In this research, we propose a novel data-centric way of optimizing a catalytic cracking unit. We first design a soft sensor to predict catalyst saturation levels within a Fluid Catalytic Cracking Unit (FCCU). To achieve this, we implement an established method and combine it with modern algorithms for accurate and robust results. The input for this model is data from a number of sensors throughout the refinery, combined with laboratory data. Catalyst saturation level is measured by way of manual refraction analysis and lookup tables. These manual measurements were combined with laboratory data to provide training input for our soft sensor models. Subsequently, we utilize this new soft sensor model in an input mix optimization in order to continuously optimize the use of the catalyst within the FCCU. This model leads to a higher product yield, less catalyst consumption, and a more efficient process. This proposed optimization pipeline can be introduced as smart process control tying into the development towards Industry 4.0.

在这项研究中，我们提出了一种新的以数据为中心的优化催化裂化装置的方法。我们首先设计一个软传感器来预测流体催化裂化装置（FCCU）中催化剂的饱和度。为了实现这一目标，我们实施了一种既定的方法，并将其与现代算法相结合，以获取准确而可靠的结果。该模型的输入是来自整个炼油厂的多个传感器的数据以及实验室数据。催化剂的饱和度是通过手动折射分析和查找表测量的。这些手动测量与实验室数据相结合，为我们的软传感器模型提供了培训输入。随后，我们在输入混合优化中利用了这种新的软传感器模型，以便不断优化FCCU中催化剂的使用。这种模式可以提高产品产量，减少催化剂消耗，提高工艺效率。可以将这种建议的优化管道作为智能过程控制引入到工业4.0的开发中。