I-Corps: Process Monitoring and Diagnosis Software for Decarbonizing Industrial Distillation

The broader impact/commercial potential of this I-Corps project is to improve the energy efficiency and carbon footprint of distillation facilities to facilitate decarbonization in chemical and refining industries, the top two energy use processing industries consuming almost half of the U.S. manufacturing sector’s primary energy and emitting half of its greenhouse gases. By de-risking distillation system operation at its optimal operating region, this software tool is expected to reduce energy requirement and greenhouse gas emissions. Some of the well-known industrial processes that benefit from this software tool include crude oil fractionation, natural gas separation, amine regeneration from carbon dioxide capture, air separation, liquors production, etc. Furthermore, by reducing operating costs, improving production throughput, and reducing safety risks, this software tool enhances the economic competitiveness of U.S. chemical plants and refineries, and improves health, safety, and environmental wellness of nearby communities. The commercialization of this software will also help train the next-generation workforce in chemical and refining industries, including plant operators, process engineers, and plant managers, to use digital tools for plant data analytics. This I-Corps project is based on the development of a data analytics technology for real-time process monitoring and fault diagnosis of industrial distillation systems. By strategically learning from historical operating data, this software tool enables online monitoring of multiple nonparametric, heterogeneous process data streams continuously produced from sensors, as well as accurate fault classification from various possible fault scenarios. This is accomplished by holistic integration of online process monitoring and fault diagnosis in a new hierarchical architecture. Compared to existing general purpose process monitoring tools that attempt to accomplish both tasks altogether, this hierarchical architecture offers superior fault detection and diagnosis performance in terms of fault detection speed, fault classification accuracy, and robustness to handle multiple plant operation scenarios. This smart distillation monitoring software can also be seamlessly integrated with existing plant control and supervisory systems already in place, which is favorable for retrofitting purposes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.