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.     

利用调度优化模型研究原油调合方案的可行性

针对某沿海炼油企业拟投用原油调合系统以稳定混炼原油的性质,设计方案要求将部分码头罐的操作模式由混储改为单储的情况,采用约束规划与数学规划复合建模,实现了码头至厂区大容积长输线模拟,建立了该企业的原油调度优化模型并研究了设计方案的可行性。针对240h调度周期、10种可加工原油、3艘到港油轮的复杂工况进行了优化计算。结果表明:该设计方案具备可行性,且油轮仍可实现到港即卸油,蒸馏装置混炼油种也可长时间保持稳定;模型的优化计算时间较短(小于15min),且对长输线收付及存油情况模拟准确,复合建模方法满足工业应用要求。     

三液相酶催化体系水解橄榄油的研究

采用三液相酶催化体系水解橄榄油,考察构成三液相体系的物质对酶的影响;考察了硫酸铵的含量和PEG400的含量对酶的分配分数、回收率及催化效率的影响,研究了中下相与油的体积比对水解橄榄油的影响,并初步探究了三液相体系水解橄榄油的微观液滴结构。结果表明:在酶添加量142.5 U/g、硫酸铵含量15%、PEG400含量28%、中下相与油的体积比3∶1、转速200 r/min条件下,酶的分配系数达到28.28,酶的回收率达99.65%,水解24 h后,脂肪酸含量达98.17%,在对照(油水体系)中,相同条件下产生的脂肪酸含量仅为42.06%。显微镜下液滴观察结果表明,三液相体系中独特的液滴结构可能是该体系优于油水两相体系的原因。     

油封唇口温度变化对密封性能的影响

基于流量因子统计学方法建立油封唇口的混合流体润滑模型,耦合油封的能量守恒方程及黏温方程,通过迭代求解获得油封唇口的温度分布、不同转速下油封唇口的最高温度及温差变化情况;对比分析考虑和不考虑温差情况下油封各项密封性能。结果表明:随着转速的增大,唇口最高温度线性递增,而唇口温差先增大后减小;油封工作时,唇口区域的温度先迅速升高后下降,越靠近唇尖的位置温度越高;与不考虑温度的情况比较,考虑温度变化的影响时密封区域的油膜厚度减小,油膜承载力下降,不利于密封。     

Impact of β-glucan on debittering,bioaccessibility and storage stability of skim milk fortified with shrimp oil nanoliposomes

Shrimp oil was encapsulated in nanoliposomes and fortified into skim milk. Shrimp oil nanoliposomes (SONL) were thermodynamically stable when added into skim milk at 10 mL 100 mL⁻¹. Mild bitterness in fortified skim milk caused by the SONL was masked by adding β-glucan at various levels (0.05–0.2 g 100 mL⁻¹). With the addition of SONL, fortified skim milk appeared more reddish in colour due to the presence of astaxanthin. Addition of β-glucan resulted in the increase in viscosity of the fortified milk by forming network of junction zones. During the storage of skim milk fortified with SONL and 0.1 g 100 mL⁻¹ β-glucan at 4 °C for 15 days, no major quality changes took place. Simulated in vitro digestion studies revealed that 45.41 g 100 g⁻¹ eicosapentaenoic acid (EPA) and 48.86 g 100 g⁻¹ docosahexaenoic acid (DHA) from shrimp oil were bioaccessible for absorption in the gut after digestion.     

考虑结构动态变形的缸套-活塞环润滑建模分析

缸套在燃烧冲击和活塞敲击激励下会产生接近表面粗糙度的动态变形,极有可能影响缸套-活塞环组件间的摩擦润滑过程。为了研究缸套动态变形潜在的影响,将动力学仿真获取的缸套内表面的动态变形经过处理后导入到润滑模型中,同时采用数值积分计算的方式对油液压力应力因子和剪切应力因子进行实时计算求解,使仿真计算更加符合实际情况。通过搭建同时考虑缸套变形与油液剪切特性影响的改进润滑模型,计算得到整个工作循环内活塞环上的最小油膜厚度和摩擦力曲线。结果表明:考虑缸套动态变形后的最小油膜厚度和摩擦力曲线出现了明显的波动,而且考虑缸套动态变形后的摩擦力比未考虑之前出现了明显下降。     

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.