cold‐water refugia – often created by tributaries and groundwater discharge. Thermal infrared (TIR) imagery was used to map cold‐water anomalies along a 53 km reach of the Cains River, New Brunswick. Trout and salmon parr did not use all identified thermal anomalies as refugia during higher river temperature periods (>21°C). Most small‐bodied trout (8–30 cm) were observed in 80% of the thermal anomalies sampled. Large‐bodied trout (>35 cm) required a more specific set of physical habitat conditions for suitable refugia, that is, 100% of observed large trout used 30% of the anomalies sampled and required water depths >65 cm within or adjacent to the anomaly. Densities of trout were significantly higher within anomalies compared with areas of ambient river temperature. Salmon parr were less aligned with thermal anomalies at the observed temperatures, that is, 59% were found in 65% of the sampled anomalies; and densities were not significantly different within/ outside anomalies. Salmon parr appeared to aggregate at 27°C, and after several events over 27°C variability in aggregation behaviour was observed – some fish aggregated at 25°C, others did not. We stipulate this is due to variances of thermal fatigue. Habitat suitability curves were developed for velocity, temperature, depth, substrate, and deep water availability to characterize conditions preferred by fish during high‐temperature events. These findings are useful for managers as our climate warms, and can potentially be used as a tool to help conserve and enhance thermal refugia for brook trout and Atlantic salmon in similar systems.     

燃气热水器在无回水管水路中实现零冷水的方案研究

本文分析了燃气热水器行业针对无回水管水路系统实现零冷水功能的现有技术方案,指出现有技术方案存在的一些缺陷,并结合试验研究,对无回水管水路系统提出了一套全新的实现零冷水功能的技术解决方案。     

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.     

沥青混合料的使用性能与试验方法分析

沥青混合料的使用性能与试验方法对保证和提高沥青路面的修建质量非常重要。在使用荷载、自然因素对沥青路面作用分析的基础上,对沥青混合料的温度稳定性、耐久性、抗滑性及施工的和易性等方面进行了系统的分析探讨,提出了相关的性能要求与试验评价方法。