PUERTOie el Mercado de la Innovación y Emprendimiento

Educación

Charlas en Optimización con el Ph.D. Ignacio E. Grossmann

Te invitamos a participar de las Charlas en Optimización que el Ph.D. Ignacio E. Grossmann realizará en la Universidad Técnica Federico Santa María el día martes 16 de agosto de 2022.

Ph.D. Ignacio E. Grossmann es profesor del Departamento de Ingeniería Química y director del Center of Advanced Process Decision-making (CAPD, consorcio industrial que involucra a más de 20 empresas petroleras, químicas, de ingeniería y de software) en Carnegie Mellon University, Pitssburgh, USA.

Es miembro de la National Academy of Engineering y editor asociado de AIChE Journal. A lo largo de su carrera, ha recibidos los siguientes premios AIChE:
– Computer in Chemical Engineering.
– William H. Walker por excelencia en publicaciones.
– Warren Lewis por excelencia en educación.
– Research Excellence in Sustainable Engineering.
– Founder Award for Outstanding Contributions en el campo de la Ingeniería Química.

En 2019 fue uno de los científicos más citados en Computer Science and Electronics: 53 a nivel mundial y 38 en USA. Es autor de más de 600 publicaciones, varias monografías sobre diseño de casos de estudio, de los libros “Advanced Optimization in Process Systems Engineering” y “Systematic Methods of Chemical Process Design” junto a Larry Biegler y Art Westerberg.

Charla “Global Optimization of Nonconvex Nonlinear Generalized Disjunctive Programs”Martes 16 de agosto, de 11.00 a 12.30 horas.
Abstract:
Inspired by pioneering work of Egon Balas in disjunctive programming, we address in this talk the global optimization of nonconvex nonlinear generalized disjunctive programming (GDP) problems that include for instance bilinear, concave and linear fractional terms. In order to solve these nonconvex problems a convex nonlinear GDP relaxation is obtained by using suitable convex envelopes for the nonconvex terms. In order to predict tighter lower bounds to the global optimum we consider a sequence of basic steps for the convex relaxation that take a disjunctive set to another one with fewer conjuncts. We show that the strength of these bounds increases as the number of conjuncts decreases leading to a hierarchy of relaxations. Based on this procedure for strengthening lower bounds, we describe two major solution methods for globally optimizing nonconvex GPD problems. One relies on a disjunctive branch and bound algorithm that makes use of bound contraction, logic inference and a spatial branch and bound search.  The other solution method relies on a logic-based outer-approximation algorithm that involves the solution of mixed-integer linear programming master problems and nonlinear programming subproblems for which new cuts are proposed, as well as a two-stage partition. A number of basic theoretical properties are proved for the proposed methods, and we illustrate the application of these methods in the global optimization of several process systems to demonstrate the computational savings that can be achieved with the tighter lower bounds.

Charla “Optimization of Power Systems Infrastructure Planning with High Renewables Penetration»
Martes 16 de agosto, de 14.30 a 16.00 horas.
Abstract:
With recent trends in decarbonization, the optimization of electric power systems is receiving increased attention. We consider in this talk the long-term planning of electric power infrastructures involving coal, natural gas and nuclear power with high renewable penetration (wind, solar) . We propose a multi-period mixed-integer linear programming (MILP) model that incorporates both the investment decisions on the generating units, storage units, and transmission lines, and short-term unit commitment decisions to capture the variations of the renewables. To make the large-scale MILP model tractable, we first propose several spatial and temporal aggregation schemes. Next, we adapt the Benders decomposition algorithm and the nested Benders decomposition algorithm to solve the problem efficiently. We show that these algorithms can be extended to the case of uncertainty in power demands. We also investigate several different algorithms to select the representative days. Case studies of the ERCOT, the independent system operator in Texas, are provided to demonstrate the capabilities of the proposed approaches. Finally, we also consider the explicit incorporation of reliability to account for the failure of power generators.

¿Dónde? Sala de Aprendizaje Activo del Departamento de Industrias, ubicada en el subterráneo del Edificio F1, Departamento de Industrias, Campus Casa Central Valparaíso USM.

¿Modalidad? Presencial.

¡Te esperamos!

Inscribete Aquí!
Dudas o consultas al correo electrónico: comunicaciones.industrias@usm.cl

Deja una respuesta