与机理杂交的支持向量机为发酵过程建模

针对生物发酵过程机理复杂、高度非线性的特点,采用基于结构风险最小的支持向量机为发酵过程建模,其算法规范,建模复杂度低于神经网络方法,所建模型的预测效果更好.还将生化过程的动力学机理与支持向量机相结合,采用串联和串并联结构,提出与机理杂交的支持向量机建模方法,并为间歇式酒精发酵过程中酵母菌体浓度变化建立了预测模型.原理分析与试验结果表明与机理杂交的支持向量机建模方法,相比于单一近似的动力学模型、单一的支持向量机模型,以及机理杂交的神经网络模型,它的预测精度高,泛化能力强,性能更为优越.     

A fuzzy diagnosis approach using dynamic fault trees

By incorporating digraph models, fault trees and fuzzy inference mechanisms in a unified framework, a novel approach for fault diagnosis is developed in this work. To relieve the on-line computation load, the fault origins considered in diagnosis are limited to the basic events in the cut sets of a given fault tree. The symptom occurrence order associated with each root cause is derived from system digraph with the qualitative simulation techniques. The implied candidate patterns are enumerated according to two proposed theorems and then encoded in the inference system with IF-THEN rules. The simulation results show that the proposed approach is not only feasible but also capable of identifying the most likely cause(s) of a hazardous event at the earliest possible time.     

基于PLS-LSSVM的谷氨酸发酵产物浓度预测建模

针对谷氨酸发酵过程关键生化参数难以在线检测给发酵优化控制带来困难问题,基于谷氨酸5 L发酵罐发酵过程,建立基于偏最小二乘（PLS）和最小二乘向量机（LSSVM）相结合的谷氨酸浓度预测模型;利用PLS对输入变量进行特征提取降低维数和消除相关性,以简化模型和提高模型精度。为确定谷氨酸发酵最佳预测模型,简化后的预测模型与发酵动力学模型进行比较;实验结果表明,简化后的耦合模拟退火（coupled simulated annealing,CSA）对参数进行优化的LSSVM模型具有最好预测性能,相对PLS预测模型和发酵动力学模型具有明显优势,均方根误差分别为1.597、8.49和2.934,可以为谷氨酸发酵过程操作及时调整及优化控制提供有效指导。     

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems. Individual SVM is used to approximate each output of the controlled plant. Then the model is used in MPC control scheme to predict the outputs of the controlled plant. The optimal control sequence is calculated using GA with elite preserve strategy. Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.     

Hybrid modeling of penicillin fermentation process based on least square support vector machine

According to the problem of the pre-estimation with least square support vector machine (LSSVM) modeling is not ideal in the initial stages of penicillin fermentation process, two hybrid models are designed by utilizing the advantage of LSSVM and kinetics model. Through selecting the appropriate state variables and adopting these methods for penicillin fermentation, the mycelial concentration can be pre-estimated. Experiment results show that these hybrid modeling methods not only improve the above problem, but also have higher predicting accuracy and more powerful generalization ability than the single LSSVM method.     

A fuzzy-logic based fault diagnosis strategy for process control loops

By considering the fault propagation behaviors in process systems with control loops, a fuzzy-logic based fault diagnosis strategy has been developed in the present work. The proposed fault diagnosis methods can be implemented in two stages. In the off-line preparation stage, the fault origins of a system hazard are identified by determining the minimal cut sets of the corresponding fault tree. The fault propagation patterns in a feedback loop are obtained on the basis of system digraph. The occurrence order of observable symptoms caused by each fault origin is derived accordingly and then encoded into a set of IF-THEN diagnosis rules. In the next on-line diagnosis stage, the occurrence indices of the top event and also the fault origins are computed in a fuzzy inference system based on real-time measurement data. Simulation studies have been carried out to demonstrate the feasibility of the proposed approach.     

