Study of the application of multiway multivariate techniques to model data from an industrial fermentation process

Several multivariate statistical techniques have been extensively proposed for monitoring industrial processes. In this paper, multiway extensions of two such techniques: multiway principal component analysis (MPCA) and multiway partial least squares regression (MPLS) were applied to a large data set from an industrial pilot-scale fermentation process to improve process knowledge. The MPCA model is able to diagnose faults occurring in the process whether they affect or not process productivity while the MPLS model enables the prediction of final product concentration and the detection of faults that will influence the fermentation productivity.     

AIE荧光聚合物RAFT可控合成与表征及光物理性质研究

聚集诱导发光(AIE)分子是与传统的聚集态荧光淬灭染料分子具有截然相反的光物理性质的新型有机发光材料,可广泛应用于化学/生物传感、生物探针与成像、诊疗一体化和光电子器件等诸多领域中。本论文通过可逆加成-断裂链转移(RAFT)聚合方法,可控合成了侧链型四苯乙烯TPE聚丙烯酸酯AIE聚合物。通过实验条件的优化与探索,尤其采用半衰期较短、活性更高的偶氮二异庚腈(ABVN)取代常规的偶氮二异丁腈(AIBN)引发剂,将原来超过12 h的过夜反应前沿科研实验,改造为较短的3–5 h聚合反应时间内即可达到中等收率和较好的聚合物产品质量,使其成为一个适合本科教学环境的新创实验。本实验融合了无水无氧操作技术、柱层析分离纯化、RAFT可控聚合和GPC分子表征技术、FTIR、NMR、UV-Vis、荧光光谱等多种现代实验技术和表征方法,考查了所合成四苯乙烯TPE侧基的AIE聚合物的光物理性质,测定其溶液中的相对荧光量子产率达17%。     

In-Line Monitoring of Bulk Polypropylene Reactors Based on Data Reconciliation Procedures

The main objective of this work is the implementation of a nonlinear dynamic data reconciliation (NDDR) procedure for the in-line monitoring of the bulk propylene polymerization, as performed in a real industrial site. Special attention is given to monitoring of the melt flow index (MI) of the polymer product obtained in an industrial extruder at different operating conditions. In order to do that, a model is developed to describe the polymerization process and the controlled degradation of polypropylene in a reactive extruder. The model is implemented in real time, allowing for in-line and real-time monitoring of the plant operation.     

Multimode process monitoring based on Bayesian method

Multimode process monitoring has recently attracted much attention both in academy and industry. Conventional methods assume that either the process data are Gaussian in each operation mode, or some process knowledge should be incorporated, thus making the methods supervised. In this paper, a new unsupervised method is developed for multimode process monitoring, which is based on Bayesian inference and two‐step independent component analysis–principal component analysis (ICA–PCA) feature extraction strategy. ICA–PCA is first introduced for feature extraction and dimension reduction. By transferring the traditional monitoring statistic to fault probability in each operation mode, monitoring results in different operation modes can be easily combined by the Bayesian inference. Another contribution of the present paper is the development of a new fault identification method. Through analyses of the posterior probability and the joint probability for the monitored data sample, the correct operation mode or fault scenario can be identified. Three case studies are demonstrated to evaluate the feasibility and efficiency of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

Arduino实时在线监测系统在聚甲基丙烯酸甲酯本体聚合过程中的应用

利用自主研发的实时在线监测系统，对不同聚合温度条件下，聚甲基丙烯酸甲酯（PMMA）的本体聚合过程进行实时在线监测，建立"虚拟信号值-反应时间"实时在线监测曲线.通过曲线的变化，可以清晰观察到PMMA本体聚合过程的4个阶段，即诱导期、初期、中期和后期.根据本体聚合"转化率-反应时间"S型曲线的特点，构建"虚拟信号值-反应时间-转化率"关系曲线和聚合物转化率计算公式，并提出PMMA预聚体的预测和实时在线监测方法.方法具有操作简单、实时性强、外界因素影响小和性能稳定等优点，是一种新型的实时在线分析方法.     

Data fusion methodologies for food and beverage authentication and quality assessment – A review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.     

Steady State Simulation of Ethylene Polymerization Using Multiple-Site Coordination Catalysts

Summary: Simulation models are important tools for the development and optimization of polymerization processes because they can describe catalyst performance and polymer properties as a function of polymerization kinetics and process conditions. As the polyolefin industry moves towards the production of resins with more complex microstructures, these models become essential for process understanding and product design. A simulation model has been developed for the polymerization of ethylene in a process with n reactors working in series. The model can predict raw material conversions and product properties like the molecular weight distribution (MWD) coupled with short chain branching distribution (SCBD), melt index, density and fluff morphology. Model parameters have been obtained from laboratory data. The model predictions are in good agreement with experimental results.     

