基于非线性偏最小二乘回归的软测量建模及应用

针对复杂工业过程存在的多变量、相关性和非线性问题,提出一种新的基于非线性偏最小二乘(partial least squares,PLS)回归的软测量建模方法。该方法利用PLS作为模型的外部框架来提取输入输出主成分变量,同时消除变量间的相关性,然后用最小二乘支持向量机(least squares support vector machine,LSSVM)作为内部函数来描述主成分变量之间的非线性关系,并引入基于误差最小化的权值更新策略,来改进模型的预测精度。以pH中和过程的Benchmark模型来验证该方法的性能,并与其他建模方法比较,结果表明该方法预测精度较高,而且具有较强的泛化能力。将该方法应用于某电站燃煤锅炉的NOx排放软测量建模之中,取得了较好的预测效果。     

基于PSO-SVR算法的燃煤锅炉结渣特性预测

针对燃煤锅炉结渣特性的有限样本、非线性和高维数问题,提出了一种基于粒子群优化（PSO）和支持向量回归（SVR）的预测模型。对于支持向量回归机在建模中存在的参数选取问题,采用改进的粒子群算法（PSO）对模型参数进行优化,该方法结合了PSO的快速全局优化能力和SVR的结构风险最小化理论,精确地逼近非线性映射关系的能力。仿真结果表明：相比遗传算法（GA）SVR预测模型和模拟退火（SA）SVR预测模型,PSO-SVR模型预测燃煤锅炉结渣特性具有较高的准确率。     

不同建模方法在茶汤中多酚含量近红外检测中的应用

针对茶汤中多酚类物质的近红外快速测定,我们采用2 mm光程采集了100例普洱茶茶汤的光谱,经过归一化预处理后建立了快速检测模型,并考察了3种不同建模方法的检测性能及效果差异。这三种方法分别是偏最小二乘回归、支持向量机和径向基神经网络。研究结果表明,偏最小二乘回归和径向基神经网络都取得令人满意的结果,且模型参数调节简便易行;支持向量机的检测结果误差偏大。     

基于LS-SVM的流向变换催化燃烧反应器的温度预测

用最小二乘支持向量机建模方法对神经网络建立的流向变换催化燃烧反应器拟定态温度分布的模型进行改进,克服了神经网络局部最小和过拟合的问题,同时最小二乘支持向量机在训练过程中所需的训练样本比神经网络大大减少,使得试验成本大大降低,工业化进程加快。仿真结果表明支持向量机建立的模型简单,精度高,满足建模的精度要求,比神经网络模型耗时少。     

基于偏最小二乘支持向量机的燃煤电站锅炉效率模型

针对电站锅炉燃烧系统非线性强、变量间强耦舍及信号噪声大等特点,提出了基于电站历史运行数据的锅炉效率建模方法。根据锅炉燃烧的机理选取关键输入变量,利用偏最小二乘原理(PLS)对其进行特征提取,建立锅炉效率与所提取特征之间的最小二乘支持向量机(LSSVM)关系模型,组成一个PLS-LSSVM混合模型,并利用电站实际数据对模型的准确性进行验证。结果表明:PLS-LSSVM模型相比于PLS模型具有更强的泛化能力,相比于LSSVM模型有更好的运行效率。     

复合PLS模型在近红外光谱分析煤炭中的应用

为了更好地确定偏最小二乘法模型的主成分数,提出一种传统偏最小二乘法和多主成分数偏最小二乘法相结合构建复合偏最小二乘模型的方法。给出了预测时两种样品相似度的计算方式:直接距离法和性质得分距离法。分别采用复合偏最小二乘法和传统偏最小二乘法对煤炭的全硫、灰分、热值和碳含量进行建模预测,比较传统偏最小二乘法和多主成分数偏最小二乘法建模过程中的相关系数和交互验证均方根误差,采用复合偏最小二乘模型对验证集样品预测时,计算了不同相似度计算方式下不同样品间距离算法的预测均方根误差,并同传统偏最小二乘法预测均方根的误差进行比较,结果表明:复合偏最小二乘法建模比传统偏最小二乘法建模有更强的适应性,能够提高预测的准确性。     

Compound PLS Model in Coal Analysis with Near-infrared Spectra

为了更好地确定偏最小二乘法模型的主成分数,提出一种传统偏最小二乘法和多主成分数偏最小二乘法相结合构建复合偏最小二乘模型的方法.给出了预测时两种样品相似度的计算方式:直接距离法和性质得分距离法.分别采用复合偏最小二乘法和传统偏最小二乘法对煤炭的全硫、灰分、热值和碳含量进行建模预测,比较传统偏最小二乘法和多主成分数偏最小二乘法建模过程中的相关系数和交互验证均方根误差,采用复合偏最小二乘模型对验证集样品预测时,计算了不同相似度计算方式下不同样品间距离算法的预测均方根误差,并同传统偏最小二乘法预测均方根的误差进行比较,结果表明:复合偏最小二乘法建模比传统偏最小二乘法建模有更强的适应性,能够提高预测的准确性.     

