A One-Shot Learning Framework to Model Process Systems

Abstract

In the era of Big Data, the utilization of data-driven analytics for process engineering systems is rising exponentially. The abundance of data from industrial sensors and various documentation logs have served as a strong basis for such analysis. Nevertheless, there are some critical data in an industry that simply rare and uncommon due to certain processing constraints or confidentiality. Such constraints may include economic costs for data acquisition, the complexity for data collection, the needs for qualified personnel and many other unforeseeable problems. Due to conventional data-driven approach requiring a large volume of data, such rare but critical data cannot be properly utilized. For this aspect, we proposed a one-shot learning framework to model process systems. The novel framework utilizes prior knowledge from multi-sourced data to learn the conditional relationships of critical variables within the process. By utilizing prior generic knowledge of the system, one-shot learning can provide a better representation of the prediction space when acting as a data-driven black-box model. A combined heat and power (CHP) system is used as the case study for one-shot learning modelling which a mean squared error of 0.00616 was achieved. The efficient use of data within this framework is expected to be beneficial when modelling under high-priority and low data availability.