Le 01/07/2019 par ramdanec :

Suite à un désistement, nous avons une opportunité de thèse. Le sujet de thèse est issue d?une collaboration interdisciplinaire entre deux laboratoires à Nancy: LORIA (Informatique) et LRGP (Génie des procédés). Vous trouverez ci-dessous une brève description du sujet. Le poste sera encore ouvert pendant quelques jours. Si vous êtes intéressé, n''hésitez pas à me contacter pour plus d''informations. ------------------------------------------------------------------------------------------------- Due to a withdrawal, we have an opportunity for a PhD position. The thesis is part of an interdisciplinary project between two laboratories: LORIA (Computer Science), LRGP (Process Engineering). Below you find a short description of the subject. The position will be still open for few days. If you are interested, do not hesitate to contact me for more information --------------------------------------------------------------------------------------------------- Bernardetta Addis bernardetta.addis@loria.fr ---------------------------------------------------------------------------------------------------- ?Process synthesis is the assembly and interconnection of units into a process network ? involving different physical and chemical phenomena to transform raw material and energy inputs into desired outputs ? with the goal of optimizing economic, environmental, and/or social objectives? (from [1]). In general processes, the overall system can be composed of different interconnected subsystems such as: distillation columns, chemical reactors, or a membrane process. From an application point of view, process synthesis offers an interesting challenge: first of all, for their economical and environmental impact on industrial applications (CO2 capture, air/water purification, etc), second, even if there exist academic contributions on optimization-based process design, such contributions are not integrated in industrial practices, where simulation based methods and ?by-hand? sensitivity analysis are still the main tool ([1]), this is a clear indicator that current methods are not enough advanced to give a convincing answer. Tackling process synthesis requires a deep knowledge both in continuous and combinatorics optimization, since even fixing the integer values (design/configuration) the problem obtained is a hard nonlinear non convex optimization problem, whereas choosing the configuration asks for solving a not naive combinatorial problem. Indeed, the mathematical formulation of process synthesis leads to a Mixed Integer Nonlinear Programming problem (MINLP), that is one of the most recent topic for the optimization community [2]. It is well known that MINLP is extremely challenging and no general solver is able to manage large size general problems [3]. Therefore, ? ?ad hoc? solution strategies combining global and discrete optimization methods and integrating problem specific information must be designed. The aim of the thesis is to define an exact algorithm for solving a possibly simplified version of process synthesis optimization. This requires the study of innovative bounding techniques for the specific non convexities of this class of problems and the definition of ad hoc exploration strategies for the discrete space that exploit the knowledge of the physical process behind. This is a challenging research objective on its own and it will constitute a building block of a tool for solving more realistic process synthesis problems derived by industrial application cases. The thesis is part of a long term research project between the LORIA, the LRGP laboratory (Nancy) and the University of Rome ?Tor Vergata?. [1] Qi Chen and I.E. Grossmann. Recent developments and challenges in optimization-based process synthesis. Annual Review of Chemical and Biomolecular Engineering, 8(1):249?283, 2017. [2] Christodoulos A. Floudas. Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications. Oxford University Press, New York, 1995. [3] Pietro Belotti, Christian Kirches, Sven Leyffer, Jeff Linderoth, Jim Luedtke, and Ashutosh Mahajan. Mixed-Integer Nonlinear Optimization. Mathematics and Computer Science Division, Preprint ANL/MCS-P3060-1112, 2012.