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Process characterization and control using multivariate statistical techniques.

机译：使用多元统计技术进行过程表征和控制。

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Fast paced developments in electronic hardware technology have resulted in heavily instrumented chemical plants. Process data from these units are frequently logged on to computers leading to data overload. To cope with these trends, data mining tools that extract useful information from the database have been proposed. These include methods based on simple visualization. multivariate statistical techniques (such as principal components analysis (PCA), partial least squares (PLS) and canonical correlations analysis (CCA)), artificial intelligence (induction or rule based) and neural networks. Recent studies indicate that a new data mining prototype is introduced every three months.; In this thesis, the use of multivariate techniques in the characterization and control of chemical processes (continuous and batch/semibatch) is explored. Utilizing the dimension reduction properties, these tools have long been used for applications related to process monitoring and fault detection in a statistical process control (SPC) framework. In certain situations (e.g. inferential model building), these methods have provided a robust alternative to the ordinary least squares regression procedure. Besides describing the theory and applications of these techniques in such traditional areas, we have investigated their suitability in the modelling and control of dynamic multivariable systems.; A powerful empirical (black-box) identification strategy that provides multivariable state space models (Canonical Variate Analysis, CVA) is reviewed. Extensive simulations are used to establish the superiority of CVA over another popular state space identification algorithm (N4SID). Extension of the CVA method to model a class of nonlinear systems, the Hammerstein structure, is provided.; Identification and control of univariate (single input single output--SISO) processes represents a relatively mature field; it is easily understood and readily implemented. We propose a novel multivariate modelling and controller synthesis strategy that is based on a combination of the PLS technique and the identification/control theory developed for SISO systems. Recognizing that, industrial plants usually operate in the regulatory mode, expressions for the design of multivariable feedforward controllers are developed. To cope with constraints on the process variables, the PLS model has been integrated into the Model Predictive Control framework. The domain of applicability extends to nonlinear systems--the Hammerstein and Wiener models provide motivating examples.; Case studies involving simulations, laboratory experiments and industrial data are included wherever appropriate.
机译：电子硬件技术的快速发展导致了仪器化程度很高的化工厂。这些单元的过程数据经常登录到计算机上，从而导致数据过载。为了应对这些趋势，已经提出了从数据库中提取有用信息的数据挖掘工具。这些包括基于简单可视化的方法。多元统计技术（例如主成分分析（PCA），偏最小二乘（PLS）和规范相关分析（CCA）），人工智能（基于归纳法或基于规则的法）和神经网络。最近的研究表明，每三个月就会引入一个新的数据挖掘原型。本文探讨了多元技术在化学过程（连续过程和间歇/半间歇过程）的表征和控制中的应用。这些工具利用降维特性，长期用于统计过程控制（SPC）框架中与过程监视和故障检测相关的应用程序。在某些情况下（例如推论模型建立），这些方法为普通最小二乘回归程序提供了可靠的替代方法。除了描述这些技术在传统领域中的理论和应用之外，我们还研究了它们在动态多变量系统建模和控制中的适用性。审查了一种强大的经验（黑匣子）识别策略，该策略提供了多变量状态空间模型（规范变量分析，CVA）。广泛的模拟用于建立CVA在另一种流行的状态空间识别算法（N4SID）上的优越性。提供了CVA方法的扩展，以对一类非线性系统Hammerstein结构进行建模。单变量（单输入单输出-SISO）过程的识别和控制代表了一个相对成熟的领域。它易于理解和易于实施。我们提出了一种新颖的多元建模和控制器综合策略，该策略基于PLS技术和为SISO系统开发的识别/控制理论的结合。认识到工业工厂通常以调节模式运行，因此开发了用于设计多变量前馈控制器的表达式。为了应对过程变量的约束，PLS模型已集成到模型预测控制框架中。适用范围扩展到非线性系统-Hammerstein和Wiener模型提供了激励示例。在适当情况下，还包括涉及模拟，实验室实验和工业数据的案例研究。

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