弹药性能试验回归方法优选

论述了弹药性能试验回归建模，普遍存在自变量的多重相关性，为克服自变量多重要关性的缺点，选取较好的回归方法是关键的技术途径，实践表明，偏最小二乘回归是一种可以采用的方法。     

基于聚类分析与粒子群算法优化神经网络的灰熔点预测

针对解决燃煤锅炉或气化炉的结渣现象，影响锅炉安全性问题，以灰成分金属氧化物为自变量，灰熔点流动温度为因变量，建立了K-Means-PSO-BPNN的灰熔点预测模型，误差分析结果表明，经过粒子群算法优化，BP神经网络模型在聚类分析后的预测效果得到了显著提高，表现出更好的相关性，相关系数为0.967,高于未优化的0.917,平均绝对误差为5.81,小于未聚类的26.98,并且模型的准确性提高到98.89%。因此，聚类分析以及粒子群算法优化后的神经网络模型能够更准确预测煤灰的流动温度(FT)。     

非线性偏最小二乘法及可视化火焰检测系统的燃煤锅炉NOx排放特性的研究

为了研究燃煤电站锅炉NOx排放特性,利用可视化火焰检测系统对燃煤锅炉燃烧火焰和温度场进行了测试研究。通过测量,由双色测温法计算得到了炉内温度场,采用数字图像处理技术提取了火焰图像特征参数,进而借助非线性偏最小二乘法建立了由炉内燃烧温度场及火焰图像特征参数来预测NOx排放量的模型。结果表明,所建模型预测值与实测值具有一致性,从而为燃煤锅炉通过燃烧调整,以降低NOx排放和提高锅炉效率提供了有效手段。     

变负荷工况下NOx排放量预测控制

NO_x是火电厂排放的主要污染物之一,降低NO_x的排放是火电厂面临的主要问题。针对火电厂变负荷工况下的NO_x排放量最小化问题,本文提出了一种基于最小二乘支持向量机（LSSVM）的非线性模型预测控制算法。根据电站锅炉实际历史数据建立锅炉负荷预测模型和NO_x排放预测模型,并以交叉验证的方法优化模型参数,从而获得高精度模型。在此基础上以NO_x的排放量最小为优化目标,考虑锅炉负荷约束,构建锅炉燃烧优化模型。采用差分进化算法求解优化模型得到控制参数的最优设定值。为了验证本文提出算法的有效性,采用实际生产数据进行实验。实验结果表明本方法能够在变负荷工况下有效降低NO_x排放量,在不增加电厂改造成本上,为电厂提供了有效的控制手段,具有一定应用前景。     

A data-derived soft-sensor method for monitoring effluent total phosphorus

The effluent total phosphorus(ETP)is an important parameter to evaluate the performance of wastewater treatment process(WWTP).In this study,a novel method,using a data-derived soft-sensor method,is proposed to obtain the reliable values of ETP online.First,a partial least square(PLS)method is introduced to select the related secondary variables of ETP based on the experimental data.Second,a radial basis function neural network (RBFNN)is developed to identify the relationship between the related secondary variables and ETP.This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP.Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.     

A deep graph convolutional network model of NOx emission prediction for coal-fired boiler

To deal with environmental problems caused by NOx production in thermal plants, it is imperative to establish a reliable model to predict NOx concentration in the combustion process. NOx formation in a coal-fired boiler is complex, and many variables affect NOx emissions. The effective information fusion of these variables can improve the accuracy of NOx concentration prediction. However, the existing NOx prediction algorithms based on thermal parameters rarely consider the mechanical knowledge of the boiler operation, and it is not easy to incorporate the topological information of production into modelling. Therefore, a graph convolutional network is proposed for NOx emission prediction. First, the key variables affecting NOx generation are selected according to the knowledge and the random forest-based variable importance. Then, the model structure is designed by exploring the topological information among thermal variables to capture the complex spatial dependence. The model inputs are constructed by coding different operation variables, and the adjacency matrix is generated according to the correlation information between variables, which can fuse data information and reduce redundancy. On this basis, the prediction model of NOx concentration is established. Historical data from a 660 MW coal-fired boiler are used in the experiment. The prediction results show that the proposed model can effectively fuse the information of characteristic variables and fully exploit the non-linear mapping relationship between process variables and NOx emission. When compared with three typical models in NOx modelling, the proposed model has better performance with a determination coefficient of 0.906.     

基于动态MPLS的发酵过程效益函数在线预报

针对发酵过程观测数据与时序相关,提出一种动态多方向偏最小二乘回归(MPLS)方法,该方法的多模型结构解决了非线性和实时性的问题,更加适用于发酵过程的在线预报,与人工神经网络(ANN)方法相比,动态MPLS回归模型可以达到更好的拟合和预报精度.对青霉素发酵过程的效益函数拟在线预报,验证了该方法的准确性和有效性.     

