Systematic analysis and multi-objective optimization of an integrated power and freshwater production cycle

This study represents the results of the analysis and optimization of an integrated system for cogenerating electricity and freshwater. This setup consists of a Solid Oxide Fuel cell (SOFC) for producing electricity. Unburned fuel of the SOFC is burned in the afterburner to increase the temperature of the SOFC's outlet gasses and operate a Gas turbine (GT) to produce additional power and operate the air compressor. At the bottom of this cycle, a combined setup of a Multi-Effect Desalination (MED) and Reverse Osmosis (RO) is considered to produce freshwater from the unused heat capacity of the GT's exhaust gasses. Also, a Stirling engine is used in the fuel supply line to increase the fuel's temperature. Using LNG and the Stirling engine will replace the fuel compressor with a pump which increases the system performance and eliminates the need for the expansion valve. To study the system performance a mathematical model is developed in Engineering Equation Solver (EES) program. Then, the system's simulated data from the EES has been sent to MATLAB to promote the best operating condition based on the optimization criteria. An energetic, exergetic, economic, and environmental analysis has been performed and a Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to achieve the goal. The two-objective optimization is performed to maximize the exergetic efficiency of the proposed system while minimizing the system's total cost of production. This cost is a weighted distribution of the Levelized Cost of Electricity (LCOE) and Levelized Cost of freshwater (LCOW). The results showed that the exergetic and energetic efficiencies of the system can reach 73.5% and 69.06% at the optimum point. The total electricity production of the system is 99 MW. The production cost is 11.71 Cents/kWh, of which 1.04 Cents/kWh is emission-related and environmental taxes. The freshwater production rate is 42.44 kg/s which costs 4.38 USD/m³.     

Feeding behavior parameters and temporal patterns in mid-lactation Holstein cows across a range of residual feed intake values

Residual feed intake (RFI) is a measurement of the difference between actual and predicted feed intake when adjusted for energy sinks; more efficient cows eat less than predicted (low RFI) and inefficient cows eat more than predicted (high RFI). Data evaluating the relationship between RFI and feeding behaviors (FB) are limited in dairy cattle; therefore, the objective of this study was to determine daily and temporal FB in mid-lactation Holstein cows across a range of RFI values. Mid-lactation Holstein cows (n = 592 multiparous; 304 primiparous) were enrolled in 17 cohorts at 97 ± 26 d in milk (± standard deviation), and all cows within a cohort were fed a common diet using automated feeding bins. Cow RFI was calculated as the difference between predicted and observed dry matter intake (DMI) after accounting for parity, days in milk, milk energy, metabolic body weight and change, and experiment. The associations between RFI and FB at the level of meals and daily totals were evaluated using mixed models with the fixed effect of RFI and the random effects of cow and cohort. Daily temporal FB analyses were conducted using 2-h blocks and analyzed using mixed models with the fixed effects of RFI, time, RFI × time, and cohort, and the random effect of cow (cohort). There was a positive linear association between RFI and DMI in multiparous cows and a positive quadratic relationship in primiparous cows, where the rate of increase in DMI was less at higher RFI. Eating rate, DMI per meal, and size of the largest daily meal were positively associated with RFI. Daily temporal analysis of FB revealed an interaction between RFI and time for eating rate in multiparous and primiparous cows. The eating rate increased with greater RFI at 11 of 12 time points throughout the day, and eating rate differed across RFI between multiple time points. There tended to be an interaction between RFI and time for eating time and bin visits in multiparous cows but not primiparous cows. Overall, there was a time effect for all FB variables, where DMI, eating time and rate, and bin visits were greatest after the initial daily feeding at 1200 h, increased slightly after each milking, and reached a nadir at 0600 h (6 h before feeding). Considering the relationship between RFI and eating rate, additional efforts to determine cost-effective methods of quantifying eating rate in group-housed dairy cows is warranted. Further investigation is also warranted to determine if management strategies to alter FB, especially eating rate, can be effective in increasing feed efficiency in lactating dairy cattle.     

C105管桩混凝土设计参数取值的探讨

通过试验得出了C105混凝土100 mm×100 mm×100 mm立方体非标准试件抗压强度换算系数以及C105混凝土的标准方差和强度变异系数。采用理论计算,得到了棱柱体抗压强度与立方体抗压强度的比值α_c1,并与试验实测平均值进行了比较,验证了理论计算方法的可行性。此外,通过对比分析,探讨了直接外延法和引用公式法两种C105混凝土设计参数计算方法的正确性,研究结果可为C105管桩混凝土的设计和应用提供参考。     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

基于C#持久蠕变强度外推功能的实现

持久蠕变强度外推是预测材料在高温高压环境下工作寿命的有效方法。目前常用的外推方法有等温法和参数法。这两种方法都是通过在实验室中采用较短时间的试验，获取试验数据后，通过Excel或者MATLAB等数据处理软件进行数据的拟合处理和外推计算。这些数据归纳统计和数学建模的过程有时需要耗费试验人员的大量精力。介绍了持久蠕变强度外推的软件实现方法，该功能包含在持久蠕变试验设备配备的软件中，试验完成后，软件直接根据试验结果进行数据拟合和持久蠕变强度的外推计算，无需试验数据的统计和建模，大大提高了持久蠕变强度外推计算的效率。     

梁结构振动支承约束反力控制

支承或连接构件对梁结构的动力学性能有至关重要影响,必须保证其在振动过程中不发生破坏或者失效。通过合理设计和布局附加弹性支承可以实现对这些重要连接构件所承受约束反力的控制。应用微分变换法推导含附加支承的梁结构支承约束反力及其对于附加支承位置和刚度的灵敏度表达式,并通过优化设计附加支承位置和刚度实现具有弹性约束端的简支梁结构各支承约束反力的平衡,可提高结构的动力学性能。     

Two Methods,One Goal: Structural Differences between Cocrystallization and Crystal Soaking to Discover Ligand Binding Poses

In lead optimization, protein crystallography is an indispensable tool to analyze drug binding. Binding modes and non-covalent interaction inventories are essential to design follow-up synthesis candidates. Two protocols are commonly applied to produce protein–ligand complexes: cocrystallization and soaking. Because of its time and cost effectiveness, soaking is the more popular method. Taking eight ligand hinge binders of protein kinase A, we demonstrate that cocrystallization is superior. Particularly for flexible proteins, such as kinases, and larger ligands cocrystallization captures more reliable the correct binding pose and induced protein adaptations. The geometrical discrepancies between soaking and cocrystallization appear smaller for fragment-sized ligands. For larger flexible ligands that trigger conformational changes of the protein, soaking can be misleading and underestimates the number of possible polar interactions due to inadequate, highly impaired positions of protein amino-acid side and main chain atoms. Thus, if applicable cocrystallization should be the gold standard to study protein–ligand complexes.     

A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.     

浅渍法发酵豇豆的工艺优化

以具有降解亚硝酸盐功能的植物乳杆菌（Lactobacillus plantarum）SD-7和具有优良抗氧化能力的植物乳杆菌（Lactobacillus plantarum）FM-LP-9为复合发酵剂（1∶1），采用浅渍法发酵豇豆。以硬度和感官评分为考察指标，通过单因素试验及响应面试验研究复合发酵剂接种量、发酵温度和发酵时间对浅渍法发酵豇豆品质的影响。结果表明，最佳发酵工艺条件为发酵温度25 ℃，接种量5%，发酵时间125 h，在此优化条件下，得到的浅渍法发酵豇豆硬度为47.31 N，感官评分为90.98分，香气浓郁、口感脆嫩。