A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ...A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.展开更多
In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance...In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.展开更多
Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized ...Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.展开更多
Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the err...Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.展开更多
软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一...软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一种基于时差的多输出tri-training异构软测量方法。通过构建一种新的tri-training框架,采用多输出的高斯过程回归(multi-output Gaussian process regression,MGPR)、相关向量机(multi-output relevance vector machine,MRVM)、最小二乘支持向量机(multi-output least squares support vector machine,MLSSVM)三种模型作为基线监督回归器,使用标记数据进行训练和迭代;同时,引入时间差分(time difference,TD)改进模型的动态特性,并通过卡尔曼滤波(Kalman filtering,KF)优化模型的参数,提高其预测性能;最后通过模拟污水处理平台(benchmark simulation model 1,BSM1)和实际污水处理厂对该模型进行了验证。结果表明,与传统的软测量建模方法相比,该模型能显著提高数据分布不平衡下软测量模型的自适应性和预测性能。展开更多
实际化工工业过程数据往往存在多重共线性、高度非线性等多重特性,这会严重影响传统软测量模型对关键质量变量的预测精度。针对这一局限性,提出了一种分布式非线性映射和并行输入的双向长短记忆(distributed nonlinear mapping and para...实际化工工业过程数据往往存在多重共线性、高度非线性等多重特性,这会严重影响传统软测量模型对关键质量变量的预测精度。针对这一局限性,提出了一种分布式非线性映射和并行输入的双向长短记忆(distributed nonlinear mapping and parallel input bidirectional long short-term memory,DNMPI-BiLSTM)软测量模型。在所提策略中,首先为了阐述过程变量与质量变量之间的关联性,采用互信息以及最大相关最小冗余方法对输入数据集进行分类。随后,为了充分挖掘工业过程内部所包含的高度复杂的非线性关系,利用深度极限学习机的隐藏层对子过程变量空间进行非线性映射到高维空间。最后,将三类数据的非线性映射结果并行,建立了基于分布式非线性映射和并行输入的DNMPI-BiLSTM软测量模型,以提升模型对复杂工业过程质量变量的预测能力。通过三个工业案例验证所提方法的有效性,仿真结果表明,所提出的基于分布式非线性映射和并行输入的BiLSTM软测量建模方法的预测精度优于其他先进模型。展开更多
文摘A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.
文摘In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.
基金Project ( 2001AA411040 ) supported by the National High Technology Development Program of China project(2002CB312200) supported by the National Fundamental Research and Development Program of China
文摘Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.
基金Supported by National Natural Science Foundation of P.R.China(50474020,60534010,60504006)
文摘Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.
文摘软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一种基于时差的多输出tri-training异构软测量方法。通过构建一种新的tri-training框架,采用多输出的高斯过程回归(multi-output Gaussian process regression,MGPR)、相关向量机(multi-output relevance vector machine,MRVM)、最小二乘支持向量机(multi-output least squares support vector machine,MLSSVM)三种模型作为基线监督回归器,使用标记数据进行训练和迭代;同时,引入时间差分(time difference,TD)改进模型的动态特性,并通过卡尔曼滤波(Kalman filtering,KF)优化模型的参数,提高其预测性能;最后通过模拟污水处理平台(benchmark simulation model 1,BSM1)和实际污水处理厂对该模型进行了验证。结果表明,与传统的软测量建模方法相比,该模型能显著提高数据分布不平衡下软测量模型的自适应性和预测性能。
文摘实际化工工业过程数据往往存在多重共线性、高度非线性等多重特性,这会严重影响传统软测量模型对关键质量变量的预测精度。针对这一局限性,提出了一种分布式非线性映射和并行输入的双向长短记忆(distributed nonlinear mapping and parallel input bidirectional long short-term memory,DNMPI-BiLSTM)软测量模型。在所提策略中,首先为了阐述过程变量与质量变量之间的关联性,采用互信息以及最大相关最小冗余方法对输入数据集进行分类。随后,为了充分挖掘工业过程内部所包含的高度复杂的非线性关系,利用深度极限学习机的隐藏层对子过程变量空间进行非线性映射到高维空间。最后,将三类数据的非线性映射结果并行,建立了基于分布式非线性映射和并行输入的DNMPI-BiLSTM软测量模型,以提升模型对复杂工业过程质量变量的预测能力。通过三个工业案例验证所提方法的有效性,仿真结果表明,所提出的基于分布式非线性映射和并行输入的BiLSTM软测量建模方法的预测精度优于其他先进模型。
