基于混合建模技术的复合肥养分含量MIMO软测量模型

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.     

利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文)

On-line estimation of unmeasurable biological variables is important in fermentation processes, directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product. In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed. By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model, a state space model is developed. An improved algorithm, swarm energy conservation particle swarm optimization (SECPSO), is presented for the parameter identification in the mechanistic model, and the support vector machines (SVM) method is adopted to establish the nonlinear measurement model. The unscented Kalman filter (UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process. The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.     

A local and global statistics pattern analysis method and its application to process fault identification☆

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incor-porated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality pre-serving projections within the PCA, is proposed to utilize various statistics and preserve both local and global in-formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simula-tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm （NAGA）, is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

黄孢原毛平革菌产酶的简化结构动力学模型

In order to understand the behavior of ligninolytic enzyme production by white rot fungi Phanerochaete chrysosporium, study on time courses and a mathematical model for the production of lignin peroxidase (LiP) and manganese peroxidase (MnP) of the fungi was undertaken. Based on the Monod-Jacob operon model, the ligninolytic enzyme would be synthesized in the absence of a related repressor. The repressor is assumed to be active in the presence of ammonia nitrogen, and as combined as co-repressor, it causes the inhibition of enzyme synthesis. The model can explain the mechanism of extracellular ligninolytic enzyme production by white rot fungi. The results,as predicted by the model, correspond closely to those observed in experimental studies. In addition, some light is also shed on unmeasured variables, such as the concentrations of repressor and mRNA that are related to the enzyme synthesis.     

工业流化床乙烯共聚合生产过程中牌号切换的最优化研究

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process.The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables,such as polymerization temperature,the ratio of hydrogen to ethylene,the ratio of co-monomer to ethylene,feed rate of catalyst,and bed level.A new performance index,the ratio of melt flow(MFR),is included in the objective function,for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resion.It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product.This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variables due to the non-linearity of the system.The grade transition model is extended to a high space time yield(STY)Process with the so-called condensed model operation.In the end,an optimization strategy for multi-product transition is proposed with two-level optimization of the objective function J(x,u) on the the basis of the optimal grade transition model.A sequential transition of six commercial polyethylene grades in illustrated for an optimal multi-product operation.     

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is in-troduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to re-trieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.     

基于动态PLS框架的多回路内模控制器设计(英文)

In this paper, a multi-loop internal model control (IMC) scheme in conjunction with feed-forward strategy based on the dynamic partial least squares (DyPLS) framework is proposed. Unlike the traditional methods to decouple multi-input multi-output (MIMO) systems, the DyPLS framework automatically decomposes the MIMO process into a multi-loop system in the PLS subspace in the modeling stage. The dynamic filters with identical structure are used to build the dynamic PLS model, which retains the or-thogonality among the latent variables. To address the model mismatch problem, an off-line least squares method is applied to obtain a set of optimal filter parameters in each latent space. Without losing the merits of model-based control, a simple and easy-tuned IMC structure is readily carried over to the dynamic PLS control framework. In addition, by projecting the measurable disturbance into the latent subspace, a multi-loop feed-forward control is yielded to achieve better performance for disturbance rejection. Simulation re-sults of a distillation column are used to further demonstrate this new strategy outperforms conventional control schemes in servo behavior and disturbance rejection.     

拟稳态模型用于间歇萃取精馏的数值模拟

Batch extractive distillation (BED) is a special method used in the distillation process by adding a solvent into the batch distillation column to alter the relative volatility of the components and improve the separation. A comprehensive design and simulation method is required due to the complexity of BED. In this study, a quasi-steady-state model for BED is proposed, the derivation and solution of the model are presented. This shortcut model can be used to simulate the composition and temperature of the reboiler, the top and other plates of the column in a batch extractive distillation operation. The calculated values are in good agreement with the experi-mental data. The results show that the quasi-steady-state model is a practical method because of some advantages such as high precision and fast calculation.     

一种基于测量的双层动态优化策略及其在青霉素发酵过程中的应用(英文)

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of al kinds of uncertainties. This assumption is not satisfied in some cases and the stan-dard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is il ustrated via a bioreactor in penicil in fermentation process.     

基于MIC的支持向量回归及其在化工过程中的应用

在化工生产中,软测量方法可以有效解决某些关键变量由于仪表故障而无法实时获取数据的问题。在建立软测量模型时,变量及回归方法的选取会直接影响模型的准确率。特别是在现代化工中,过程变量众多且变量间存在着冗余且复杂的非线性关系。对此,本文提出了一种基于最大信息系数的支持向量回归算法,利用最大信息系数在非线性相关性度量的优势,选择合适的辅助变量,避免了全部变量作为输入所造成的数据冗余。在此基础上,利用支持向量回归方法建立软测量模型,实现对软测量目标的预测。该方法被应用于存在仪表故障的某催化重整装置进料换热器热端压降的软测量中,结果表明该方法可以有效地实现对压降的软测量,实现了对仪表故障时的数据校正。     

