Project aims to establish a new domain within process systems engineering termed as reliable process systems engineering utilizing set-based tools. In an era dominated by digitization, IoT, and burgeoning AI applications, model-based monitoring and control serve as crucial technology for real-time analysis and operation of process systems. These systems are instrumental in producing essential high-end consumer products, such as drugs, plastics, and fuels, pivotal for the sustenance of modern developed societies.

While process systems engineering has made significant strides using model-based control, it faces challenges in dealing with complex, nonlinear, and uncertain systems, aggravated by the influx of extensive data from sensor technologies and the ongoing digital revolution. The primary objective of this project is to tackle these challenges and attain a new milestone by developing innovative methods specifically designed to address these complexities. The project aims to pioneer novel set-based arithmetics, algorithms, and methodologies for constructing mathematical models, ensuring reliable identification of data-based models, accurate parameter and state estimation, and designing efficient off-line and on-line control strategies. Furthermore, it will focus on developing software tools to facilitate the widespread adoption of these methodologies.

To validate the effectiveness of the proposed advancements, simulated industrial conditions will be employed, utilizing pilot/laboratory plants from the domain of chemical engineering in collaboration with industry experts. The ultimate goal is to ensure maximum impact by demonstrating and implementing these developments in practical settings.