Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.     

压水堆堆芯损伤评价系统研究与开发

堆芯损伤评价是反应堆事故后应急评价的重要组成部分。本文在国外文献基础上,结合国内的运行经验,对压水堆堆芯损伤评价进行研究,并开发了相应的软件程序。堆芯损伤评价包括基于堆芯裸露时间、在线监测仪表读数和取样分析数据三种方法。考虑应急实时要求、电厂实际情况与国际经验,本文采用了基于在线监测仪表读数的评价方法,该方法主要是基于堆芯热电偶读数与安全壳辐射监测仪表读数进行评价,其他监测仪表读数进行辅助合理性证实。     

An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.     

烟气CO2捕集热能梯级利用节能工艺耦合优化

醇胺法捕集CO₂技术是一种较成熟的CO₂捕集技术，具有吸收速度快、脱除效果好等显著优点，但其操作费用高、解吸能耗大。本文以降低醇胺法捕集烟气中CO₂系统再生能耗为出发点，对常规醇胺法捕集CO₂工艺统进行了节能优化研究。在常规工艺流程基础上引入压缩式热泵节能技术，并利用Aspen Plus软件建立了基于压缩式热泵技术的CO₂捕集工艺流程模型。研究了压缩式热泵与机械蒸汽压缩回收（MVR）热泵、分流解吸、分布式换热、级间冷却4种节能工艺耦合，通过模拟计算与优化，结果说明了最佳节能工艺组合为“解吸塔压缩式热泵+贫液MVR热泵+分流解吸+级间冷却”耦合的CO₂捕集工艺流程，当解吸塔顶气体分流比为0.25∶0.75、冷富液分流比为0.05∶0.95、级间冷却器位于吸收塔17块塔板位置、吸收塔输入冷量为-3.0GJ/h时，系统再生能耗最低，为2.533 GJ/tCO₂，相比常规有机胺工艺（再生能耗4.204GJ/tCO₂）节能率39.748%。     

斗轮取料机液压控制系统的故障分析及改进

针对臂式斗轮取料机运行中抖动现象,进行了故障分析和排除,并对其液压原理和元件选型进行了优化及改进,为相关液压系统的故障分析和设计选型提供参考。     

两级伸缩式液压缸与四柱两级滑轨的故障分析与排除

本文通过对两级伸缩式液压缸与四柱滑轨结合使用中所出现问题及处理过程的阐述,提出了在液压系统设计与制造中,准确的原理仅仅是基础,其上还有许许多多的细枝末节需要仔细修剪,比如污染、噪声、温度、泄漏以及与机械的协调等等。     

Design of a thermal management system for a battery pack in an electric vehicle using Dymola

As the world moves toward more green and efficient means of modes of transport, electric vehicles are the most suitable and ideal choice to fulfill this requirement. Rapid developments in the field of battery technology are the main reason for their progress, but thermal management in such systems has been an area of concern for a long time. The work undertaken is to design and develop a battery management system (BMS) with a specific focus on the thermal behavior of the battery pack with varying vehicle loads as well as environmental conditions. To design an efficient BMS, one needs to model the battery behavior covering the thermal as well as electrical aspects of the battery. Apart from the battery model, a mathematical model of the electrical vehicle to mimic the various road load conditions for battery also needs to be modeled. Depending on the need for cooling based on battery behavior, the cooling circuit is modeled for the battery pack used. The entire study has been carried out using Dymola, a mathematical modeling software.     

Fault tolerant system based on non-integers order observers: Application in a heat exchanger

This work presents a fault-tolerant (FT) scheme based on the application of non-integer order observers also called fractional observers, the case of study is a double pipe countercurrent heat exchanger (HE). The aim of the FT is to detect sensors faults as soon as possible, and to provide a healthy signal in order to replace the faulty sensor signal by the fractional observer estimation. To develop the FT scheme a bank of high gain fractional order observers (HGFOO) is proposed. The Riemann-Liouville (RL) fractional derivative definition is used to solve each fractional observer. Experimental measures from a HE were used to test the performance of the fractional observers and the control scheme. The results show the robustness of the proposed observers.     

Design and control of coaxial switching system for ion cyclotron wave resonance heating system of EAST

Ion cyclotron wave resonance heating system(ICRH) which is one of the most important auxiliary system in EAST provides conditions for heating the plasma. In order to make the whole transmission network of ICRH form the required state, the coaxial switching system was developed, which mainly consists of 11 same coaxial switches. Each coaxial switch has a controller with an RS485 communication interface and is able to switch between two states. All controllers are integrated to an RS485 hub which connects to the computer. A master computer software is developed to control the coaxial switching system so as to achieve the state needs of ICRH. Moreover, several rounds of experiments show that the coaxial switching system operates reliably. The coaxial switching system has already been applied to normal use currently.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.