立式热虹吸再沸器设计及核算软件开发

在查阅和分析国内外大量立式热虹吸再沸器资料的基础上，传承再沸器的各种设计方法，并用具体的工业生产实例进行考核。最终，编制出通用于各种工艺工况，便于进行立式热虹吸再沸器设计的软件，为编制开发出统一应用于各种工艺条件下热虹吸再沸器的设计软件包成为可能。     

立式热虹吸再沸器的设计与校核

立式热虹吸再沸器在石化行业中应用十分广泛,但其计算十分复杂。介绍了立式热虹吸再沸器的设计方法,以实例详细介绍了其计算步骤和校核过程,并分析了设计过程中应考虑的一些问题。     

Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler

The hydrodynamics and heat transfer in a thermosiphon reboiler interact with each other making the process very complex. Prediction of the rates of heat transfer and thermally induced flow are the primary requirements for the design of thermosiphon reboilers. The objective of this study was to develop, for the first time, a unified data-driven model, for the prediction of circulation rate in a thermosiphon reboiler for different pure components with wide variation in thermo-physical properties and operating parameters, using support vector regression (SVR)-based modeling technique. In the present work, 148 experimental data points from accessible sources, including the author's own study were used. First, a multiple regression (MR) model for circulation rate (in the form of Reynolds number) was developed as a function of dimensionless parameters namely, Peclet number for boiling (Pe_b), Subcooling number (K_sub), and the Lockhart–Martinelli parameter (X_tt), followed by the formulation of an SVR-based model. Statistical analysis revealed that the proposed generalized SVR-based model had high prediction accuracy with an average absolute relative error (AARE) of 3.82%, root mean square error (RMSE) of 0.0717, leave-one-out cross validation (Q²_LOO) of 0.9975 and mean relative error (MRE) of 0.0288 on the training data. Corresponding values of 6.11% AARE, 0.0816 RMSE, 0.9991 leave-one-out cross validation on test data (Q²_ext) and 0.0541 MRE were obtained for the test data. A comparison of the SVR-based correlation was made with the MR model and with some selected empirical correlations in the literature. It was observed that the proposed SVR-based model significantly exhibited an enhanced prediction and generalization performance.     

热管中添加纳米颗粒

随着纳米技术的飞速发展,研究者逐渐把这一高新技术应用于热能动力这一传统领域.提出了一种新的提高传热的方法——在以水为工作液体的两相闭式热虹吸管中添加一定数量的纳米颗粒.在理论和实验中研究了这种热虹吸管蒸发段的工作特性,结果表明,在实验范围内,与普通热管相比较,这种新式热管具有很好的启动特性,低的管壁温度,传热系数提高了47%～96％,轴相热流率提高了7.6%～15％.这种新的方法简单而且容易应用于化工生产中.     

Use of Wire Matrix Inserts in Thermosiphon Reboilers in the Lower Operating Range

Turbulators are applied to increase the thermal efficiency of heat transfer units. The additional pressure drop can be challenging for the self-circulation of thermosiphon reboilers. Thus, the effects of hiTRAN®-wire matrix inserts for the boiling of water and a water/glycerol mixture were investigated herein, using the performance of a thermosiphon reboiler with plain tubes as a reference. The reboiler was operated at sub-atmospheric pressures, small driving temperature differences, and under flooded conditions. Favorable and unfavorable operating conditions for using inserts were specified. Especially for the water/glycerol mixture, significant improvements of self-circulation and heat transfer up to six times compared to the plain tube reference were observed, allowing an operation of thermosiphon reboilers at smaller driving temperature differences under sub-atmospheric pressures.     

