基于推断模型的工业气相聚乙烯装置多牌号质量指标在线控制

针对气相法聚乙烯装置生产中的缺乏质量指标在线测量、大时间滞后和频繁的牌号切换等特性,基于质量指标推断模型,提出一种基于质量指标推断模型的多牌号质量指标在线控制方法。所提方法已成功应用于中石化某气相聚乙烯装置,长周期的运行结果证实了所提方法可以有效地减少产品质量波动,减轻了操作人员的工作强度,取得了显著的经济效益。     

Development of polyethylene melt index inferential model

In polyolefin processes the melt index (MI) is the most important control variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a large number of MI estimation and correlation methods have been proposed. In this work, we propose a new MI predicting model in which the reaction heat term in existing prediction models is replaced by temperature. The proposed model is compared with other prediction models as well as operating data to evaluate MI predicting performance. The present model shows least predicting errors compared to existing models.     

基于粒子滤波联合估计的气相聚乙烯质量指标在线估计

针对气相聚乙烯生产中各种复杂的工况造成在线估计精度下降的现象,基于乙烯聚合机理并利用特征建模方法建立了聚乙烯质量指标预测模型,结合扩展卡尔曼滤波,提出了粒子滤波联合估计方法,即将状态和修正系数组成增广状态向量,实现对质量指标预测模型的在线滤波修正,并分析了基于粒子滤波估计的收敛性。所提方法在中石化某气相聚乙烯装置的长周期运行结果证实了所提方法的可行性和有效性,为实施聚乙烯装置的先进控制奠定了基础。     

STATE ESTIMATION IN THE BATCH DRYING OF FOODS

ABSTRACT

Measurement of material moisture content is necessary for the control of product quality in batch drying. However, this variable cannot be measured on-line, and state estimation techniques are proposed. A non-linear dynamic model is developed for batch drying of foods. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. This filter is based on the local linearization of the process model around the suboptimal filter estimates. The moisture estimation approach was applied to experimental points obtained in a laboratory dryer with quite satisfactory results.     

基于拉曼光谱的高密度聚乙烯质量检测

密度和熔融指数是高密度聚乙烯（HDPE）产品最重要的质量指标。本文通过拉曼光谱,结合偏最小二乘法（PLS）分析,实现了对HDPE密度和熔融指数的同时检测。通过对2700～2970 cm^-1范围内HDPE的拉曼光谱进行PLS分析,发现了HDPE的密度与短支链数量的负相关,并建立了HDPE密度的PLS回归模型。模型所得密度预测值与真实值的相关系数（r）、平均相对误差（ARD）和预测标准误差（SEP）分别为0.950、0.09%和1.02,优于近红外光谱和基于凝聚态结构分析的拉曼光谱的检测结果。利用HDPE乙烯基含量与熔融指数的正相关,通过分析1288～1650 cm^-1范围内的拉曼光谱,建立了HDPE熔融指数的PLS回归模型,所得熔融指数的预测值与真实值的r、ARD和SEP分别为0.966、8.61%和0.99。与熔融指数的红外光谱检测结果相比,拉曼光谱的检测结果具有更高的精度。     

Neural virtual sensor for the inferential prediction of product quality from process variables

A predictive Fuzzy ARTMAP neural system and two hybrid networks, each combining a dynamic unsupervised classifier with a different kind of supervised mechanism, were applied to develop virtual sensor systems capable of inferring the properties of manufactured products from real process variables. A new method to construct dynamically the unsupervised layer was developed. A sensitivity analysis was carried out by means of self-organizing maps to select the most relevant process features and to reduce the number of input variables into the model. The prediction of the melt index (MI) or quality of six different LDPE grades produced in a tubular reactor was taken as a case study. The MI inferred from the most relevant process variables measured at the beginning of the process cycle deviated 5% from on-line MI values for single grade neural sensors and 7% for composite neural models valid for all grades simultaneously.     

