|CPSE Director, Imperial College London
|Professor of Chemical Engineering, Imperial College London
|Reader in Process Systems Engineering, Department of Chemical Engineering, Imperial College London
|Lecturer, Department of Chemical Engineering, Imperial College London
|Research Associate, Chemical Process Engineering Research Institute, Thessaloniki, Greece
|Research Chemical Engineer, Koninklijke Shell Laboratorium, Amsterdam, The Netherlands
|PhD in Chemical Engineering, Carnegie Mellon University, USA. Thesis title: "Systematic procedures to improve process flexibility in retrofit design" (supervisor: Prof. I.E. Grossmann)
|Diploma in Chemical Engineering, Aristotle University of Thessaloniki, Greece
The objective of my research programme is to develop fundamental theory and optimization based methodologies and computational tools that enable process engineers to analyse, design and evaluate process manufacturing systems which are economically attractive, energy efficient and environmentally benign, while at the same time exhibit good performance characteristics like flexibility, controllability, robustness, reliability and safety. Our research involves three main strands:
Process synthesis and the environment: Here we are concerned with the development of process integration and pollution prevention strategies for the design and operation of plant-wide sustainable processes. Novel process synthesis modelling concepts are explored together with life-cycle and environmental impact assessment aspects, leading to new designs which feature step-change improvements in energy efficiency, waste minimization and process sustainability.
Integration of operability objectives in process design and operation: Our work here has centred on the development and implementation of novel analytical tools to simultaneously assess process flexibility, controllability, robustness, reliability and availability of complex process manufacturing systems and the systematic incorporation of these tools at the design and operational level.
Process optimization under uncertainty - theory, algorithms and applications: Here we develop the fundamental underlying mathematical theory, numerical algorithms and efficient computational tools for the solution of multi-parametric and stochastic mixed integer optimization problems, which arise in the context of the work described in the other two research strands.
et al., 2012, Modelling and explicit model predictive control for PEM fuel cell systems, Chemical Engineering Science, Vol:67, ISSN:0009-2509, Pages:15-25
et al., 2011, Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming, Automatica, Vol:47, ISSN:0005-1098, Pages:1638-1645
Liu P, Pistikopoulos EN, Li Z, 2010, A Multi-Objective Optimization Approach to Polygeneration Energy Systems Design, AICHE Journal, Vol:56, ISSN:0001-1541, Pages:1218-1234
Dua P, Doyle FJ, Pistikopoulos EN, 2009, Multi-objective blood glucose control for type 1 diabetes, Medical & Biological Engineering & Computing, Vol:47, ISSN:0140-0118, Pages:343-352