蜂窝型加湿器流体动力学模拟及传热传质分析

蜂窝型加湿器是具有多用途的高效传热传质器件,仅就用于海水淡化和蒸发冷却领域的加湿器性能进行研究.利用流体体积法建立了蜂窝型加湿器的流体动力学模型(CFD),反映了稳态的三维气液两相流动的传热传质过程.在不同的空气进口参数及喷淋条件下,用该模型分别对蜂窝型加湿器在海水淡化和蒸发冷却中的流场与温度场分布进行了模拟研究.在此基础上,分析了通道中气流的含湿量分布,从而为加湿器在海水淡化和蒸发冷却不同领域中的设计计算提供了理论指导.     

神经网络设计及其电路实现

神经网络是由一系列简单的高度至连的处理单元组成的协同计算系统，利用基于模拟电路的神经网络可以进行地球物理实时反演。神经网络设计的主要步骤是针对具体问题构造计算能量函数，将能量函数极小点的求解转换成求解系统的稳定平衡点，然后构造其网络的实现电路。文章从阵列声波成像处理问题出发，设计出一种实时求解的神经网络模型，仿真试验表明其网络模型在纳秒级时间内求得问题的最优解。对于有界约束LS问题、线性规划和非线性问题的求解，文章列出了其神经网络的实现电路示意图。     

热脱附成套处理装置喷淋罐降温效率研究计算

针对热脱附成套处理装置中喷淋罐对混合气的降温冷凝过程,通过计算流体动力学方法进行气液换热过程的仿真计算以分析其降温效率。湍流模型使用标准k-ε模型,喷淋液滴使用基于欧拉—拉格朗日方法的离散相模型。仿真结果表明:喷淋罐内降温效率为69%,与设备实际工况相符合。该项研究为热脱附成套装置中喷淋罐的后续优化提供了基础模型。     

海水开架式气化器运行工况研究

阐述了应用动态模拟软件建立开架式气化器(ORV)系统动态模型,研究LNG流量、天然气管网压力、海水温度等因素扰动变化时,天然气出口温度、海水出口温度、海水进出口温差、ORV换热量、换热器平均温差等主要操作参数的动态响应趋势。分析表明,动态模拟能够准确模拟ORV主要工艺参数随相关扰动因素的变化特性,可指导设计人员的选型设计和操作人员的安全运行实践。     

神经模糊系统的储层参数反演

神经模糊系统,即把神经网络与模糊逻辑结合起来,用神经网络来构造模糊系统,使建立的储层参数反演模型既能处理输入信息,又能嵌入专家的模糊性知识,提高了模型的抗干扰能力和预测精度,同时,也克服了人工神经网络技术的储层参数反演与预测在实际应用中暴露出的一些难点.通过基于神经模糊系统的网络结构、建模步骤及应用的研究,与常规多元线性回归分析、模糊系统建模相比较,获得了较高的储层参数反演预测精度.     

基于神经模糊系统的储层参数反演

神经模糊系统,即把神经网络与模糊逻辑结合起来,用神经网络来构造模糊系统,使建立的储层参数反演模型既能处理输入信息,又能嵌入专家的模糊性知识,提高了模型的抗干扰能力和预测精度,同时,也克服了人工神经网络技术的储层参数反演与预测在实际应用中暴露出的一些难点。通过基于神经模糊系统的网络结构、建模步骤及应用的研究,与常规多元线性回归分析、模糊系统建模相比较,获得了较高的储层参数反演预测精度。     

冷却水温度对滨海电厂凝汽器水室阴极保护电位的影响

利用动电位扫描法测得极化曲线,研究了滨海电厂凝汽器Q235B碳钢水室在不同温度下的天然海水中的腐蚀行为。实验表明:冷却水温度位于20~50℃时,随着温度的升高碳钢水室管板处电位变化趋势基本一致,且腐蚀电位呈下降趋势,腐蚀电流密度变大,腐蚀速率逐渐增大,碳钢水室腐蚀越来越严重。通过仿真软件COMSOL对滨海电厂凝汽器水室阴极保护电位分布进行了数值模拟,并在室内搭建了相同的物理模型,对其施加外加电流阴极保护,待保护系统稳定后,测得凝汽器水室管板中心线处电位,结果表明:实验测得管板中线电位比模拟值要偏正些,偏差范围在3%以内,其结果与实验结果较吻合,证明所建模型的可靠性和准确性,因而可以用该方法对凝汽器水室电位分布规律进行探讨。     

