Over the last 10 years, the laboratory of process systems engineering has developed novel mathematical programming techniques for optimisation of energy storage systems, the optimisation of supply chain networks, the optimisation of production and energy planning and scheduling, the design of polygeneration energy systems, and the modelling and optimisation of biological systems. More specifically:

In this area we have developed novel mixed-integer programming models for the efficient scheduling and planning of process industries. Particular emphasis has been placed on the solution of real-world problems from the food, chemicals, batch and discrete manufacturing sector

Recent Representative Publications:

  1. Kopanos, G.M., Puigjaner, L., Georgiadis, M.C. (2012) Efficient mathematical frameworks for detailed production scheduling in food processing industries. Computers & Chemical Engineering, 42, 206-216.
  2. Kyriakidis, T.S., Kopanos, G.M., Georgiadis, M.C. (2011).MILP formulations for single- and multi-mode resource-constrained project scheduling problems. Computers & Chemical Engineering, 36, 369-385
  3. G.M. Kopanos, M.C. Georgiadis, L. Puigjaner (2011). Production Scheduling in Multiproduct Multistage Semicontinuous Food Processes. Ind. Eng. Chem. Res. 50, 6316-6324.
  4. G. Kopanos, L. Puigjaner and M.C. Georgiadis (2010). Production Scheduling and Lot-sizing in Dairy Plants: The Yoghurt Production Line. Industrial & Engineering Chemistry Research, 49, 701-718

We have developed new mathematical programming frameworks for the design and operation of supply chain production and distribution networks under uncertainty, the design of flexible and generalised supply chains, the optimisation of military supply chains and the integration of financial engineering aspects in the design of supply chains.

Recent Representative Publications:

  1. P. Longinidis, G. Kozanidis, M.C. Georgiadis (2015). Integrating Operational Hedging of Exchange Rate Risk in the Optimal Design of Global Supply Chain Networks. Industrial and Engineering Chemistry Research,54, 6311-6325.
  2. M.A. Kalaitzidou, P. Longinidis, Georgiadis, M.C. (2015). Optimal design of closed-loop supply chain networks with multifunctional nodes. Computers and Chemical Engineering, 80, 73-91.
  3. M.A. Kalaitzidou, P. Longinidis, P. Tsiakis, Georgiadis, M.C. (2014). “Optimal design of multiechelon supply chain networks with generalized production and warehousing nodes”, Industrial Engineering Chemistry Research, 53: 13125-13138
  4. P. Logginidis and M.C. Georgiadis (2013). Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty. Computers and Chemical Engineering, 48, 264-279.
  5. M.C. Georgiadis, P. Tsiakis, P. Logginidis and M. Sofioglou (2011). Optimal design of supply chain networks under uncertain transient demand variations. OMEGA, 39, 254-272.

Our contributions in this area include the development of long-term national energy planning models, the operation of CHP-based residential networks and the design of energy supply chains.

Recent Representative Publications:

  1.  N. E Koltsaklis, I. Gioulekas, M.C. Georgiadis (2018). Optimal Scheduling of Interconnected Power Systems. Computers and Chemical Engineering. 111, 164-182.
  2. N. E Koltsaklis, M. Giannakakis, M. C. Georgiadis (2017). Optimal energy planning and scheduling of microgrids. Chemical Engineering Research and Design, DOI: https://doi.org/10.1016/j.cherd.2017.07.030
  3.  A.P. Elekidis, N.E. Koltsaklis, M.C. Georgiadis (2018). An Optimization Approach for the Assessment of the Impact of Transmission Capacity on Electricity Trade and Power Systems Planning. Ind. Eng. Chem. Res., 57, 9766-9778.
  4. Koltsaklis, N.E., Dagoumas, A., Kopanos, G.M. Pistikopoulos, E.N. and Georgiadis, M.C. (2014). A spatial multi-period long-term energy planning model: A case study of the Greek power system. Applied Energy, 115, 456-482
  5. Koltsaklis, N.E., Kopanos, G.M. and Georgiadis, M.C. (2014). Design and Operational Planning of Energy Networks Based on, Combined Heat and Power Units. Industrial & Engineering Chemistry Research, dx.doi.org/10.1021/ie404165c
  6. G.M. Kopanos, M.C. Georgiadis and E.N. Pistikopoulos (2013). Energy Production Planning of a Network of Micro Combined Heat and Power Generators. Applied Energy, 102, 1522-1534
  7. Liu, P., Georgiadis, M.C., Pistikopoulos, E.N. (2013). An energy systems engineering approach for the design and operation of microgrids in residential applications. Chemical Engineering Research and Design, 91, Pages 2054-2069

New Model-based control algorithms and methodologies based on Non-Linear Model predictive Control and Parametric Programming Control techniques with applications in the real-time control and operation of PEM fuel cell systems, separation processes, hydrogen storage equipment, catalytic reactors.

