Customization of ZrO2 loose/tight bilayer ultrafiltration membranes by reverse micelles-mediated aqueous sol-gel process for wastewater treatment

Two types of spherical zirconyl oxalate aqueous sols were successfully customized by a reverse micelles-mediated aqueous sol-gel process, and the sols were sequentially spin-coated on porous supports to prepare ZrO₂ loose/tight bilayer ultrafiltration membranes. After three times of spin-coating process, a defect-free ZrO₂ loose ultrafiltration membrane with pure water permeability of 110.5 ± 2.25 L m^?2 h^-1 bar^-1, molecular weight cut-off (MWCO) of 16.5 kDa and excellent rejection of up to 97.5 % for bovine serum albumin was fabricated. Then, the loose ultrafiltration membrane was used as a substrate to prepare ZrO₂ tight ultrafiltration membrane. Performances of tight ultrafiltration membrane regarding to permeability, retention of polyethylene glycol and treatment of dyes wastewater were evaluated. The tight ultrafiltration membrane with a thickness of 200 nm exhibited a pure water permeability of 22.5 ± 0.3 L m^-2 h^-1 bar^-1 and MWCO of 1150 Da. Additionally, the rejections of methyl red and methyl orange by the tight ultrafiltration membrane were both <65 %, while of alizarin red, direct red, bromocresol green and methyl blue achieved maximum values of 98.5 %, 99.2 %, 99.5 % and 99.6 %, respectively. The fouled membranes could restore the virgin performance for reuse by cleaning and low-temperature calcination.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

FPGA互连测试中的反馈桥接故障覆盖问题

现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题；然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。     

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.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

用于电弧检测的电力专用SoC设计

针对传统的电弧电路故障检测结果不准确的问题,设计用于电弧检测的SoC系统,并且在55nm工艺下进行流片验证。采用包含两种结构的模数转换器的片上电压源,设计了锁相环以及复位电路,精度最高可达8.67 bit。验证结果表明,本设计可提高电弧检测的准确性。     

Crystal structure,polarization properties and reverse nonlinearity of solid solutions of the KNN-PZT system in a wide range of external influences

Experimental research of the crystal structure, polarization properties, and reverse nonlinearity of ceramic solid solutions of the (1-x) (Na_0·5K_0.5)NbO₃-xPb(Ti_0·5Zr_0.5)O₃ (KNN-PZT) quasi-binary system with 0.0 = x ≤ 1.0 in a wide range of external influences (temperatures, strength of dc/ac fields) has been done. Based on the X-ray structural data, an x-T diagram of the system has been constructed, and correlations of the behavior of the macroproperties of solid solutions with the features of their phase states with the temperature change have been established. It has been concluded that it is advisable to use the proposed compositions when designing microelectronic devices operating in various extreme conditions.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Highly efficient full-fluorescence organic light-emitting diodes with exciplex cohosts

Full-fluorescence organic light-emitting diodes (FOLEDs) with low cost and high efficiency are imperious demands for commercial process in flat panel display and lighting products. We fabricated a series of FOLEDs employing C545T and DCJTB as doped dyes and different exciplex blends as cohosts. The results proved that reverse intersystem crossing (RISC) efficiency of exciplex cohost has a significant effect on the device performance. Devices with TAPC:PIM-TRZ as cohost which possessed the highest RISC efficiency showed the best results. The green FOLEDs exhibited the maximum external quantum efficiencies (EQEs) approaching to 20%, the red FOLEDs exhibited EQEs over 10% and all the EQE roll-offs are less than 10% at 1000 cd m⁻², which are among the best reported results so far, suggesting these exciplex cohosts are promising for FOLEDs.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.