PIPENET在石化装置消防水系统设计中的应用

利用PIPENET软件中的消防模块对马来RAPID项目消防水系统进行水力校核。将整个消防水系统分为水喷淋系统和消防主管网两部分,采用最远端算法,先分别对5个单元15组水喷淋系统进行计算,再将水喷淋系统与消防主管网连接成整体,对整体消防水系统进行工况校核。校核内容主要包括水喷淋系统流量、管道水流速、消防主管网接口流量及压力等参数。校核过程中通过调整管径等方式对设计内容进行优化,最终使消防水系统设计结果满足项目及国际规范要求。     

产水气井气液两相瞬变流动分析

产水气井在开、关井过程中,井筒内气水两相的流动为瞬变流动,井筒压力、温度等参数是井深和时间的函数。根据气液瞬变流动过程中所遵循的质量、动量和能量守恒定律,建立了描述气液两相瞬变流动的数学模型。该模型是一组偏微分方程,采用有限差分法对其进行求解,给出了计算井筒压力、温度和气液相速度的差分方程和详细的计算步骤。最后用具体实例模拟了产水气井开井过程中压力、温度随时间变化的情况,分析了压力、温度随时间变化的规律,证明了流动达到稳定后,瞬变模型的计算结果和稳定模型的计算结果是一致的,从而证明了模型是有效的。     

交流伺服位置控制系统的建模与仿真

分析了一种典型交流伺服位置控制系统的基本结构及特点,并将它与快速成型系统的位置控制系统进行了比较,由此将后者等效为一个二阶非线性控制系统,针对快速成型系统中位置控制系统的非线性特点,将神经网络方法引入到控制系统的数学模型建立过程,采用斜坡和抛物线两种输入对系统的数学模型进行仿真,通过比较数学模型与物理系统的输出,对影响模型精度的因素进行了分析。结果表明,采用神经网络方法对交流伺服位置控制系统建模,     

神经网络在平台桩基分析中的应用

针对海洋平台桩基模拟中存在的问题,将神经网络应用于桩基分析。在原BP算法基础上,依据桩基分析的特点,改进了BP网络,开发了适合于桩基分析的神经网络仿真系统,并给出了利用神经网络计算桩顶刚度矩阵的方法。计算实例表明,应用神经网络可以实现对桩基进行快速而准确的分析。     

水泵全特性曲线的神经网络预测模型的研究

研究水泵全特性曲线的冲经网络预测模型。通过计算机实验，讨论样本、学习算法和网络结构等对神经网络预测模型性能的影响及其改进措施。实验结果表明，神经网络预测模型具有较佳的学习能力和泛化能力。     

Development of artificial neural network models for predicting water saturation and fluid distribution

We have developed artificial neural network (ANN) models to predict water saturation from log data. Two Middle Eastern sandstone reservoirs were investigated. In the first case, an ANN model was tested on the Haradh formation in Oman using wireline logs and core Dean–Stark data. In the second case, the ANN was used to model the saturation–height function in a complex sandstone reservoir.In the first case study, the model is based on a three-layered neural network structure. The model was successfully tested yielding a prediction of water saturation with a root mean square error (RMSE) of around 0.025 (fraction of pore volume P.V.) and a correlation factor of 0.91 to the test data. Furthermore, the ANN model was shown to be superior to conventional statistical methods such as multiple linear regression, which gave a correlation factor of 0.41.In the second case, the model yielded a saturation–height function with an RMSE of 0.079 (fraction P.V.) in saturation when using core porosity and height above free water level. This is a considerable improvement over conventional methods. The error was also greatly reduced when permeability and a lithology indicator were introduced. A minimum error of 0.045 (fraction P.V.) was obtained when using core data such as height, porosity, permeability, lithology and a functional link. We then used gamma ray, neutron, density, resistivity wireline data and the cation exchange capacity as inputs. Our best case which gave an RMSE error of 0.046 (fraction P.V.) was obtained. The ANN was then used to predict the hydrocarbon saturation in the Gharif formation and good results were obtained. The neural network model proved the robustness of saturation prediction in another field for the same formation.     

