Research

Modular Process Intensification Synthesis

Modular process intensification offers the potential to realize step changes in process economics, energy efficiency, and environmental impacts. However, key open question remains on how to systematically and "smartly" generate intensified process systems, ideally also with operability considerations? To address this challenge, we have developed a novel process synthesis strategy based on the Generalized Modular Representation Framework (GMF), leveraging phenomenological building blocks to represent chemical processes in a compact and abstract manner. Innovative process structures can thus be identified without pre-postulation of equipment design. GMF is further integrated with model-based flexibility, safety, and control strategies leading to a holistic framework and software prototype for synthesis of operable and intensified systems.

Selected Publications:
- Pistikopoulos, E. N., Tian, Y. (2022) Synthesis and Operability Strategies for Computer-Aided Modular Process Intensification, Elsevier.
- Tian, Y., Meduri, V., Bindlish, R., Pistikopoulos, E. N. (2022). A Process Intensification synthesis framework for the design of dividing wall column systems, Computers & Chemical Engineering, 160, 107679. 1.

Energy Supply Network Decarbonization

There has been burgeoning interest from the chemical and energy sectors string for the development of clean energy solutions using renewable energy sources, green production routes, carbon capture and utilization techniques, etc. Innovative process design and operation strategies leveraging current process infrastructure are in dire need to start paving the way from fossil fuel to zero-carbon. We propose an operational optimization approach for product scheduling in response to grid and renewable energy source intermittency. A multi-scale energy systems engineering strategy has also been applied to fully integrate reactor-scale modeling, plant-scale synthesis, and network-scale supply chain optimization in a hierarchical manner.

Selected Publications:
- Niziolek, A. M., Onel, O., Tian, Y., Floudas, C. A., Pistikopoulos, E. N. (2018). Municipal Solid Waste to Liquid Transportation Fuels – Part III: An Optimization-Based Nationwide Supply Chain Management Framework, Computers & Chemical Engineering, 116, 468-487.
- Avraamidou, S., Baratsas, S., Tian, Y., Pistikopoulos, E. N. (2020). Circular Economy – A Challenge and an Opportunity for Process Systems Engineering, Computers & Chemical Engineering, 133, 106629.

Fault-Prognostic Design, Control, and Optimization

We aim to address a fundamental research question on how to optimize operation over multiple time scales integrally and proactively, not only taking optimal action to now but also forecasting to future trajectory over substantially large time span. Particularly, we are interested in the integration of design, control and real-time optimization for fault prognosis and mitigation, thus to reduce fault occurrence maximally and inherently instead of posterior amending.

Selected Publications:
- Ali, M., Cai, X., Khan, F. I., Pistikopoulos, E. N., Tian, Y. (2023). Dynamic risk-based process design and operational optimization via multi-parametric programming. Digital Chemical Engineering, 100096.
- Tian, Y., Pappas, I., Burnak, B., Katz, J., Pistikopoulos, E. N. (2021) Simultaneous Design & Control of a Reactive Distillation System – A Parametric Optimization & Control Approach, Chemical Engineering Science, 230, 116232.

Machine Learning Optimization in Limited Data Regime

We aim to distill a data-driven understanding of the process directly from the sparse experimental input-output data, thus “re-discovering” the governing rules complementary to first-principles models and the inevitable modeling assumptions. The resulting machine learning derived model also empowers the rapid screening of near-optimal process designs, before proceeding with rigorous design and analysis via first-principles models.

Selected Publications:
- Masud, M., Tian, Y. Machine Learning in Limited Data Regime – A Literature Survey & Motivating Chemical Example.