Computational applications using data driven modeling in process Systems: A review

Abstract

Modeling and optimization of various processes enable more efficient operations and better planning activities for new process developments. With recent advances in computing power, data driven models, such as Machine Learning (ML), are being extensively applied in many areas of chemical engineering topics. Compared to mechanistic models that often do not reflect the realities of field conditions and the high costs associated with them, these techniques are relatively easier to implement. Data-driven models generated via ML techniques can be regularly updated, thereby giving an accurate picture of the system. Due to these inherent benefits, such tools are increasingly gaining a lot of traction in process systems. Even though data-driven models have the potential to be used as a replacement for traditional optimization tools that can be implemented in various process industries, it was found that applications of such models in process systems were quite limited to reactor modeling, molecular design, as well as safety, and relatability. The challenge still exists for data-driven modeling due to the lack of specialized tools tailored for macro systems and scale up. Most datasets were found to be derived from experimental studies which are limited in nature and only fit into microsystems. Hence, this paper provides a state of the art review on recent applications for data driven modeling research in process systems, and discusses the prominent challenges and future outlooks that were observed.

Keywords

• Modeling
• Optimization
• Machine learning
• Data-driven modeling