The Simulation of Microbial Enhanced Oil Recovery by Using a Two-layer Perceptron Neural Network

The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been applied in modeling of microbial enhanced oil recovery since now. Finally, we are going to design a controller for the neural network. This controller is designed for the case where output of the network is oil recovery factor. For this purpose, the network is designed as a one-layer network in which just one output matches each time. In this case, a one-layer network will have acceptable results.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

The Use of ANN and the Mathematical Model for Prediction of the Main Product Yields in the Thermal Cracking of Naphtha

Thermal cracking of naphtha has such numerous reaction routes that the detailed reaction mechanism has not yet been determined. In this regard, a model of artificial neural networks (ANNs), using back propagation (BP), is developed for modeling thermal cracking of naphtha. The optimum structure of the neural network was determined by a trial-and-error method. Different structures were tried with several neurons in the hidden layer. The model investigates the influence of the coil outlet temperature, the pressure of the reactor, the steam ratio (H2O/naphtha), and the residence time on the pyrolysis product yields. A good agreement was found between model results and experimental data. A comparison between the results of the mathematical model and the designed ANN was also conducted and the relative absolute error was calculated. Performance of the ANN model was better than the mathematical model.     

有杆抽油系统故障诊断的人工神经网络方法

将人工神经网络用于有杆抽油系统故障的自动识别.对江苏油田的实测示功图数据进行了预处理,利用Matlab6.5进行编程,应用相同的数据对BP神经网络模型和自组织竞争神经网络模型的识别效率进行了对比.结果表明,由自组织竞争神经网络建立的模型对测试数据的正确识别率更高,识别效果稳定.因此,将自组织竞争神经网络应用于示功图的自动识别问题对实现有杆抽油系统故障诊断的自动化以及实现真正意义上的数字油田提供了一种有效途径.     

The Use of ANN and the Mathematical Model for Prediction of the Main Product Yields in the Thermal Cracking of Naphtha

Abstract

Thermal cracking of naphtha has such numerous reaction routes that the detailed reaction mechanism has not yet been determined. In this regard, a model of artificial neural networks (ANNs), using back propagation (BP), is developed for modeling thermal cracking of naphtha. The optimum structure of the neural network was determined by a trial-and-error method. Different structures were tried with several neurons in the hidden layer. The model investigates the influence of the coil outlet temperature, the pressure of the reactor, the steam ratio (H2O/naphtha), and the residence time on the pyrolysis product yields. A good agreement was found between model results and experimental data. A comparison between the results of the mathematical model and the designed ANN was also conducted and the relative absolute error was calculated. Performance of the ANN model was better than the mathematical model.     

基于神经网络的高压水射流冲蚀破碎预测模型的研究

在室内高压水射流冲蚀破岩试验结果的基础上，利用人工神经网络的基本特征．建立了高压水射流冲蚀破碎体积与射流压力、围压和喷距之间的数学模型，将其用人工神经网络的连接权值矩阵和节点阈值向量分布式表达出来，并应用人工神经网络进行了射流冲蚀破碎预测，预测结果与试验结果十分吻合，说明应用人工神经网络所建立的描述高压水射流冲蚀破碎体积与射流压力、围压、喷距之间关系的模型是可靠的。     

油田供水管网管径反演求解

在对油田供水管网进行系统仿真计算时 ,采用的数学模型都涉及到管网中各管段的内径、粗糙度等参数 ,这类参数的选取直接影响管网平差计算的精度。在管网运行多年后 ,上述参数大都和原始设计参数相差较大。由于实际生产过程中不允许也不可能对管网中的管道管径进行测量 ,导致管网仿真计算数学模型与实际不符 ,使得仿真计算失去本来意义。为解决此问题 ,提出了一种运用部分测试数据对管网管道内径进行反演求解的方法 ,并从数学角度证明了这种求解方法的可行性。实际算例计算结果表明 ,用该方法计算的管网平差结果与测试结果吻合