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<img src="fig/graphical_abstract_appEnergy2021.png" alt="Surrogate-based simulation optimization">
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<h1 class="display-2"><span>Surrogate-based Simulation Optimization</span></h1>
<h3><span>Using data-driven models to assist the optimization of black-box simulations.</span></h3>
<a href="#s"><button type="button" target="_blank" class="btn btn-outline-dark btn-lg">Learn more!</button></a>
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<h1 class="display-2"><span>Direct Simulation Optimization</span></h1>
<h3><span>Using meta-heuristics, derivative-free methods, or gradient-based optimization to problems with black-box simulation embedded.</span></h3>
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<h3><span><code class="prettify">dwsimopt</code>: Open-source Python package for DWSIM flowsheet optimization</span></h3>
<a href="https://github.com/lf-santos/dwsimopt" target="_blank"><button type="button" class="btn btn-outline-dark btn-lg">Learn more!</span></button></a>
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<h1 class="display-2"><span>Multi-objective Simulation Optimization</h1>
<h3><span>Finding trade-off solutions to competing objectives in black-box simulations.</span></h3>
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<p class="lead">We work on problems in economics theory and their application. In general, our work aims at contributing to the field of computer science and economics</p>
<p class="lead">The website is under continuous building and will be updating accordingly</p>
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<h1 class="display-4">About Meliora Nexus Labs </h1>
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<p class="lead">Thanks for stopping by!
At Meliora Nexus Labs, we are dedicated to pushing the boundaries of knowledge and fostering innovation in the fields of economics and computer science.
Our mission is to drive positive change and make a meaningful impact on society through rigorous research, interdisciplinary collaboration, and the pursuit of excellence.
<br><br>
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<li>
PhD in Chemical Engineering, Aug 2019 - Present
<br />State University of Maringá <br />University of Alicante (Double
Degree)
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<li>
MSc in Chemical Engineering, Aug 2018 - Aug 2019
<br />State University of Maringá
</li>
<li>
BSc in Chemical Engineering, Feb 2013 - Jul 2018
<br />State University of Maringá <br />University of California,
Santa Barbara (One-year sandwhich period)
</li>
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<li>Machine Learning in strategic environments and for socio-economic development</li>
<li>Game Theory</li>
<li>Computational Economics</li>
<li>Algorithmic Game Theory (Algorithmic Economics)</li>
<li>Development Economics and Policy Research</li>
<li>Mathematical programming</li>
<li>Signal Processing</li>
<li>Theoretical Mathematics</li>
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<p>Aspen HYSYS, DWSIM, XCOS</p>
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<h4 style="font-style: italic;" style="padding-left: 1rem;"><a href="https://github.com/lf-santos/dwsimopt" target="blank"><code style="color: magenta; font-style: normal;">dwsimopt</code></a>: DWSIM simulation optimization with Python</h4>
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The <a href="https://github.com/lf-santos/dwsimopt" target="blank"><code style="color: magenta;">dwsimopt</code></a> 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 <a href="https://github.com/lf-santos/dwsimopt" target="blank">github repository</a>
or install the <code>dwsimopt</code> package with <code>pip</code> from the <a href="https://pypi.org/project/dwsimopt/" target="blank">PyPI repository</a> under MIT license.
</p>
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<a href="https://github.com/lf-santos/dwsimopt" target="_blank">
<img src="/fig/vid_pyDWSIMopt_x4.gif" alt="Gif of dwsimopt in action" style="width: 100%;">
</a>
</div>
<h4>Mathematical Background</h4>
<p style="padding-left: 2rem; padding-right: 2rem;">
-->
<!--
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 <a href="https://doi.org/10.1016/j.ces.2021.116699" target="_blank">[1]</a>.
The use of simulation may also introduce noise to the calculations due to convergence and approximations of numerical methods, which can
jeopardize the calculation of accurate approximate derivatives and, therefore, the use of gradient-based optimization methods directly <a href="https://doi.org/10.1002/aic.11579" target="_blank">[1]</a>.
