Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.     

A novel minimum weight formulation of topology optimization implemented with reanalysis approach

In this paper, we develop an efficient diagonal quadratic optimization formulation for minimum weight design problem subject to multiple constraints. A high-efficiency computational approach of topology optimization is implemented within the framework of approximate reanalysis. The key point of the formulation is the introduction of the reciprocal-type variables. The topology optimization seeking for minimum weight can be transformed as a sequence of quadratic program with separable and strictly positive definite Hessian matrix, thus can be solved by a sequential quadratic programming approach. A modified sensitivity filtering scheme is suggested to remove undesirable checkerboard patterns and mesh dependence. Several typical examples are provided to validate the presented approach. It is observed that the optimized structure can achieve lighter weight than those from the established method by the demonstrative numerical test. Considerable computational savings can be achieved without loss of accuracy of the final design for 3D structure. Moreover, the effects of multiple constraints and upper bound of the allowable compliance upon the optimized designs are investigated by numerical examples.     

轧机垂直系统动力学参数辨识

 针对轧机垂直系统动力学参数可信度不足等问题，提出一种基于实测数据的改进粒子群算法辨识轧机垂直系统动力学参数的方法。首先,将轧机垂直系统刚度和阻尼考虑成达芬振子和范德波尔振子，构建轧机垂直系统非线性动力学模型，并对粒子群算法进行改进；然后，通过数值仿真算例辨识得到系统在感染噪声和不含噪声时的动力学参数，验证了该算法的有效性；最后，以现场某轧机垂直系统为研究对象，基于现场实测数据，应用该算法进行辨识，得到了轧机垂直系统动力学参数估计，通过实测位移、速度和加速度信号分别与辨识后的位移、速度和加速度信号进行对比，证明该方法辨识轧机垂直系统动力学参数结果可靠，具有一定的工程应用价值。     

基于(火用)效率目标的环冷机余热回收系统操作参数优化

摘要：针对烧结环冷机余热回收利用率不高的难题，采用分析法建立了评价某钢铁厂烧结环冷机余热回收系统运行效率的效率模型。基于多孔介质模型、局部非热平衡方程、真实气体SRK方程建立环冷机内气固两相换热模型。通过CFD仿真模拟，探究料层高度、循环风机输入烟气温度、烧结矿底部入口风速三项可控环冷机运行工艺参数对系统效率的影响规律。结果表明，料层厚度在1～1.5 m区间每增加0.1 m，效率增加0.8%～1.1%；循环风温在100～140℃之间每增加10℃，效率增加1.4%～1.5%；烧结矿底部入口风速在0.9～1.9 m/s之间每增加0.1 m/s，效率降低0.18%～0.24%。在此基础上，基于工业运行数据建立效率正交试验优化模型，提高了该余热回收系统3.42%的效率。     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

Title加TextRank抽取关键句的情感分类研究

考虑到不同句子对判断文档情感倾向的重要程度不同，因而区分文档的关键句和细节句将有助于提高情感分类的性能。同时，考虑到Title和上下文信息，提出了一种基于Title和加权TextRank抽取关键句的情感分析方法SKTT，实现了高效的情感分析。根据文档Title的情感权重计算Title贡献度，考虑到标点和语义规则对情感倾向的影响；根据加权TextRank算法思想，在文档正文中构建了一个情感句有向图来提取关键句；计算所有关键句的情感倾向进行情感分类。在4个领域上进行实验，实验结果表明，该SKTT方法性能明显优于Baseline，具有高效性。     

基于协同优化算法的流速仪检定系统设计

提出了一种小型轻便的流速仪检定系统的设计方案,并运用改进的多学科协同优化方法求解各学科设计变量的最优解。首先介绍了算法思想及改进措施,并根据系统设计要求建立了各学科的设计变量、目标函数和约束条件；其次,运用遗传算法求解各学科设计变量的最优解,并根据优化结果完善了系统的设计方案,同时运用计算机仿真等方法验证了优化结果的可行性。结果表明,优化后的检定系统满足流体力学效应、匀速运动时间、系统重量等设计要求,同时证明了协同优化算法解决多学科设计优化问题的有效性。     

A hierarchical simple particle swarm optimization with mean dimensional information

To reduce the negative influence of the overemphasis of gbest the dimensional information of particle is introduced to be a new example. This additional information source is incorporated into simple PSO to establish a simpler position model. Another two simpler position updating models, cognition only model and social only model, based on the simple PSO algorithm are presented as well. Time hierarchy strategy is extended from probability hierarchy, both aiming to make full use of advantages of three models. Three models are used with time or probability hierarchy to update each particle’s position. Thus, two proposed algorithms THSPSO and PHSPSO are finally obtained. Experiments are conducted on fifteen benchmark functions. The results demonstrate the two proposed algorithms both have excellent performances for basic functions compared with other popular PSO variants. Probability hierarchy strategy is more effective than time hierarchy strategy in general.     

New version of continuous–discrete cubature Kalman filtering for nonlinear continuous–discrete systems

For nonlinear continuous–discrete systems, this paper elaborates a new accurate implementation of continuous–discrete cubature Kalman filter (CD-CKF). As the main contribution of this work, the new Kalman prediction stage begins by integrating the nonlinear continuous model for all the cubature sample vectors; the prior estimate state and covariance prediction are based on the weighted statistics of these integrated cubature sample vectors and the Gauss–Legendre approximation scheme. The new square root form CD-CKF is also derived and accurately implemented by combining with the modified variable stepsize NIRK. As the advantages of proposed approach, the complicated and error-prone processes of solving covariance differential equation or calculating derivatives are avoided, while the positive semi-definiteness of prior error covariance are numerically guaranteed. Simulations of traffic control scenarios further confirm the new approach’s superior filtering performance in both reliability and accuracy.     

能源土边界面模型的应力更新算法及算例分析

水合物的存在会显著影响能源土的刚度、峰值强度与剪胀性。针对已有能源土模型的不足，结合边界面模型的建模思想，构建一个新的能源土边界面模型，模型参数较少，能够恰当反映能源土的应力-应变关系。计算能源土变形问题的核心在于正确积分塑性本构方程，应用完全隐式回退Euler算法，建立模型应力及塑性内变量的更新公式，并给出显式的一致性切线模量表达式。基于ABAQUS软件提供的二次开发接口，编写模型的用户材料子程序，应用已有试验数据验证程序正确性。最后应用开发的子程序对能源土的平面应变试验进行模拟，分析水合物饱和度对剪切带倾角与孔隙比的影响。