Fermentation diagnosis by multivariate statistical analysis

During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).     

Direct Transformation of N,N′‐Methylene Bisacrylamide Self‐Assembled Fibers Into Polymer Microtubes via RAFT Polymerization

A novel fabrication method of polymer tubes with simple operation process and high yield is presented. N,N′‐methylene bisacrylamide (MBA) polymer microtubes are fabricated via reversible addition–fragmentation chain transfer (RAFT) polymerization using MBA self‐assembled fibers as both the template and monomer source. The resulting products are characterized by SEM, TEM, FTIR, and element analysis. The mechanical properties of the gel‐like product and the MBA organogel are measured by rheometer. The morphology of the polymer tubes obtained via RAFT polymerization is compared with the sample obtained via conventional radical polymerization. Based on the current investigations, the fabrication mechanism of this method is initially proposed.     

Crystallization analysis fractionation

Crystallization analysis fractionation (CRYSTAF) is an analytical technique for determining the distribution of chain crystallizabilities of semicrystalline polymers. After only approximately a decade since it was developed, CRYSTAF has become one of the most important characterization techniques in polyolefin characterization laboratories because it provides fast and crucial information required for the proper understanding of polymerization mechanisms and structure–property relationships. In the polyolefin industry, it has been established as an indispensable tool for product development and product quality monitoring. This highlight article covers basic operation procedures, applications, and theoretical aspects of polymer fractionation with CRYSTAF. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 1557–1570, 2005     

The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems

The use of Fourier transform near infrared (FT-NIR) spectroscopy for simultaneous determination of multiple properties in an active pharmaceutical ingredient (API) fermentation process is described, together with procedures for developing accurate NIR calibrations with a performance independent of scale and the specific bioreactor used. Measurements were made in situ, by insertion of transflection probes into pilot and industrial bioreactors providing direct contact with the fermentation culture media. The ultimate goal was to establish methods for real time process monitoring aimed at enhanced process supervision, fault detection diagnosis and control of bioreactors. The in situ acquired spectra were related to lab results of samples taken from the reactors during the course of the manufacturing process. Suitable spectral wavenumber regions were selected and calibration models based on partial least squares (PLS) were developed. The root mean square errors of prediction for API content, viscosity, nitrogen source and carbon source concentration were all within acceptable ranges as compared to the off-line lab measurements, respectively, 0.03% (w/w), 150 cp, 0.01% (w/w), and 0.4% (w/w).     

Polymerization kinetics and modeling of a metallocene cyclic olefin copolymer system

Cyclic olefin copolymers (COCs) have received increasing attention owing to their unique properties and extensive applications, especially in biomedical and photo-optic areas. The applications of COCs depend on the glass transition temperature of the copolymers. The composition of a particular copolymer largely determines the glass transition temperature of such a copolymer.This study presents a polymerization mechanism and model for the ethylene-norbornene copolymerization. Effects of operating conditions such as ethylene pressure, catalyst concentration, and cocatalyst ratio on the product properties including molecular weight, copolymer composition and structure, and copolymer yields are discussed. The kinetic parameters were obtained by minimization of residues between calculated results and experiments conducted in a laboratory scale reactor. The proposed model can facilitate the design and the operation of polymerization reactors.     

Forced Oscillations in a Nonisothermal Continuous Polymerization Reactor

A dynamic model of a nonisothermal continuous stirred tank polymerization reactor is developed to predict product molecular weight distribution parameters. Deliberate oscillations in operating variables were programmed into the model and the resulting time-averaged reactor performance indices compared with that for optimum steady-state operation. Results indicate that periodic operation does influence polymerization reactor performance, particularly with regard to time-averaged molecular weight polydispersity. Significant resonant behavior was encountered with molecular weight properties at low frequencies.     

Polymerization of monolayers of vinyl and divinyl monomers

Monolayer balance techniques have been used to study “two-dimensional” polymerization of monomolecular films of octadecyl methacrylate and a divinyl ester. Polymerizations were initiated with ultraviolet radiation, and reaction rates and properties of the product evaluated from surface pressure–area isotherms. The polymerization rate of the acrylate in argon is linear up to 70% conversion; the product obtained in this way exhibits an isotherm which may reflect the packing and orientation of the starting monolayer. Rate data for the divinyl system—the product of which should be a sheet-like, two-dimensional analog of a network system—exhibit no unusual features.     