Recent Representative Publications:

  1. C. Ziogou, S. Voutetakis, M.C. Georgiadis, S. Papadopoulou (2018). Model Predictive Control (MPC) Strategies for PEM Fuel Cell Systems – A Comparative Experimental Demonstration. Chemical Engineering Research and Design, https://doi.org/10.1016/j.cherd.2018.01.024
  2. Ziogou, C., Pistikopoulos, E.N., Georgiadis, M.C., Voutetakis, S., Papadopoulou, S. (2013). Empowering the performance of advanced NMPC by multiparametric programming – An application to a PEM fuel cell system. Ind. Eng. Chem. Res., 52, 4863-4873.
  3. C. Ziogou, S. Papadopoulou, S. Voutetakis, M.C. Georgiadis (2013). On-line Nonlinear Model Predictive Control of a PEM Fuel Cell System. Journal of Process Control, 23, 483-492.
  4. C. Panos, C. Kouramas, M.C. Georgiadis, E.N. Pistikopoulos (2012). Modelling and explicit model predictive control for PEM fuel cell systems Chem. Eng. Science, 67, 15-25.
  5. P. Christos, K. Kouramas, M.C. Georgiadis and E.N. Pistikopoulos (2010). Dynamic Optimization and Robust explicit model predictive control of hydrogen storage tanks. To appear in Computers & Chemical Engineering, 34, 1341,1347.

In collaboration with the groups of Prof. Sakis Mantalaris (Department of Biomedical Engineering, Georgia Institute of Technology, USA) and Nicki Panoskaltsis (Department of Hematology and Medical Oncology, Emory University, USA) we have developed novel population-balance models for cell cycles under Acute Myeloid Leukemia (AML) and we have illustrated the use of these models in optimizing chemotherapy schedules.

Recent Representative Publications:

  1. M. Fuentes-Gari, R. Misener, D. Garcıa-Munzer, E. Velliou, M. C. Georgiadis, M. Kostoglou, E. N. Pistikopoulos, N. Panoskaltsis and A. Mantalaris (2015). A mathematical model of subpopulation kinetics for the deconvolution of leukemia heterogeneity. Journal of Royal Society Interfaces, In Press; http://dx.doi.org/10.1098/rsif.2015.0276.
  2. D.G. García Münzer, M. Kostoglou, M.C. Georgiadis, E.N. Pistikopoulos, A. Mantalaris (2015). Cyclin and DNA distributed cell cycle model for GS-NS0 cells. PLOS Computational Biology, 11, Article number e1004062, 28p.
  3. Krieger, A. Panoskaltsis, N., Mantalaris, A., Georgiadis, M.C., Pistikopoulos, E.N. (2014). Modelling and analysis of individualized pharmacokinetics and pharmacodynamics for volatile anesthesia. IEEE Transactions on Biomedical Engineering, 61, Article number 6568924, 25-34
  4. Pefani, E., Panoskaltsis, N., Mantalaris, A., Georgiadis, M.C., Pistikopoulos, E.N. (2013). Design of optimal patient-specific chemotherapy protocols for the treatment of acute myeloid leukemia (AML). Computers and Chemical Engineering, 57, 187-195
  5. Pefani, E., Panoskaltsis, N., Mantalaris, A., Georgiadis, M.C., Pistikopoulos, E.N. (2013). Chemotherapy drug scheduling for the induction treatment of patients with acute myeloid leukemia, IEEE Transactions on Biomedical Engineering, 61, Article number 6777541, Pages 2049-2056.

Advances in this area focus on the multi-scale modeling and simulation of Pressure Swing Adsorption (PSA) processes for gas separation, the design of hybrid separation processes (e.g. PSA-membrane, distillation-membrane), the detailed modeling, simulation and optimization of reactive distillation, crystallization, PEM Fuel Cells, drying, anaerobic digestion and hydrogen storage processes as well as catalytic reactors.

Recent Representative Publications:

  1. G. Nikolaidis, E.S. Kikkinides, M.C. Georgiadis (2017). An Integrated Two-Stage P/VSA Process for Postcombustion CO2 Capture Using Combinations of Adsorbents Zeolite 13X and Mg- MOF-74. Ind. Eng. Chem. Res. DOI: 10.1021/acs.iecr.6b04270.
  2. G. N Nikolaidis, E. S. Kikkinides, Michael C. Georgiadis (2017). A model-based approach for the evaluation of new zeolite 13X-based adsorbents for the efficient post-combustion CO2 capture using P/VSA processes. Chemical Engineering Research and Design, DOI: //doi.org/10.1016/j.cherd.2017.06.016.
  3. G.N. Nikolaidis, E.S. Kikkinides, and Michael C. Georgiadis (2016). Model-Based Approach for the Evaluation of Materials and Processes for Post-Combustion Carbon Dioxide Capture from Flue Gas by PSA/VSA Processes. Industrial and Engineering Chemistry Research, 55, 635−646
  4. G. Pantoleontos, M.C. Georgiadis, E.S. Kikkinides (2012). A heterogeneous dynamic model for the simulation and optimisation of the steam methane reforming reactor . International Journal of Hydrogen Energy, 37, 16346-16358
  5. C. Ziogou, S. Voutetakis, S. Papadopouloua, M.C.Georgiadis (2011). Modeling, simulation and experimental validation of a PEM fuel cell system. Computers and Chemical Engineering, 35, 1886-1900
  6. K. Krokos, D. Nikolic, E.S. Kikkinides, M.C. Georgiadis and A. Stubos (2009). Modeling and optimization of multi-tubular metal hydride beds for efficient hydrogen storage. International Journal of Hydrogen Energy, 34, 9128-9138.
  7. D. Nikolic, A. Giovanoglou, M.C. Georgiadis and E.S. Kikkinides (2009). Optimisation of multibed pressure swing adsorption processes. Industrial Engineering Chemistry Research. 48, 5388 – 5398