Process system engineering
The term Process Systems Engineering (PSE) was first introduced at the first Process Systems Engineering conference in Kyoto in 1982. Robert Sargent (1983), regarded as the “father” of the PSE Domain, defined PSE as, “Process systems engineering is all about the development of systematic techniques for process modeling, design and control.” Four decades of research around the world enriched this domain and extended its perimeter to include research opportunities related to data analytics and mathematical optimization. So, process systems engineering is now a multidisciplinary research area that uses mathematical modeling, data analytics, design, optimization, and control theories to integrate scales and components that describe the behavior of a physicochemical system. PSE provides a scientific foundation and computational tools to handle current and upcoming difficulties in areas like energy, the environment, the "industry of tomorrow," and sustainability.
Our department has a team of dedicated researchers specializing in various aspects of process systems engineering. Our faculty members work in several areas: production and distribution optimization, AI & ML, optimal control, supply chain optimization, and optimization under uncertainties. Here's a brief overview of each research area:
- Production and distribution optimization: This research area focuses on developing strategies to optimize the production and distribution of goods and services. Our faculty members use mathematical models and simulation tools to identify bottlenecks in the production process, improve efficiency, and reduce costs.
- AI & ML: Artificial intelligence (AI) and machine learning (ML) are powerful tools for analyzing complex data sets and making predictions. Our faculty members use these techniques to develop algorithms for optimizing processes and making data-driven decisions.
- Optimal control: This area focuses on developing control strategies to achieve optimal performance in complex systems. Our faculty members use mathematical models and optimization techniques to design controllers that can adapt to changing conditions and improve system performance.
- Supply chain optimization: Supply chains are complex systems that involve producing, distributing, and delivering goods and services. Our faculty members use mathematical models and optimization techniques to improve the efficiency and effectiveness of supply chains, reducing costs and improving customer satisfaction.
- Optimization under uncertainties: Many real-world systems involve uncertainties and variability. Our faculty members develop techniques to optimize systems in the presence of uncertainties, using methods such as stochastic programming and robust optimization.
At our department, we are committed to advancing the field of process systems engineering through cutting-edge research and innovative solutions to real-world problems. Our faculty members collaborate with industry partners and other academic institutions to apply our research to a wide range of applications, from material production to energy and water distribution to transportation and logistics.