过程分析技术(PAT)在原料药生产中的应用

该文通过采用近红外光谱分析技术对原料药(API)的浓度调节过程进行实时监控,介绍了在良好生产规范条件下过程分析技术(PAT)的实施过程。利用偏最小二乘算法开发出两个校正模型分别用以监控原料药和水分含量,并通过模型校正均方根误差(RMSEC)、交叉检验均方根误差(RMSECV)和预测均方根误差(RMSEP)以及对应的决定系数(R~2)来评估模型的性能。为保证模型性能,按照分析方法验证要求对模型的线性和范围、准确性、精密度(重复性)、专属性以及稳健性指标进行验证。最后通过系统性能测试确认检测系统满足商业化运行的要求。结果显示,采用过程分析技术控制浓度调节过程,可以大幅度缩短浓度调节时间,节约蒸汽能耗和检测费用,减少生产过程中的偏差,提升产品工艺水平和批次间一致性。     

Industrial Polymerization Monitoring

Summary: Monitoring and control of polymerization reactions is essential for high process safety, high product quality and competitive production costs. Ideally the entire process chain is regarded, starting with raw material analysis and the polymerization reaction up to the measurement of polymer- and application- properties. Process data like temperatures and pressures can be used to monitor reaction trajectories in a cost effective way, e.g. using calorimetric evaluations. Additional sensors can provide chemical or morphological information but must be robust and inexpensive for commercial applications (e.g. NIR- or Raman spectroscopy). Data from these different sources can be used for multivariate data analysis, delivering additional insights that might not be obtained by direct measurement.     

苯胺在十二烷基苯磺酸/水体系中的乳液聚合过程研究

利用光学显微镜和扫描电镜,对以水为介质、十二烷基苯磺酸(DBSA)为乳化剂的苯胺乳液聚合过程进行监测,发现苯胺在水体系中与DBSA反应形成DBSA-苯胺盐的棒状聚集结构,讨论了DBSA、苯胺、氧化剂的配比及浓度对聚合过程中棒状聚集结构的长度和数量及生成聚苯胺的电导率的影响,提出苯胺在DBSA/水体系中的乳液聚合反应是在胶束表面进行的,而棒状聚集结构中的DBSA-苯胺盐单体经水相扩散到乳胶粒子中,形成颗粒状的聚苯胺.     

Effect of pH on Poly(acrylic acid) Solution Polymerization

The free-radical redox-initiated aqueous solution polymerization of fully and partially neutralized acrylic acid was carried out at room temperature under full exposure to air. The effect of neutralization degree on the polymerization rate and product properties was studied. Increasing neutralization of the reaction mixture with sodium hydroxide resulted in greater conversion of acrylic acid to sodium acrylate. The rate of polymerization, determined from a gravimetric off-line water removal technique, was shown to decrease significantly with decreasing degree of neutralization. Molecular weight also decreased with decreasing degree of neutralization. The glass transition temperature and hydrophilicity of the polymer product decreased with increasing degree of neutralization. In-line infrared monitoring was also used to monitor the reaction progress and was shown to be an effective tool for this purpose.     

Modeling Nd-Catalyzed Butadiene Rubber Production

Summary: A simulation tool was developed for the industrial solution polymerization of 1,3-butadiene with a Nd-based homogeneous Ziegler-Natta catalyst system. Insight into underlying reaction mechanisms was gained from laboratory experiments. Besides the chain growth reaction, the following steps were identified: catalyst formation, deactivation reactions, and molecular weight control reactions. A kinetic model based on this reaction scheme was developed to quantitatively describe butadiene conversion and product molecular weight distribution. By including process characteristics, the laboratory (batch) model was transferred to the industrial production process. A correlation function relates product molecular weight to the relevant product property Mooney viscosity. This polymerization model was successfully applied, e.g. to optimize product grade transitions and to maintain high product quality by predicting the influence of process changes.     

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

This paper presents the development of wavelet neural network (WNN) with an improved fruit fly optimization algorithm (IFOA) for the melt index prediction in the industrial propylene polymerization process. The structure, calculation, and prediction process of WNN are proposed, and the improved details of IFOA are introduced, which can enhance the searching efficiency and improve the searching quality over the traditional fruit fly optimization algorithm. Finally, the WNN–IFOA model can obtain the least predicting errors compared with other existing models and shows better generality for the online melt index prediction from the experimental results. Copyright © 2015 John Wiley & Sons, Ltd.