Cascade closed-loop optimization and control of batch reactors

In this paper, a cascade closed-loop optimization and control strategy for batch reactors is proposed. Based on the reduction of a physical conservation model a cascade system is developed, which can effectively combine optimization and control to achieve good on-line optimization and tracking performance under the common condition where incomplete knowledge of the reaction system exists. A two-tier estimation scheme using a nonlinear observer for heat production rate and reaction rates is also developed. In the reaction rate estimation, calorimetric information is used. The on-line closed-loop optimization strategy uses a descending horizon dynamic optimization algorithm based on nonlinear programming and an additive unknown disturbance for feedback. A simple adaptive nonlinear tracking system is designed based on the generic model control concept. The efficiency of this strategy is demonstrated through simulations on a batch reactor under various operation conditions, such as noisy measurements, varying initial states and model mismatch.     

Prediction of the melt flow index using partial least squares and support vector regression in high-density polyethylene (HDPE) process

In polyolefin processes the melt flow index (MFI) is the most important control variable indicating product quality. Because of the difficulty in the on-line measurement of MFI, a large number of MFI estimation and correlation methods have been proposed. In this work, mechanical predicting methods such as partial least squares (PLS) method and support vector regression (SVR) method are employed in contrast to conventional dynamic prediction schemes. Results of predictions are compared with other prediction results obtained from various dynamic prediction schemes to evaluate predicting performance. Hourly MFIs are predicted and compared with operation data for the high density polyethylene process involving frequent grade changes. We can see that PLS and SVR exhibit excellent predicting performance even for severe operating situations accompanying frequent grade changes.     

STATE ESTIMATION IN THE BATCH DRYING OF FOODS

Measurement of material moisture content is necessary for the control of product quality in batch drying. However, this variable cannot be measured on-line, and state estimation techniques are proposed. A non-linear dynamic model is developed for batch drying of foods. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. This filter is based on the local linearization of the process model around the suboptimal filter estimates. The moisture estimation approach was applied to experimental points obtained in a laboratory dryer with quite satisfactory results.     

Modelling a uranium ore bioleaching process

A dynamic simulation model for the bioleaching of uranium ore in a stope leaching process has been developed. The model incorporates design and operating conditions, reaction kinetics enhanced by Thiobacillus ferrooxidans present in the leaching solution and transport properties. Model predictions agree well with experimental data with an average deviation of about ± 3%. The model is sensitive to small errors in the estimates of fragment size and ore grade. Because accurate estimates are difficult to obtain a parameter estimation approach was developed to update the value of fragment size and ore grade using on-line plant information.     

聚合物质量控制技术研究进展

综述了聚合物生产过程中质量控制技术的重要性,讨论了在线软测量和牌号切换操作的质量控制、建立能真实反映过程机理的质量模型,是实现聚合物质量精密d控制的核心;介绍了进行过程全局质量控制的必要性,对该领域的难点问题及可能的研究方向进行了探讨。     

基于动态过程划分的熔融指数软测量建模

牌号切换过程是聚合反应过程节能降耗的关键环节,已有的模型往往因为质量样本较少而弱化甚至避开这一过程。因此,在之前专门针对牌号切换过程提出的三阶段分解方法的基础上,进一步面向整个生产过程,针对聚丙烯反应过程存在的同一牌号的稳定生产过程以及不同牌号间的切换过程具有不同动态过程的特性,按照不同的生产模式和生产牌号划分不同动态过程的样本,采用多模型的方法在各自的样本集上建立子模型,有针对性地把握相应的动态变化规律。为了实现多个子模型之间的切换,进一步基于反应条件和反应结果估计值构建了综合判断模型。最后,通过实际数据验证,三阶段多模型相对Kim多模型、单一模型来说,具有更好的预测结果。     

Pole-placement self-tuning control of nonlinear Hammerstein system and its application to pH process control