竖炉燃烧过程智能故障预报系统

赤铁矿竖炉燃烧过程机理复杂,运行工况变化频繁,使得故障易发,从而导致生产不稳定。将案例推理和软测量技术相结合,提出一种竖炉燃烧过程的智能故障预报方法。软测量模型对难以在线测量的关键工艺参数进行实时测量,基于案例检索与重用的故障预报模型根据过程数据及关键工艺参数软测量值的变化对燃烧过程的典型故障进行趋势预报,采用概率的形式表达诊断结果,并提供操作指导,可以有效避免故障的发生。将建立的故障预报系统应用于竖炉燃烧过程的生产实际中,故障发生率明显降低,表明了方法的有效性。     

基于在线聚类的多模型软测量建模方法

针对石化行业中软测量建模样本的特性，提出一种基于在线聚类和v-支持向量回归机(vSVR)的多模型软测量建模方法。在vSVR建模过程中，通过在线聚类算法改善vSVR模型参数选择算法的稳定性，并用vSVR参数的先验知识和KKT条件实现模型参数的快速寻优，提高了模型的学习效率和精度。该建模方法在加氢裂化分馏塔装置的轻石脑油终馏点在线预测系统中取得了良好的效果。     

基于D-FNN的聚合过程转化速率软测量建模及重构

引言以氯乙烯单体(VCM)为原料,采用悬浮法聚合工艺生产聚氯乙烯(PVC)树脂是一种典型的间歇式化工生产过程。VCM的转化率对PVC树脂产品质量有很大影响,不同转化率时对PVC     

乙醛生产过程中的软测量实现

针对乙醛生产过程 ,建立关键过程变量粗乙醛浓度的软测量模型 ,并在此基础上建立粗乙醛的实时收率预测模型。对软测量实现中涉及到的回归变量选择、样本预处理、回归一致性分析、实时校正机制等关键技术进行讨论。该系统的预测值与离线分析值平均相对偏差为 1.2 %。     

基于聚类的多模型软测量建模及其应用

实际生产中往往存在对产品质量影响重大、但又难以在线测量的一些参数(只能离线测量与分析)。本文提出一种基于聚类的多模型建模方法对这些难以在线测量的参数实现软测量,将相关性分析、主元分析(PCA)、聚类和多模型建模应用于软测量建模中,构建一种实现重要参数软测量的基本框架:首先,基于相关度分析进行辅助变量的选择,然后用主元分析进行数据的进一步降维,再用k-means聚类与多模型建模思想相结合。最后将提出的思想和方法应用于某精馏塔组分的软测量中,仿真结果表明,测量精度有了较大的提高。     

基于协同量子粒子算法的透平蒸汽流量软测量

透平蒸汽流量是分析透平运行效率的重要参数,由于现场大量缺乏检测信息,且针对传统测量方法存在可靠性差,非接触式测量成本高、安装困难等问题,提出了一种协同量子粒子算法(CQGAPSO),同时优化神经网络(NN)结构和参数的透平蒸汽流量的软测量建模方法。该方法利用节点间的连接开关,有效消除冗余连接对神经网络逼近能力的影响,引入量子概率幅编码和协同机制来提高神经网络的学习效率、逼近精度和泛化能力。透平蒸汽流量软测量的仿真结果表明:相比全连接神经网络和其他模型,所述方法具有更好的预测精度和鲁棒性。     

基于多元线性回归与滚动窗的NO_x排放量软测量

提出一种基于多元线性回归与滚动窗的NO_x动态软测量建模方法,并研究模型更新周期和不同变量选择方法对软测量模型预测结果的影响。仿真结果表明:基于BIC向后剔除多元回归和滚动窗方法的软测量模型在运行较长时间后,仍然能够较好地预测NO_x的排放量。     

基于粒子滤波的高浓度染料色泽软测量模型辨识方法

为了解决高浓度混合染料间歇染色过程织物色泽的在线测量问题,即非线性色泽软测量,提出了基于粒子滤波的软测量方法,该方法通过测定间歇染机内染液的吸光度,采用粒子滤波算法来估计该染液中各染料浓度,再根据染料浓度与织物色泽的软测量模型计算出染机内织物色泽。以3种活性染料拼染纯棉为例,对该方法的可行性进行验证。实例证明,基于粒子滤波的软测量方法的色泽估计值与人工离线实测值之间的色差值在1.0 CIELAB之内,模型的估计值能够满足工艺要求。因此,该方法是有效可行的。     

基于动态神经网络的高炉炉壁不完备温度检测信息软测量方法

针对高炉炉壁温度检测系统中由于传感器故障导致的检测信息不完备问题,提出一种基于动态神经网络的不完备检测信息软测量方法。首先,依据高炉结构和炉壁温度传感器位置分布建立温度传感器位置描述模型和分区域温度检测模型;其次,根据热传递学分析炉壁分区域温度检测模型中各个传感器之间存在的相关性,并采用最大互信息非参统计量方法从传感器检测序列上定量的计算分区域温度检测模型中各传感器间的相关度;最后,依据相关性分析结果,结合温度传递规律,提出炉壁不完备温度检测信息软测量模型,采用Elman神经网络对模型的结构和参数进行辨识。通过高炉冶炼现场采集的数据仿真计算表明,提出的方法具有较好的准确度与检测精度,能够满足现场的检测精度要求,具备广泛的应用价值。