基于改进DPC的青霉素发酵过程多模型软测量建模

青霉素发酵过程具有较强的非线性、时变性、阶段性和不确定性,基于单一的软测量模型对产物浓度进行在线估计,难以满足系统对模型精度的要求。针对上述问题,提出一种改进密度峰值聚类的多模型软测量建模方法来估计青霉素发酵过程中的产物浓度。首先,引入相似度函数代替欧氏距离计算样本点的k近邻,并且计算样本点与其k近邻之间的共享近邻,进而利用样本点的k近邻及共享近邻重新定义样本点的局部密度。其次,利用样本点之间的k近邻关系来重新定义样本点的分配策略;通过改进的聚类算法得到各聚类子集,分别建立基于最小二乘支持向量机的软测量模型。Pensim仿真平台的验证结果表明,改进的聚类算法能够更加准确地对样本数据进行聚类,从而有效提高青霉素发酵过程软测量模型的估计精度。     

污染物的微生物毒性检测方法的比较研究

根据文献调查,选择了微生物脱氢酶活性(TTC)、微生物毒性Microtox(MTX)、微生物呼吸率(BOD)、以及微生物降解动力学(DKT)等四种方法系统地比较研究了其表征污染物对微生物的毒性效应的灵敏性、准确性、可靠性和实用性。研究结果表明MTX方法能够最好地反映污染物的微生物毒性,而且操作简单、费时极少,数据的解释也最为方便。该方法可以应用于评价自然水体的毒性以及污染物生物降解所受的毒性效应。     

基于RISOMAP的非线性过程故障检测方法

化工过程监控数据存在非线性特点,且过程常常运行于多个模态,针对该类问题,提出基于相对等距离映射(relative isometric mapping, RISOMAP)的过程故障检测方法,该方法采用相对测地距离构造高维空间的距离关系阵,运用多维尺度变换(MDS)计算其低维嵌入输出,从高维数据中提取子流形信息和残差信息分别构造监控统计量进行故障检测,同时运用核ridge回归在线计算测试数据的低维输出,核矩阵通过综合相似度进行更新。数值算例和TE过程的仿真结果表明,RISOMAP方法可以更为有效地实施故障检测,故障检测的灵敏度较高,同时也为基于流形学习的多模态过程故障检测的实施提供了一条思路。     

Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles

Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins' functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.     

Genetic Elements Orchestrating Lactobacillus crispatus Glycogen Metabolism in the Vagina

Glycogen in the female lower reproductive tract is a major carbon source for colonization and acidification by common vaginal Lactobacillus species, such as Lactobacillus crispatus. Previously, we identified the amylopullulanase encoding gene pulA of Lactobacillus crispatus to correlate with the ability to autonomously utilize glycogen for growth. Here, we further characterize genetic variation and differential regulation of pulA affecting the presence of its gene product on the outer surface layer. We show that alpha-glucan degrading activity dissipates when Lactobacillus crispatus is grown on glucose, maltose and maltotriose, in agreement with carbon catabolite repression elements flanking the pulA gene. Proteome analysis of the S-layer confirmed that the amylopullulanase protein is highly abundant in an S-layer enriched fraction, but not in a strain with a defective amylopullulanase variant or in an amylopullulanase-sufficient strain grown on glucose. In addition, we provide evidence that Lactobacillus crispatus pulA mutants are relevant in vivo, as they are commonly observed in metagenome datasets of human vaginal microbial communities. Analysis of the largest publicly available dataset of 1507 human vaginal metagenomes indicates that among the 270 samples that contain a Lactobacillus crispatus pulA gene, 62 samples (23%) had a defective variant of this gene. Taken together, these results demonstrate that both environmental, as well as genetic factors explain the variation of Lactobacillus crispatus alpha-glucosidases in the vaginal environment.     

Use of the Weibull model on sizing thickeners—Part II: Methods of thickener sizing

Among several methods employed for sizing thickeners available in the literature, the Kynch, Biscaia Jr., Talmadge and Fitch, Roberts, Coe and Clevenger, and Oltmann methods use experimental data from sedimentation curves and graphical approaches. By using the Weibull distribution, it is possible to represent sedimentation curves with algebraic equations, which does not require the use of graphical approaches and provides more accuracy and speed for sizing calculations. In the present work, the main objective is the development of a set of equations for sizing continuous thickeners, for six conventional methods found in the literature, using the Weibull model. A comparative analysis of calculated and literature diameters for each graphical method presented variations between 0.73% and 8.93%. The use of the Weibull model presented the best accuracy for the Biscaia Jr. method, with a 0.73% average absolute error.     