By taking advantage of the separation characteristics of nonlinear gain and dynamic sector inside a Hammerstein model, a novel pole placement self tuning control scheme for nonlinear Hammerstein system was put forward based on the linear system pole placement self tuning control algorithm. And the nonlinear Hammerstein system pole placement self tuning control (NL-PP-STC) algorithm was presented in detail. The identification ability of its parameter estimation algorithm of NL-PP-STC was analyzed, which was always identifiable in closed loop. Two particular problems including the selection of poles and the on-line estimation of model parameters, which may be met in applications of NL-PP-STC to real process control, were discussed. The control simulation of a strong nonlinear pH neutralization process was carried out and good control performance was achieved.     

DMS中人工神经网络的在线训练法

基于ANN(artificialneuralnetwork)的分馏塔侧线质量指标动态在线检测系统 ,简称DMS (dynamicon -linemonitoringsystem) ,成功地应用于炼油厂粗汽油干点、柴油凝点、Reid蒸汽压、闪点、冰点等的实时、在线、动态检测 ,达到很高的预测精度 ,极大地改善了分馏塔的控制性能 ,实现了分馏塔的操作优化 .本文提出一种新的网络训练方法———在线训练法 ,提高了在线检测系统的预测精度 .实际应用表明 ,这种方法可以有效克服人工神经网络离线训练的不足 ,提高动态在线检测系统的可靠性 ,并且省时省力     

影响单丝牌号5000S产品质量的因素分析

单丝牌号5000S是大庆HDPE装置的主要产品,适用于加工渔网丝、编织袋等产品。作者们讨论了产品的物性参数特点,分析了以往产品质量存在的主要问题。从工艺技术方面对于如何提高5000S产品的优级品率,提高产品的加工性能和使用性能进行了讨论。     

基于工业色谱仪的精馏脱炔烃塔软测量建模

提出一种基于工业色谱仪的软测量建模方法,并针对碳五馏分分离过程中的精馏脱炔烃塔塔底成分估计问题,建立了合适的工业软测量模型。介绍了工业色谱仪在线质量检测原理和LM-BP神经网络模型的建立,并利用工业色谱仪在线检测的质量数据进行系统的在线和周期性模型更新,提高了软测量模型的在线估计精度。研究结果表明,基于工业色谱仪的LM-BP神经网络模型是一种有效的软测量建模方法。     

On-line batch process monitoring using dynamic PCA and dynamic PLS models

Producing value-added products of high-quality is the common objective in industries. This objective is more difficult to achieve in batch processes whose key quality measurements are not available on-line. In order to reduce the variations of the product quality, an on-line batch monitoring scheme is developed based on the multivariate statistical process control. It suggests using the past measured process variables without real-time quality measurement at the end of the batch run. The method, referred to as BDPCA and BDPLS, integrates the time-lagged windows of process dynamic behavior with the principal component analysis and partial least square respectively for on-line batch monitoring. Like traditional MPCA and MPLS approaches, the only information needed to set up the control chart is the historical data collected from the past successful batches. This leads to simple monitoring charts, easy tracking of the progress in each batch run and monitoring the occurrence of the observable upsets. BDPCA and BDPLS models only collect the previous data during the batch run without expensive computations to anticipate the future measurements. Three examples are used to investigate the potential application of the proposed method and make a comparison with some traditional on-line MPCA and MPLS algorithms.     

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.     

基于支持向量机的发酵过程生物量在线估计

Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.     

高密度聚乙烯装置低压溶剂回收系统分析

介绍了环管淤浆法高密度聚乙烯工艺低压溶剂回收的两种方法:膜回收法和压缩冷凝法。从回收率、设备配置等方面对这两种回收方法进行了对比。针对现有压缩冷凝工艺中乙烯回收率低的缺点,经Aspen Plus模拟计算,得到提升尾气压力和乙烯回收率的关系曲线,提出了优化方案。