Research on Real-time Prediction and Optimization Techniques for Refining Units Using Big Data Technology: Applications
Thread Content
Research Project on Real-time Prediction and Optimization of Refining Units Using Big Data Technology: Are there any existing examples of applications, or potential application areas, for this technology in hydrocracking units? Can it automatically adjust the PID or prevent misoperations to guide parameter adjustment? ? ? ? ? Thank you: P
Currently, big data technology already has some application precedents in the real-time prediction and optimization of refining units. Among them, these technologies can also be applied to hydrocracking units. Specifically, big data technology can predict potential future scenarios of a device by collecting and processing real-time monitoring data from it, and make corresponding optimization adjustments. For example, by predicting the trends in parameters such as furnace tube temperature, pressure, and flow rate, the PID controller can be adjusted in a timely manner to maintain stable operation. Furthermore, big data technology can also be used to predict aspects such as raw material quality, product quality, and equipment service life, thereby guiding parameter adjustments or equipment maintenance. In summary, big data technology is widely used in the real-time prediction and optimization of refining units, including hydrocracking units. By analyzing and processing real-time monitoring data, it is possible to **improve the safety, stability, and efficiency of the device. .
Our company has carried out automated optimization upgrades using big data for multiple units in central state-owned enterprises, including those used for vacuum distillation, hydroprocessing, and reforming. We have numerous successful practical cases to demonstrate this. Compared with the advanced control projects developed by Zhejiang University’s Zhongkong Group, this approach results in reduced energy consumption, higher yields, prevention of operational errors, ensured operation of equipment within safe parameters, lower investment costs, and good economic benefits, earning high praise from customers
The interface of Zhejiang University’s Zhongkong system is not user-friendly; the labels for curves are displayed in too small a size. Moreover, after configuration, users’ custom curve groups cannot be saved. Yokogawa’s system is better, although it comes with certain risks