Also, the lack of analytical formulations of the optimization problem prevents the derivation of rigorous upper and lower bounds of the functions that are used for deterministic global optimization
<a href="https://doi.org/10.1007/s11590-016-1028-2" target="_blank">[2]</a>. In that sense,
The optimization models that require simulations to calculate the objective function and/or constraints are often referred to as
simulation optimization problem <a href="https://doi.org/10.1007/s10479-015-2019-x" target="_blank">[2]</a>. A simplified version of this class of problems can be described as to find an
<img src="https://latex.codecogs.com/svg.latex?\mathbf{x}^*\in\mathbb{R}^n" title="\mathbf{x}^*\in\mathbb{R}^n" style="max-height: 0.8em; vertical-align:baseline;"/> that solves globally the following constrained problem
<br>
<img src="https://latex.codecogs.com/svg.latex?\begin{align*}&space;\min_{\mathbf{x}\in\mathcal{D}}\&space;\&space;\&space;&f(\mathbf{x})\\&space;\text{s.t.}\&space;\&space;\&space;&\mathbf{g}(\mathbf{x})\le&space;0&space;\end{align*}"
title="\begin{align*} \min_{\mathbf{x}\in\mathcal{D}}\ \ \ &f(\mathbf{x})\\ \text{s.t.}\ \ \ &\mathbf{g}(\mathbf{x})\le 0 \end{align*}"
style="max-height: 5.5rem; padding: 1rem"/>
<br>
in which the objective function <img src="https://latex.codecogs.com/svg.latex?f:\mathbb{R}^n\mapsto\mathbb{R}" style="max-height: 1em;"/>
and constraints <img src="https://latex.codecogs.com/svg.latex?g:\mathbb{R}^n\mapsto\mathbb{R}^q" style="max-height: 1em;"/> are somewhat expensive to calculate, slightly noisy, and black-box functions.
</p>
<br clear="all">
<h4>Installation</h4>
<span style="padding-left: 2rem; padding-right: 2rem;">
Install the latest version of <code>dwsimopt</code> via pip:
<pre class="brush: python" style="padding: 0.5rem; margin: 1rem; border-color: gray; border-width: 0.1rem; border-style: solid; border-radius: 5px;">pip install dwsimopt</pre>
or clone and install it from the github repository:
<pre class="brush: python" style="padding: 0.5rem; margin: 1rem; border-color: gray; border-width: 0.1rem; border-style: solid; border-radius: 5px;">git clone https://github.com/lf-santos/dwsimopt.git<br>cd dwsimopt<br>python setup.py install</pre>
Make sure you have the open-source chemical process simulator <a href="https://dwsim.inforside.com.br/new/" target="blank">DWSIM v7.0</a> installed into your machine.
Navegate throught the jupyter notebook examples. Use the <code>SimulationOptimization</code> class to embed your <code>DMSWIM</code> simulation into Python.
Add degrees of freedom, objective function and constraints from your simulation optimization problem with the <code>py2dwisim</code> python-dwsim data exchange interface.
Solve the problem with a suitable optimization solver (surrogate-based optimization or global optimization meta-heuristics recommended).