基于微纳流控技术的流体相态特性研究进展

微纳流控技术是在微纳米尺度下研究并检测流体的作用和性质,具有可视化和快速精准等技术优势。在化工热力学研究中,近二十年来逐渐兴起了基于微纳流控技术的流体相态特性研究。详细阐述了微纳流控技术在流体相态特性领域的研究进展,重点总结了基于微纳流控技术的流体物质相态特性研究的各个领域,主要涵盖了蛋白质、聚合物、表面活性剂与盐,以及工业气体与石油天然气。其中,基于微流控技术的流体相态特性检测分析手段,成功地弥补了传统“压强-体积-温度”（pressure-volume-temperature,PVT）方法中样本体量大、传质传热慢、耗时长和高温高压高危险性等缺点,因而具有很强的实用导向性;纳流控技术则以研究纳米尺度下特有的流体相态特性为主要目标,因而具有重要的科学意义和应用价值。同时展望了微纳流控技术在流体相态特性领域研究的发展前景。     

Comparison of finite difference and control volume methods for solving differential equations

Comparisons are made between the finite difference method (FDM) and the control volume formulation (CVF). An analysis of truncation errors for the two methods is presented. Some rules-of-thumb related to the accuracy of the methods are included. It is shown that the truncation error is the same for both methods when the boundary conditions are of the Dirichlet type, the system equations are linear and represented in Cartesian coordinates. A technique to analyze the accuracy of the methods is presented. Two examples representing different physical situations are solved using the methods. The FDM failed to conserve mass for a small number of nodes when both boundary conditions include a derivative term (i.e. either a Robin or Neumann type boundary condition) whereas the CVF method did conserve mass for these cases. The FDM is more accurate than the CVF for problems with interfaces between adjacent regions. The CVF is (ΔX) order of accuracy for a Neumann type boundary condition whereas the FDM is (ΔX)² order.     

基于聚类分析和可视化的增强遗传算法—I.算法的引出、原理与分析

提出了一种基于数据可视化的聚类分析法(ClusterConstrainedMapping,CCM)和人机结合的增强遗传算法,可保证进化过程在可行域中进行,不需要任何惩罚函数参数,可有效地进行带约束问题的优化.     

Multiphase equilibria calculation by direct minimization of Gibbs free energy with a global optimization method

This paper presents a new method for multiphase equilibria calculation by direct minimization of the Gibbs free energy of multicomponent systems. The methods for multiphase equilibria calculation based on the equality of chemical potentials cannot guarantee the convergence to the correct solution since the problem is non-convex (with several local minima), and they can find only one for a given initial guess. The global optimization methods currently available are generally very expensive. A global optimization method called Tunneling, able to escape from local minima and saddle points is used here, and has shown to be able to find efficiently the global solution for all the hypothetical and real problems tested. The Tunneling method has two phases. In phase one, a local bounded optimization method is used to minimize the objective function. In phase two (tunnelization), either global optimality is ascertained, or a feasible initial estimate for a new minimization is generated. For the minimization step, a limited-memory quasi-Newton method is used. The calculation of multiphase equilibria is organized in a stepwise manner, combining phase stability analysis by minimization of the tangent plane distance function with phase splitting calculations. The problems addressed here are the vapor–liquid and liquid–liquid two-phase equilibria, three-phase vapor–liquid–liquid equilibria, and three-phase vapor–liquid–solid equilibria, for a variety of representative systems. The examples show the robustness of the proposed method even in the most difficult situations. The Tunneling method is found to be more efficient than other global optimization methods. The results showed the efficiency and reliability of the novel method for solving the multiphase equilibria and the global stability problems. Although we have used here a cubic equation of state model for Gibbs free energy, any other approach can be used, as the method is model independent.     

基于聚类选择k近邻的LLE算法及故障检测

针对化工过程在多种运行模式下多种流形结构具有不同最优近邻数问题,提出了基于聚类选择k近邻的局部线性嵌入(LLE)过程监控方法。使用LLE算法提取高维数据的低维子流形,通过局部线性回归得到高维数据空间到特征空间的映射矩阵;选择Silhouette指标作为聚类有效性指标评估嵌入空间样本信息的相似性,进而确定最优近邻数,根据映射矩阵构建故障监控统计量及其控制限,进行故障检测。最后将所提算法与其他经典算法应用于TE化工过程对比分析,验证了算法的有效性。     

一种基于改进核Fisher的故障诊断方法

针对化工过程故障数据呈非线性分布,数据类别复杂,难以进行故障诊断的问题,提出一种改进核Fisher的故障诊断方法。对于原始样本数据投影后,样本出现因类间距离存在很大差异性而导致部分类别样本存在混叠的现象,以及不同类别的边界数据归类模糊问题,给出了统一的解决办法。该方法首先采用改进类间距的方法来改变样本投影空间的分布,使得样本具有较好的投影效果,然后通过定义阈值参数来筛选出边界数据,对于边界数据,采用改进的K近邻（K-NN）算法来分类,对于非边界数据,采用马氏距离来分类。最后在TE过程中进行了仿真验证,结果表明方法在兼顾了故障诊断时间的同时,有效提高了故障诊断精度。     

Protein distance constraints predicted by neural networks and probability density functions

We predict interatomic Calpha distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distance thresholds produce the most information-rich constraints and, in turn, the optimal performance of the two methods. The predictions are based on a data set derived using a new threshold which defines when sequence similarity implies structural similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/.      