垂直窄矩形通道内流动沸腾传热研究

在热虹吸条件下，实验研究了缝宽接近于气泡脱离直径型的窄矩形通道内流动沸腾的传热特性。发现其对流蒸发传热中有时处于过渡流状态。首次为对流蒸发传热系数建立了一个通用算式。还将该算式与加和模型相结合，为窄矩形通道形成了第一个完整的流动沸腾传热算法。该算法的预测值与实验数据相比，其平均绝对偏差为１４．９％。     

盐酸汽提塔再沸器改造

针对盐酸汽提塔管壳式石墨立式热虹吸再沸器在运行中出现的问题,结合圆块孔换热器的特点,应用新型分体式圆块孔石墨再沸器替代原管壳式石墨再沸器。     

一种特殊的液液热交换设备——螺旋板热虹吸式重油冷却器

螺旋板热吸虹式重油冷却器是一种与热管的工作原理相同，可控制壁面温度的新型换热器。它克服了普通间壁式冷油器因壁面温度低而形成粘油绝热层，冷却效果差的缺点，又比热管式换热器的结构更适合于容积流量较小的液液热交换场合。     

Bager/UOP苯乙烯装置脱氢系统瓶颈分析及改造

针对影响苯乙烯装置脱氢单元长周期运行的瓶颈,对SMART反应器氧气消耗量高进行热量核算,对HS-219、TT-203操作参数进行分析,找出了影响长周期运行的原因,指出了MS-241热虹吸管线设计上的缺陷。     

热虹吸式重沸器循环回路的设计探讨

探讨热虹吸式重沸器循环回路设计,包括热虹吸式重沸器的分类及特点、塔釜结构型式、塔釜液位确定、塔釜至重沸器的降液管及重沸器至塔釜的升气管尺寸计算等。     

Circulation rates of boiling liquids in a vertical tube

An experimental study of heat transfer to boiling liquids under natural convective flow has been carried out in a single tube vertical thermosiphon reboiler to investigate the effect of heat flux and submergence on circulation rates. The test liquids used were distilled water, various concentrations of propan-2-ol in water and their azeotrope. The test section was an electrically heated stainless steel tube of 25.56 mm i.d., 28.85 mm o.d. and 1900 mm long with twenty-one spot welded copper constantan thermocouples to measure the variations in wall temperature along its axis. The uniform heat fluxes in the range of 3.5–42.4 kW/m² were employed while inlet liquid sub-cooling varied from 0.2 to 30.7 °C. The liquid submergence levels were maintained in the range 30–100%. The typical experimental data has been graphically presented and discussed. An empirical correlation has been developed from the experimental data of the present study through regression analysis.     

过程系统变负荷下的数据校正与参数估计方法

过程系统的数据校正与参数估计是进行实时操作优化与过程控制的基础。过程系统变负荷下由于模型参数变化的非线性及显著误差的影响,导致数据校正与参数估计的结果不准确,从而影响实时操作优化与过程控制的效率。针对此问题,本文提出了一种用于变负荷下的数据校正与参数估计方法。此方法主要包括过程的稳态检测与数据采样,多工况下的数据聚类和基于多组测量的数据校正与参数估计。首先选择有效和可靠的过程测量数据,根据变负荷下工况的波动性与系统的非线性特征进行数据聚类,最后基于聚类结果调整模型参数使得模型输出与过程测量数据偏差最小。此方法可有效地减小模型参数变化的非线性及显著误差对数据校正与参数估计结果的影响。基于现场的测量数据,将此方法应用于空气分离流程系统中,结果显示了基于此方法的数据校正与参数估计结果更准确。     

基于非负矩阵分解的多模态过程故障监测方法

针对传统的多元统计故障监测方法往往需要假设测量数据服从单一高斯分布的不足,提出了一种基于非负矩阵分解(NMF)的多模态故障监测方法。首先使用标准的NMF算法对训练集数据进行聚类,将多模态数据划分到各个模态中;然后使用稀疏性正交非负矩阵分解(SONMF)算法对各模态分别建模,同时构造监控统计量进行故障监测。将提出的基于非负矩阵分解的多模态故障监测方法应用于数值例子和TE过程的仿真结果表明,该方法能够及时有效地检测出多模态过程中的故障。     

一种改进的动态数据校正离群值检测法

Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation （DDR） is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor （CSTR） process verifies the effectiveness of the proposed algorithm.     

