Data Generation and Knowledge-based Augmentation: Continuous OME Production

机器学习(ML)的最新进展催生了检测化工过程中异常和故障的新方法。由于缺乏公共访问的实际过程数据，它们通常是用来自动态过程模拟的合成数据进行开发和基准标记的。这种方法有相当大的局限性，因为数据是理想化的，而且许多工厂的异常在没有实验的情况下很难在模拟中预测。项目B2的主要目标是克服这些限制，并提供实际连续化工厂在非异常和异常操作点的大量实验过程数据。研究工厂是申请者技术实验室现有的一家生产合成柴油的微型工厂。它由一个反应器、一个蒸馏装置和回收装置组成。该工厂配备了行业典型的传感器(温度、压力、流量、液位、离线分析)和先进的传感器(用于检测降水和产品颜色变化的摄像头)。所产生的实验数据是研究单位(RU)研究A区异常检测的ML方法的必要基础。然而，生成的实验数据仍然太稀疏，无法训练所开发的深度学习方法。因此，项目B2的另一个主要目标是基于实验工厂数据和力学模型方程中的物理知识提供额外的、伪真实的合成数据。这些方程包括物质和能量的守恒定律，以及描述混合物的化学反应和热力学性质的方程。它们将被实施到工厂的稳态模拟器中。方程式的一部分将被选择和修改，从而在项目A2、A3和A4的过程变量之间产生有保证的关系。与A4和B1项目合作，开发了生成合成数据集的方法。因此，通过比较合成实验数据和实际实验数据，使用ML方法对工厂机械过程模拟的结果进行了修改和增强，其中包括噪声、不可测过程变量和动态内插。为了支持最大似然方法，采用机械Hammerstein模型进行动态内插。生成的实验和合成数据将与项目B1合作，为RU和其他社区收集、存储和传播开放获取的数据。合并后的数据用作异常检测(项目A1)、勘探、解释和可视化(项目A3)的高级ML方法的训练和评估数据。反过来，项目B2将在工厂运营中测试项目A1和A3中开发的方法，并提供宝贵的反馈。

Recent advances in machine learning (ML) gave rise to novel methods for detecting anomalies and faults in chemical processes. Due to a lack of actual process data with public access, they are typically developed and bench-marked with synthetic data from dynamic process simulations. This procedure has considerable limitations since the data is idealized, and many plant anomalies are hardly predictable in simulations without experiments. The primary objective of Project B2 is to overcome these limitations and provide large amounts of experimental process data of an actual continuous chemical plant in non-anomalous and anomalous operation points. The plant of study is an existing mini-plant for the production of synthetic diesel fuels at the applicant's technology lab. It consists of a reactor, a distillation train, and recycles. The plant is equipped with industry-typical sensors (temperature, pressure, flowrate, levels, offline analyses) and advanced sensors (cameras for detecting precipitation and changes in product color). The produced experimental data is the essential base for developing the ML methods for anomaly detection in Research Area A of the Research Unit (RU). However, the generated experimental data is still too sparse for training the developed deep learning methods. Therefore, another major objective of Project B2 is to provide additional, pseudo-authentic synthesized data based on the experimental plant data and physical knowledge in mechanistic model equations. The equations consist of conservation laws for material and energy and equations describing the mixture's chemical reactions and thermodynamic properties. They will be implemented into a steady-state simulator of the plant. Parts of the equations will be selected and modified, yielding guaranteed relationships among process variables for Projects A2, A3, and A4. In collaboration with projects A4 and B1, methods to generate synthetic data sets are developed. Thereby, the results of the mechanistic process simulation of the plant are modified and augmented by noise, non-measure process variables, and dynamic interpolations using ML methods that are trained by comparing synthetic and actual experimental data. For supporting the ML methods, dynamic interpolations are produced using mechanistic Hammerstein models. The generated experimental and synthetic data will be collected, stored, and disseminated with open access in collaboration with Project B1 for the RU and the community beyond. The combined data serves as training and evaluation data for the advanced ML methods of anomaly detection (Project A1), exploration, explanation, and visualization (Project A3). In turn, Project B2 will test the methods developed in Projects A1 and A3 in plant operations and provide precious feedback.