Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation

The performance of Microbial electrolysis cell (MEC) is affected by several operating conditions. Therefore, in the present study, an optimization study was done to determine the working efficiency of MEC in terms of COD (chemical oxygen demand) removal, hydrogen and current generation. Optimization was carried out using a quadratic mathematical model of response surface methodology (RSM). Thirteen sets of experimental runs were performed to optimize the applied voltage and hydraulic retention time (HRT) of single chambered batch fed MEC operated with dairy industry wastewater. The operating conditions (i.e) an applied voltage of 0.8 V and HRT of 2 days that showed a maximum COD removal response was chosen for further studies. The MEC operated at optimized condition (HRT- 2 days and applied voltage- 0.8 V) showed a COD removal efficiency of 95 ± 2%, hydrogen generation of 32 ± 5 mL/L/d, Power density of 152 mW/cm² and current generation of 19 mA. The results of the study implied that RSM, with its high degree of accuracy can be a reliable tool for optimizing the process of wastewater treatment. Also, dairy industry wastewater can be considered to be a potential source to generate hydrogen and energy through MEC at short HRT.     

基于重要度排序的配电自动化终端优化布局方法

针对配电自动化终端优化布局问题，提出了一种基于重要度排序的终端优化布局方法。首先以等年值综合费用为目标函数，供电可靠性和投入产出比为双重约束建立了配电自动化终端优化布局模型。然后通过分析配电自动化终端对供电可靠性提升的影响，给出各个节点的“二遥”及“三遥”终端安装重要度定义及计算公式。最后采用枚举法确定最优终端安装数量，基于节点终端安装重要度排序确定终端的最优安装位置。该方法考虑了已布局节点对剩余节点终端安装重要度的影响，能够在降低计算量的同时兼顾布局合理性。运用所提方法RBTS-BUS2系统及扩充模型进行终端优化布局并与智能优化算法的布局结果进行对比，验证了本文方法的有效性及优越性。     

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。     

Laboratory Studies to Determine Suitable Chemicals to Improve Oil Recovery from Garzan Oil Field

Garzan oil field is located at the south east of Turkey. It is a mature oil field and the reservoir is fractured carbonate reservoir. After producing about 1% original oil in place (OOIP) reservoir pressure started to decline. Waterflooding was started in order to support reservoir pressure and also to enhance oil production in 1960. Waterflooding improved the oil recovery but after years of flooding water breakthrough at the production wells was observed. This increased the water/oil ratio at the production wells. In order to enhance oil recovery again different techniques were investigated. Chemical enhanced oil recovery (EOR) methods are gaining attention all over the world for oil recovery. Surfactant injection is an effective way for interfacial tension (IFT) reduction and wettability reversal. In this study, 31 different types of chemicals were studied to specify the effects on oil production. This paper presents solubility of surfactants in brine, IFT and contact angle measurements, imbibition tests, and lastly core flooding experiments. Most of the chemicals were incompatible with Garzan formation water, which has high divalent ion concentration. In this case, the usage of 2-propanol as co-surfactant yielded successful results for stability of the selected chemical solutions. The results of the wettability test indicated that both tested cationic and anionic surfactants altered the wettability of the carbonate rock from oil-wet to intermediate-wet. The maximum oil recovery by imbibition test was reached when core was exposed 1-ethly ionic liquid after imbibition in formation water. Also, after core flooding test, it is concluded that considerable amount of oil can be recovered from Garzan reservoir by waterflooding alone if adverse effects of natural fractures could be eliminated.     

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.     

贵州职业技术学院数据中心双活技术架构的研究

The importance of data processing and storage to colleges and universities is increasing day by day. However, many colleg- es and universities are still at a low level in the process of data recovery and application reduction, and its RPO and RTO indicators need to be improved by adopting the dual active technology architecture. Taking the Guizhou Vocational Technology Institute as an example, this paper focuses on the analysis of the architecture of the double active data center, aiming at the actual situation of the college, and gradually realizes multiple disaster recovery level of data level, application level and business level.     

矿用直线振动筛结构故障分析

针对煤矿选煤厂直线振动筛工作过程中出现的结构故障问题,采用有限元仿真与现场测试结合的方法对振动筛出现故障的原因进行了分析研究。介绍了振动筛的主要结构及常见故障,并以某矿选煤厂发生大梁断裂的振动筛为例,对该振动筛进行三维建模和有限元模态分析,分析结果表明:随着模态阶次的增加,振型的变化逐渐由移动转为结构本身的扭曲变形。在该振动筛空载工作状态下,选用本质安全型便携式测振记录仪进行振动测试并处理测试信号,得到该振动筛的工作频率。结合模态分析结果和实际测试结果,判断出空载时频繁运行振动筛是造成其大梁断裂的主要原因。根据故障原因给出维护建议:应当尽量避免振动筛在空载或物料较少的状态下长时间运行;在振动筛投入使用前,应预先了解振动筛的工作状态及可能存在的薄弱环节,尽可能避免结构故障,保证振动筛可靠运行。     

海上稠油油田蒸汽吞吐产量确定新方法

针对目前蒸汽吞吐产量预测模型假设条件简单、普适性差等问题,一般采用测试法和类比法综合确定海上稠油油田蒸汽吞吐初期产量。由于目前海上油田通常只开展常规测试,无法直接获得热采开发初期产量。笔者提出海上稠油油田蒸汽吞吐初期产量确定新方法,建立蒸汽吞吐相对于常规开发的初期产量倍数预测模型,通过蒸汽吞吐产量倍数,将常规测试确定的产量转化为蒸汽吞吐产量。研究表明,蒸汽吞吐初期产量倍数主要受储集层渗透率、原油黏度、注入强度、蒸汽干度等因素影响,利用正交试验设计和多元回归等方法,建立海上稠油油田蒸汽吞吐初期产量倍数与油藏地质参数及注入参数之间的非线性预测模型,该模型经实际生产数据验证,预测误差小于5%,可靠性高,能够为海上稠油油田蒸汽吞吐初期产量的确定提供依据。     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.