Nikolaos A. Diangelakis
Nikolaos Diangelakis is a postdoctoral research associate at Texas A&M University and Texas A&M Energy Institute. He holds a PhD from Imperial College London, under the supervision of Prof. E. N. Pistikopoulos and has been a member of the "Multi-parametric Optimization and Control Group" since late 2011, when he was pursuing his M.Sc. on Advanced Chemical Engineering. He earned his bachelor degree in 2011 from the National Technical University of Athens (NTUA).
His research interests are on the area of optimal receding horizon strategies. More specifically, his research focuses on the development of control and scheduling policies of chemical and energy processes while simultaneously optimizing their design. For that purpose, Nikos is investigating novel solution methods for classes of non-linear and robust multi-parametric optimization programming problems. Furthermore, Nikos is one of the current main developers of the PARametric Optimization and Control (PAROC) platform and the Parametric OPtimization (POP) toolbox. In 2016 Nikos was chosen as one of five participants in the "Distinguished Junior Researcher Seminars" in Northwestern University, organized by Prof. Fengqi You. He is the coauthor of 12 peer reviewed articles, 9 conference papers, 3 book chapters and is currently working on a book on "Multi-parametric Optimization and Control".
- Multi-parametric Optimization and Control; Wiley-VCH; to appear in 2019.
- Explicit (Offline) Optimization for MPC. In Handbook of Model Predictive Control; Rakovic, S., Levine, W., Eds.; Control Engineering; Birkhäuser, Cham, 2019.
- A multi-objective optimization for the design and operation of a hydrogen network for transportation fuel. Chemical Engineering Research and Design 2018, 131, 279-292.
- Natural Gas based SOFC in Distributed Electricity Generation: Modeling and Control. In Natural Gas Processing from Midstream to Downstream; Elbashir, N. O., El-Halwagi, M. M., Hall, K. R., Economou, I., Eds.; Wiley, 2018.
- Simultaneous Process Scheduling and Control: A Multiparametric Programming Based Approach. Industrial & Engineering Chemistry Research 2018, 57 (11), 3963-3976.
- Model Approximation in Multiparametric Optimization and Control - A Computational Study. 13th International Symposium on Process Systems Engineering (PSE 2018); Elsevier, 2018; pp 655-660.
- Integration of Design, Scheduling, and Control of Combined Heat and Power Systems: A Multiparametric Programming Based Approach. 13th International Symposium on Process Systems Engineering (PSE 2018); Elsevier, 2018; pp 2203-2208.
- On multiparametric/explicit NMPC for Quadratically Constrained Problems. 6th IFAC Conference on Nonlinear Model Predictive Control; Elsevier, 2018; pp 400-405.
- On unbounded and binary parameters in multi-parametric programming: Applications to mixed-integer bilevel optimization and duality theory. Journal of Global Optimization 2017, 69 (3), 587-606.
- Explicit Model Predictive Control: A connected-graph approach. Automatica 2017, 76, 103-112.
- A multi-scale energy systems engineering approach to residential combined heat and power systems. Computers & Chemical Engineering 2017, 102, 128-138.
- A multi-parametric programming approach for the simultaneous process scheduling and control - Application to a domestic cogeneration unit. Foundations of Computer Aided Process Operations / Chemical Process Control; 2017.
- Mixed Integer Bilevel Optimization through Multi-parametric Programming. Foundations of Computer Aided Process Operations / Chemical Process Control; 2017.
- Modelling, Design and Control Optimization of a Residential Scale CHP System. In Advances in Energy Systems Engineering; Kopanos, G. M., Liu, P., Georgiadis, M. C., Eds.; Springer Berlin Heidelberg, 2017.
- Process Design and Control optimization: A simultaneous approach by multi-parametric programming. AIChE Journal 2017, 63 (11), 4827-4846.
- Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization. 27th European Symposium on Computer-Aided Process Engineering (ESCAPE-27); Elsevier, 2017; pp 1867-1872.
- Decentralized Multiparametric Model Predictive Control for Domestic Combined Heat and Power Systems. Industrial & Engineering Chemistry Research 2016, 55 (12), 3313-3326.
- Towards the integration of process design, control and scheduling: Are we getting closer?. Computers & Chemical Engineering 2016, 91, 85-92.
- POP - Parametric Optimization Toolbox. Industrial & Engineering Chemistry Research 2016, 55 (33), 8979-8991.
- Explicit MPC in real-world applications: The PAROC framework. American Control Conference (ACC); 2016; pp 913-918.
- On multi-parametric programming and its applications in process systems engineering. Chemical Engineering Research and Design 2016, 116, 61-82.
- Design, scheduling and control: A simultaneous approach by multi-parametric programming. Computing and Systems Technology Division 2016 - Core Programming Area at the 2016 AIChE Annual Meeting; AIChE, 2016; pp 145-147.
- PAROC-An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science 2015, 136, 115-138.
- A Decentralised Multi-parametric Model Predictive Control Study for a Domestic Heat and Power Cogeneration System. 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering; Elsevier, 2015;, Computer Aided Chemical Engineering 37 pp 1499-1504.
- Towards the integration of process design, control and scheduling: Are we getting closer?. 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering; Elsevier, 2015;, Computer Aided Chemical Engineering 37 pp 41-48.
- Design optimization of an internal combustion engine powered CHP system for residential scale application. Computational Management Science 2014, 11 (3), 237-266.
- A framework for design and control optimisation. Application on a CHP system. Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design; Elsevier, 2014;, Computer Aided Chemical Engineering 34 pp 765-770.