Qualitative Modeling and Machine Learning Applied to the Real-Time Estimation of Chemical Process Dynamics

动态变化过程的控制，例如 以分批或半分批模式操作的方法是复杂的，因为 用于实施控制策略的数学模型不 有分析的解决方案。 数值解，如递归 有时使用最小二乘（RLS）估计，但这些不是 易于实施的化学处理系统， 顺序、时变、非线性或全部三种。 修改RLS 结合特定的系统特性 成功地导致具有有限适用性的解决方案。 在这项工作中，PI计划生成自动控制算法 使用基于将干扰分类为以下三种之一的RLS 并相应地规划控制行动。 所得 方法具有普遍适用性。 有三种基本类型的干扰： 第一章 系统状态的缓慢漂移-对于这种系统， 可接受的操作条件范围， 允许使用线性化模型，但该过程 将最终移出该频带（真实的示例 是由催化剂衰变、结垢和结渣引起的， 等）。 （二） 短时间的稳态运行-这 导致线性动态模型不可操作。 第三章 设定点突然变化-紧接着 这种干扰线性模型将是严重的， 不准确的（真实的例子发生在进料批次改变时， 例如，一个或多个）。 PI计划设计他的算法， 所有的干扰都是一种或多种干扰的组合。 的 然后，方法涉及对过程行为的连续诊断， 更新控制操作。

The control of dynamically changing processes, for example those operated in a batch or semi-batch mode, are complex because the mathematical models used to implement the control strategies do not have analytical solutions. Numerical solutions such as recursive least-squares (RLS) estimation are sometimes used, but these are not easy to implement for chemical processing systems which may be high order, time-varying, non-linear or all three. Modifying RLS incorporating specific system characteristics has been used successfully resulting in solutions that have limited applicability. In this work, the PI plans to generate automatic control algorithms using RLS that are based on classifying disturbances into one of three categories and planning control action accordingly. The resulting method is expected to have general applicability. There are three basic types of disturbances: 1) Slow drift in system state - for such systems there is an acceptable band of operating conditions which will permit the use of linearized models, but the process will ultimately move out of this band (real examples are caused by catalyst decay, fouling and slagging, etc.). 2) Short periods of steady state operation - this causes linear dynamic models to be inoperable. 3) Sudden change in setpoint - immediately following such disturbances linear models will be grossly inaccurate (real examples occur when feed lots are changed, for example). The PI plans to design his algorithm so that it will decompose all disturbances into combinations of one or more of these. The method then involves continuous diagnosis of process behavior to update control action.