Tensor sequence component analysis for fault detection in dynamic process

For dynamic processes, using sequence information to augment the data can improve fault detection performance. Traditional approaches transform raw data into augmented vectors, which leads to losses in structural information in the variables and increases the data dimension. This paper proposes a novel data dimension reduction algorithm called tensor sequence component analysis (TSCA) and applies it to dynamic process fault detection. The algorithm extends each sample into a matrix comprising current and past process data, and simultaneously reduces the dimensions of time delay and the variables for feature extraction, solving the problem of the curse of dimensionality. For the dimension reduction of time delay, in order to extract similar information from the samples, each sample is reconstructed with time neighbourhoods. For the dimension reduction of the variables, considering the information of different variables variance information of the latent variables is maximized for feature extraction. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the efficacy of the proposed method.     

THE DETECTION OF A SINGLE ADDITIVE OUTLIER OF UNKNOWN POSITION

Abstract. Kudo (On the testing of outlying observations. Sankhya 17 (1956), 67–73) has derived an optimal invariant detector of a single additive outlier of unknown position in the context of an underlying Gaussian process consisting of independent and identically distributed random variables. We show how this author's arguments can be extended to derive an invariant detector of an additive outlier of unknown position for an underlying zero-mean Gaussian stochastic process. This invariant detector depends on the parameters of this process; its properties are analysed further for the particular case of an underlying zero-mean Gaussian AR( p ) process. It provides an upper bound on the performance of any invariant detector based solely on the data and it may be 'bootstrapped' to provide an invariant detector based solely on the data. A plausibility argument is presented in favour of the proposition that the bootstrapped detector is nearly optimal for sufficiently large data length n. The truth of this proposition has been confirmed by simulation results for zero-mean Gaussian AR(1) and AR(2) processes (for certain sets of possible outlier positions). The bootstrapped detector is shown to be closely related to the detector based on the approximate likelihood ratio criteria of Fox (Outliers in time series. J. Roy. Statist. Soc. Ser. B 34 (1972), 350–63) and the leave-one-out diagnostic of Bruce and Martin (Leave- k -out diagnostics in time series. J. Roy. Statist. Soc. Ser B 51 (1989), 363–424). It is also shown how the case of an underlying Gaussian process with arbitrary mean can be reduced to the case of an underlying zero-mean Gaussian process.     

冷却水参数对钠钾合金热管传热性能影响

冷却水参数对钠钾合金热管传热性能有重要影响,通过改变不同冷却水流量和冷却水温度,研究了冷却水参数对钠钾合金热管传热性能的影响规律。实验结果表明,钠钾合金热管运行于较低冷却水流量（4~18 ml·s^-1）的冷却条件时,流量对热管冷凝段外壁面的温度影响很大,而当热管运行于较高冷却水流量的冷却条件时,冷却水流量对热管外壁面温度影响较小。整体而言,增大冷却水流量可以有效地提高钠钾合金热管的传热量及其传热性能。当热管运行于较大冷却水流量的冷却条件时,冷却水温度的变化对热管传热性能影响较小。     

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively, this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization (ONMF) and hidden Markov model (HMM). The new clustering technique ONMF is employed to separate data fromdifferent processmodes. ThemultipleHMMs for various operating modes lead to highermodeling accuracy. The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can bewell interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.     

Detection,Diagnosis and Root Cause Analysis of Sheet‐Break in a Pulp and Paper Mill with Economic Impact Analysis

Sheet‐break is a long standing problem in the pulp and paper industry. This study is concerned with the analysis of process data to diagnose causes of sheet‐breaks and therefore significant down times. PCA was used to model the process and a combined index based on the Hotelling's T² and Squared Prediction Error (SPE) was developed as a sheet‐break detection indicator. As the process is subject to external disturbances, changes and frequent interruptions, pre‐processing of the data played an important role in getting consistent results. We used several novel techniques for data selection, scaling and modelling. The models were validated using a large validation data set with known fault conditions. The developed model, data visualization tool and engineering judgement was used for off‐line diagnosis of root causes of sheet‐breaks. Several operational changes were recommended and implemented on the process resulting in significantly reduced sheet‐breaks. Key Performance Indicators calculated before and after the changes shows the significant economic gain as a result of this 'data‐mining' project.