基于M估计器的支持向量机算法及其应用

训练样本的准确性对回归分析模型有很大的影响,然而训练样本中难免会出现一些造成分析模型失效的奇异点。 为克服奇异点对回归模型的影响,本文提出了一种基于M估计器的支持向量机（M-SVM）。它采用M估计器的目标函数代替最小二乘支持向量机（LS-SVM）目标函数中的残差平方和,同时提出了M-SVM的迭代求解算法,并将该算法应用于含有奇异点的低维仿真数据回归和汽油近红外光谱定量分析中。实验结果证明,相比于其他的支持向量机,M-SVM具有更好的稳健性和分析精度。     

基于支持向量机的软测量建模方法的应用

利用基于最小二乘支持向量机(LS-SVM)的软测量建模方法,通过工业现场数据来对丁二烯精馏装置建立软测量模型.对于该软测量模型,支持向量机方法比BP神经网络方法具有更好的泛化能力.研究结果表明,基于最小二乘的支持向量机建模方法是一种有效的软测量建模方法.     

Modeling pressure drop coefficient for cyclone separators: A support vector machine approach

As one of performance critics for cyclone separators, pressure drop is an important parameter to evaluate and design cyclone separators. In order to accurately predict the complexly nonlinear relationships between pressure drop coefficient (PDC) and geometrical dimensions, a support vector machine (SVM) model is developed and employed to model PDC for cyclone separators. Based on the normalization method and the random sampling technique for the experimental sample dataset, a dynamically optimized search technique with cross validation is introduced to determine optimal algorithm parameters in the model. Then the optimized SVM model is trained and tested by the simulation results. According to the predicted accuracy of PDC for cyclone separators, the SVM model performance is compared and evaluated. It is found that the SVM model provides the higher generalization performance than the conventional models including the theoretical and statistical models as well as the artificial neural network model, with the mean squared error of 3.64×10⁻⁴ and the correlation coefficient of 0.9974. The result also demonstrates that SVM can offer an alternative and powerful approach to model cyclone pressure drop.     

基于支持向量机的化工装置电力电子故障诊断

采用小波包分析与支持向量机（SVM）对化工装置电力电子故障进行自动识别和诊断,运用变尺度分辨小波包方法对电力电子故障信号进行特征处理。支持向量机能够对小样本数进行模式识别,并且具有良好的分类推广能力。在小波包分析特征基础上,采用分布式多支持向量机（SVM）分类器识别化工装置电力电子故障。结果表明：该方法能准确有效地对化工装置的电力电子故障进行识别和诊断。     

A fault magnitude based strategy for effective fault classification

A common approach in fault diagnosis is monitoring the deviations of measured variables from the values at normal operations to identify the root causes of faults. When the number of conceivable faults is larger than that of predictive variables, conventional approaches can yield ambiguous diagnosis results including multiple fault candidates. To address the issue, this work proposes a fault magnitude based strategy. Signed digraph is first used to identify qualitative relationships between process variables and faults. Empirical models for predicting process variables under assumed faults are then constructed with support vector regression (SVR). Fault magnitude data are projected onto principal components subspace, and the mapping from scores to fault magnitudes is learned via SVR. This model can estimate fault magnitudes and discriminate a true fault among multiple candidates when different fault magnitudes yield distinguishable responses in the monitored variables. The efficacy of the proposed approach is illustrated on an actuator benchmark problem.     

混沌最小二乘支持向量机及其在发酵过程建模中的应用

针对青霉素发酵过程的参数检测存在不确定因素,提出一种基于混沌最小二乘支持向量机的青霉素浓度预测方案。采用混沌优化算法对最小二乘支持向量机参数进行寻优,建立了一种混沌最小二乘支持向量机模型。首先,利用该模型对两种常规非线性函数曲线进行了仿真回归,结果表明,算法具有良好的建模精度;其次,基于Pensim仿真平台,运用文中方法预测青霉素发酵过程的产物量,实验仿真表明混沌优化算法具有良好的全局优化性能,在参数选择中可以有效避免陷入局部最小值,基于混沌优化的最小二乘支持向量机具有较高的建模精度。     