Comparative study of estimation methods of NO<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript> emission with selection of input parameters for a coal-fired boiler

This study focuses on estimation of NO_x emission and selection of input parameters for a coal-fired boiler in a 500 MW power generation plant. Careful selection of input parameters is required not only to improve accuracy of the estimation, but also to reduce the model dimensionality. The initial operating input parameters are determined based on operation heuristics and accumulated operation knowledge; the essential input parameters are selected by sensitivity analysis where the performance of the estimation model is assessed as one or some input parameters are successively eliminated from the computation while all other input parameters are retained. From the sequential input selection process, less than ten input parameters survived out of 36 initial input parameters. Auto-regressive moving average (ARMA) model, artificial neural networks (ANN), partial least-squares (PLS) model, and least-squares support vector machine (LSSVM) algorithm were proposed to express the relationship between the operating input parameters and the content of NO_x emission. Historical real-time data obtained from a 500 MW power plant coal-fired boiler were used to test the proposed models. It was found that principal components analysis (PCA) enhances the estimation performance of each model. Among the four proposed estimation models, the LSSVM model coupled with PCA scheme showed the minimum root-mean square error (RMSE) and the best R-square value.     

煤种性质对煤粉工业锅炉结焦的影响

燃煤锅炉内结焦会对锅炉运行的安全性和经济性造成极大损害,因而分析影响燃煤锅炉结焦的因素,进而有效预防燃煤锅炉结焦至关重要。在实际应用中,针对影响燃煤锅炉结焦的不同因素,可采取不同的预防措施。研究发现煤的灰熔融性温度、煤粉颗粒大小、锅炉燃烧气氛、一二次风动力场、锅炉截面热负荷和锅炉热负荷等都会影响燃煤锅炉结焦。为了解决某地区煤粉工业锅炉预燃室、炉膛、对流受热面大面积燃烧结焦问题,笔者结合燃煤锅炉燃烧结焦的机理,先后采取调整燃烧气氛、增大二次风刚性、减小煤粉颗粒粒径、更换孙家岔煤粉等措施对不同条件下的结焦现象进行对比分析,发现煤种、煤粉粒径大小是影响某地区煤粉工业锅炉燃烧结焦的因素。通过SEM-EDS(扫描电镜和能谱分析)对锅炉焦块进行微观形貌与元素组成分析,现场取样锅炉现用煤粉和孙家岔煤粉进行煤质及灰成分对比分析,并根据灰成分进行结渣性判别指标计算,结果表明锅炉燃烧现用煤种灰熔融性温度较低,煤灰软化温度Ts为1 170℃,小于1 200℃,为易熔煤,容易结渣,属于典型的易结焦煤种;结渣性判别指标计算结果显示,4项指标评价为"严重",1项指标评价为"中等",结渣性严重。综合分析认为:锅炉燃烧煤种发生改变,煤的灰熔融温度较低是影响某地区煤粉工业锅炉燃烧结焦的最本质因素。为进一步解决现场实际问题,采取破坏煤灰中酸碱平衡,提升煤的灰熔融温度,配合调节煤粉粒径等措施,如对锅炉现用煤种掺混5%的石英,提高煤灰中Si O2含量,掺混后煤粉的灰熔融温度达到1 280℃,提高了110℃;调大煤粉磨机频率,从19 Hz增大到22 Hz,煤粉粒度(200目,0.075 mm)过筛率从70%增大到85%。经过上述调整后,锅炉运行平稳,结焦状况显著改善,燃烧调整措施取得了较好的效果。     

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.     

基于径向基函数的非线性系统近似偏最小一乘准则辨识算法

针对输入信号非线性相关的非线性系统,提出了基于径向基函数的近似偏最小一乘准则辨识算法。首先对观测数据矩阵进行列扩展,以径向基函数（radial basis function,RBF）网络的输出作为观测数据矩阵的扩展项,然后利用近似偏最小一乘算法对扩展的观测矩阵和输出矩阵进行线性回归。近似偏最小一乘算法用确定性可导函数近似代替残差绝对值,可以抑制对称α稳定（symmetrical alpha stable,SαS）分布的尖峰噪声。同时,通过主成分分析去除非线性系统数据向量矩阵之间的非线性相关,得出模型参数的唯一解。仿真实验表明,本文算法可以对输入信号存在非线性相关的非线性系统进行直接辨识,抑制了尖峰噪声对辨识结果的影响,具有优良的稳健性。     

基于稳定Hammerstein模型的在线软测量建模方法及应用

针对复杂工业过程中由于存在未建模动态和不确定干扰,导致关键变量的软测量精度下降的问题,提出了一种基于稳定Hammerstein模型(H模型)的在线软测量建模方法。H模型的非线性增益采用带有时变稳定学习算法的小波神经网络模型,线性系统部分采用基于递推最小二乘的ARX模型,基于输入到状态稳定性理论证明了H模型辨识误差的有界性。其中小波神经网络具有表征强非线性的特性,稳定学习算法可抑制未建模动态和不确定干扰的影响,改善了模型的预测精度和自适应能力。以典型非线性系统和实际污水处理过程为例进行了仿真研究,结果表明,基于稳定H模型的软测量方法具有较高的在线软测量精度。