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My name is Lucas Francisco dos Santos.
I am a 26-year-old, enthusiastic, young reasearcher, who is trying to contribute to science and technology in the topic of optimization of complex systems.
I am most interested in solving optimization problems with black-box, simulated models embedded using surrogate-based approaches.
I really believe that this research topic can improve robust and multi-objective engineering decisions aimed to a more sustainable and technological world.
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Python, MATLAB, GAMS, C++, LaTeX, HTML, CSS
Aspen HYSYS, DWSIM, XCOS
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dwsimopt: DWSIM simulation optimization with Python
The dwsimopt is a Python library that automates DWSIM chemical process simulations for optimization.
The simulations dlls are embedded in the programming environment so that it can be accessd and modified by the optimization algorithms.
Check out the code in the in the github repository
or install the dwsimopt package with pip from the PyPI repository under MIT license.
Although very efficient to describe in details complex systems that would otherwise have to be simplified or approximated, black-box process simulators lack the symbolic formulation of the process model equations
and the analytical derivatives that are useful for optimization, for example [1].
The optimization models that require simulations to calculate the objective function and/or constraints are often referred to as
simulation optimization problem [2]. A simplified version of this class of problems can be described as to find an
that solves globally the following constrained problem
in which the objective function
and constraints
are somewhat expensive to calculate, slightly noisy, and black-box functions.
dwsimopt via pip:
pip install dwsimoptor clone and install it from the github repository:
git clone https://github.com/lf-santos/dwsimopt.gitMake sure you have the open-source chemical process simulator DWSIM v7.0 installed into your machine. Navegate throught the jupyter notebook examples. Use the
cd dwsimopt
python setup.py install
SimulationOptimization class to embed your DMSWIM simulation into Python.
Add degrees of freedom, objective function and constraints from your simulation optimization problem with the py2dwisim python-dwsim data exchange interface.
Solve the problem with a suitable optimization solver (surrogate-based optimization or global optimization meta-heuristics recommended).
Data-driven models replace the expensive-to-evaluate, black-box, simulator-dependent objective and constraints to assist the search of solution to the optimization problem.
Learn more!
Meta-heuristics, derivative-free methods, or gradient-based optimization is applied directly to the black-box, simulation optimization problem.
Learn more!
Superstructure-derived MINLP models are solved with meta-heurics or mathematical programming to acheive optimal work and heat integration.
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Model and simulation of microstructure formation in polymers via mass-transfer driven spinodal decomposition in ternary polymer solution.
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