An investigation on the applicability of Parachor model for the prediction of MMP using five equations of state

Miscible gas injection is generally used as one of the most efficient methods in the enhanced oil recovery. Minimum miscibility pressure (MMP) is an important parameter in the miscible gas injection projects, since local displacement efficiency in the reservoir media is highly dependent on the MMP. Therefore, an appropriate estimation of MMP would bring significant economic benefits. This paper presents a comparative study on five representative equations of state (EoSs) for predicting MMP using Parachor model together with the criterion of zero interfacial tension (IFT) at the miscibility conditions. The predicted MMP values are compared with the experimental data obtained from the most reliable measurement technique, so called slim tube method. Such a prediction would enables us to judge the accuracy of the results obtained from different equations of state as well as the capability of Parachor model to calculate the MMP. The results of predictions obtained for five oil-gas systems in this study reveal reliable MMP values within 5% of accuracy.     

Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants

There are some computational models for fluids viscosity calculation. However, each of these models is reliable in confined density. In this comparative study two methods are evaluated for viscosity prediction in all range of density. We determine the effectiveness of each of the models and we demonstrate the strengths and weaknesses of them. Viscosity of the six refrigerants is calculated by some computational models based on Chapman⿿Enskog and Rainwater⿿Friend theories. Then a feed forward artificial neural network (ANN) with multilayer perceptrons is used to viscosity prediction and finally two methods (computational models and artificial neural network) are comparing. It is concluded that there is no opinion by computational methods to calculate viscosity from low to high density. The results show that prediction accuracy of computational models in low and moderate densities is good as ANN method. However artificial neural network has very good accuracy in high densities while computational method is defeated when the density is more than 8.     

基于距离空间统计量分析的多模态过程无监督故障检测

工业过程往往运行于多个生产模态,针对多模态过程数据的空间分布特点,提出了一种新的基于样本距离空间统计量分析的故障检测方法(DSSA)。首先用每一个样本与其训练集样本中的邻居之间的k个最近邻距离之差来表示该样本,将样本从原始变量空间映射到对应的距离空间中。然后在距离空间中通过移动窗口的方式计算各阶统计量,最后对由各阶统计量组成的统计量样本进行主元分析(PCA)。将DSSA方法、PCA方法以及另一种基于k近邻规则的多模态故障检测方法(FD-kNN)应用于TE过程中,仿真结果表明DSSA方法对多模态故障检测更为有效。     

Targeting and design of water networks for fixed flowrate and fixed contaminant load operations

Water-using processes are typically modeled as either fixed flowrate operations or fixed contaminant load operations. A new method for targeting the minimum freshwater and pinch in a single-contaminant water network is proposed, which can be applied to both kinds of operations. The method consists of plotting separate source and demand composites with flowrate as the horizontal axis and contaminant load unusually as the vertical axis. It is elegant, non-iterative, and can handle hybrid problems where both kinds of operations coexist.To design minimum freshwater networks for fixed flowrate problems, an algorithm is presented based on a newly developed principle of nearest neighbors. The principle simply states that the source streams to be chosen to satisfy a particular water demand must be the nearest available neighbors in terms of contaminant concentration.To design minimum freshwater networks for fixed contaminant load problems, the nearest neighbors algorithm is applied to process units that lie across the pinch. Units that lie entirely on one side of the pinch are satisfied by the cleanest source available on that side of the pinch. In other words, below-pinch units are satisfied by freshwater and above-pinch units are satisfied by the cleanest available stream above the pinch. Designs based on this methodology, apart from meeting the minimum freshwater target, also minimize the water flowing through the process units resulting in reduced network capital cost.     

MemDis: Predicting Disordered Regions in Transmembrane Proteins

Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.     

Estimating parameters in nonlinear rate equations

Reaction rate equations with coefficients that have an Arrhenius dependence on temperature require nonlinear procedures to obtain parameter estimates. Estimates are important, but of equal importance are their measures of plausibility. The simplest measures, in the form (estimate ± limits), are based on linear approximations, which can be, and often are, highly misleading. But there is no need to use approximations because modern statistical profiling techniques can produce accurate intervals very efficiently. Profiling also provides valuable insight into the estimation situation by revealing how models can be simplified. Strategies are given for model reformulation and parameter transformation to produce models with well-behaved estimates.     

Some modern optical methods for the diagnostics of processes in multiphase granular media

Modern optical methods (mainly, immersion tomography) for studying the fluid flow and heat and mass processes in a granular medium flooded with a liquid are described. The transparency of this composite medium is provided by an appropriate choice of the refractive index of the saturating liquid. This makes it possible to use all available visualization methods, including polarization interference microscopy, for obtaining the information on the processes proceeding in the medium. Applications of the method to different engineering problems are discussed.