基于极深因子分解机的化工过程故障诊断方法

近年来,随着化学工艺、设备的复杂化和大型化程度不断深入,如何为化工企业及时、准确地诊断故障、排除事故,成为一个极具挑战性的问题。目前,以深度学习为代表的化工过程故障检测与诊断技术成为业界解决问题的主要思路之一,但现有深度方法在构建诊断模型时只关注了变量的非线性高阶特征,不能充分、全面地挖掘多源交互信息,难以有效地融合各类异构数据。基于此,提出一种基于极深因子分解机的化工过程故障诊断方法,通过并行融合三类不同网络模型(分解机模型、深度神经网络模型、压缩交互网络模型),实现对高阶、低阶及线性特征的自动提取和高效整合。为了评估模型性能,从单故障诊断和多故障混合诊断的角度出发,在田纳西-伊斯曼过程(TE)仿真数据集上进行了广泛的对比实验,结果表明,所提方法较以往故障诊断方法在精确率和召回率等指标上具有明显优势。     

基于向量投影的代谢支持向量机乙烯精馏产品质量软测量建模

针对支持向量机(SVM)增量学习过程中易出现计算速度慢、稳定性差的缺陷,提出了一种基于向量投影的代谢支持向量机建模方法.该方法首先运用向量投影算法对训练样本进行预选取来减少样本数量,提高SVM建模速度.然后将新增样本"代谢"原则引入SVM增量学习过程中,以解决因新增样本不断加入而导致训练样本数量"爆炸"的问题.最后将该方法用于乙烯精馏产品质量软测量建模,实验结果表明,与传统SVM和最小二乘支持向量机(LSSVM)相比,向量投影的代谢SVM具有更好的预测结果.     

基于最小二乘支持向量回归机的光管污垢特性预测

搭建了污垢实验系统以测得管壁温度和出、入口温度等参数,并将这些参数作为模型的输入变量,以污垢热阻值作为模型的输出变量,利用最小二乘支持向量回归机搭建了污垢预测模型,对光管的污垢特性进行了预测。一方面,通过与测量结果相比较,验证所搭建的模型是合理可行的;另一方面,通过对多次预测结果分析比较得出,该模型不但适用于流速、水浴温度、材质等参数为定值的情况,而且当这些参数发生改变时,该模型也是适用的。     

A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process

In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C-SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results. © 2018 American Institute of Chemical Engineers AIChE J, 65: 992–1005, 2019     

用于发酵过程在线建模的自适应局部最小二乘支持向量机回归方法

提出一种基于自适应局部学习的最小二乘支持向量机回归（LSSVR）在线建模方法。考虑样本间的距离和角度信息以获得更全面合理的相似样本集,推导了采用快速留一法在线优化模型参数的准则,并给出了发酵过程在线自适应模型选择的策略。以链激酶流加发酵过程为例,验证了所提出算法能够从过程的第2批次开始,同时对活性菌体浓度和链激酶浓度进行较准确的在线预报,较普通的局部LSSVR等建模方法具有更高的预报精度和自适应性。     

A novel analysis of color component for top dyed melange yarn with support vector machine

In view of the difficulty of distinguishing the color component in top dyed melange yarn due to the spectral overlap of the component colors, a novel color component analysis method based on support vector machine is presented. With this method, spectra data can be distinguished more accurately and effectively than with the traditional method—human‐eye detection—and therefore, the method will be very helpful for accurate color matching. In our work, the core idea was to convert the overlapped spectra data into linearly separable ones in a high dimension space, followed by recognition and determination of the composition of melange yarn by trained support vector machine classifier. The effects of four kernel functions, i.e., linear, radial basis kernel, sigmoid, and polynomial, as well as five spectral preprocessing methods, including amplification, first derivative, second derivative, principal components analysis, and L*a*b* values were studied. The results demonstrated that with the amplification factor of 100 of reflectance spectra coupled with L*a*b* as input data, and using radial basis kernel as kernel function, the highest recognition rate was achieved, with an average recognition rate of eight colors of 96.5%, indicating that it was a better color component analysis method for top dyed melange yarn. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 636–641, 2016