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Analysis of employment demand forecasting (African economies)
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>Mass-transfer driven spinodal decomposition in a ternary
polymer solution</a
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DR Tree, LF Dos Santos, CB Wilson, TR Scott, JU Garcia, GH
Fredrickson
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Soft matter 15 (23), 4614-4628, 2019
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<td class="pub_tab_col">
<a
href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=4020809759866515926"
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>26</a
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<td class="pub_tab_col0">
<a
href="https://www.sciencedirect.com/science/article/abs/pii/S0306261919321294"
target="_blank"
>Synthesis and optimization of work and heat exchange networks
using an MINLP model with a reduced number of decision
variables</a
>
<p>LF Santos, CBB Costa, JA Caballero, MASS Ravagnani</p>
<p>Applied Energy 262, 114441, 2020</p>
</td>
<td class="pub_tab_col">
<a
href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=3615921409250587934"
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<td class="pub_tab_col">2020</td>
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<td class="pub_tab_col0">
<a
href="https://www.sciencedirect.com/science/article/pii/S0009250921002645"
target="_blank"
>Kriging-assisted constrained optimization of single-mixed
refrigerant natural gas liquefaction process</a
>
<p>LF Santos, CBB Costa, JA Caballero, MASS Ravagnani</p>
<p>Chemical Engineering Science 241, 116699, 2021</p>
</td>
<td class="pub_tab_col">
<a
href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=18376340409791161180"
target="_blank"
>6</a
>
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2021
</td>
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<td class="pub_tab_col0">
<a
href="https://link.springer.com/article/10.1007/s43153-021-00111-8"
target="_blank"
>Design and optimization of energy-efficient single mixed
refrigerant LNG liquefaction process</a
>
<p>LF Santos, CBB Costa, JA Caballero, MASS Ravagnani</p>
<p>Brazilian Journal of Chemical Engineering, 1-14, 2021</p>
</td>
<td class="pub_tab_col">
<a
href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=279530157788236971"
target="_blank"
>6</a
>
</td>
<td class="pub_tab_col">2021</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://doi.org/10.3303/CET2188079"
target="_blank"
>Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization</a
>
<p>
LF Santos, CBB Costa, JA Caballero, MASS Ravagnani
</p>
<p>
Applied Energy, 310, 118537
</p>
</td>
<td class="pub_tab_col">
<a href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=13710822674820088791">2</a>
</td>
<td class="pub_tab_col">
2022
</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://www.sciencedirect.com/science/article/abs/pii/B9780128233771502330"
target="_blank"
>Search Space Analysis in Work and Heat Exchange Networks
Synthesis using MINLP Models</a
>
<p>LF Santos, CBB Costa, JA Caballero, MASS Ravagnani</p>
<p>Computer Aided Chemical Engineering 48, 1393-1398, 2020</p>
</td>
<td class="pub_tab_col"><a href="https://scholar.google.com.br/scholar?oi=bibs&hl=en&cites=1402322583916226757">1</a></td>
<td class="pub_tab_col">2020</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://doi.org/10.1016/j.energy.2022.125271"
target="_blank"
>Multi-objective simulation optimization via kriging surrogate models applied to natural gas liquefaction process design</a
>
<p>
LF Santos, CBB Costa, JA Caballero, MASS Ravagnani
</p>
<p>
Energy, 125271
</p>
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<td class="pub_tab_col">
0
</td>
<td class="pub_tab_col">
2022
</td>
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<tr class="item">
<td class="pub_tab_col0">
<a
href="https://doi.org/10.1016/B978-0-323-95879-0.50133-8"
target="_blank"
>MINLP model for work and heat exchange networks synthesis considering unclassified streams</a
>
<p>
LF Santos, CBB Costa, JA Caballero, MASS Ravagnani
</p>
<p>
Computer Aided Chemical Engineering 51, 793-798
</p>
</td>
<td class="pub_tab_col">
0
</td>
<td class="pub_tab_col">
2022
</td>
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<tr class="item">
<td class="pub_tab_col0">
<a
href="https://doi.org/10.1016/B978-0-323-95879-0.50131-4"
target="_blank"
>Multi-objective optimization of natural gas liquefaction process simulation via kriging surrogate model</a
>
<p>
LF Santos, CBB Costa, JA Caballero, MASS Ravagnani
</p>
<p>
Computer Aided Chemical Engineering 51, 793-798
</p>
</td>
<td class="pub_tab_col">
0
</td>
<td class="pub_tab_col">
2022
</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://doi.org/10.1016/B978-0-323-95879-0.50131-4"
target="_blank"
>Framework for Embedding Process Simulator in GAMS via Kriging Surrogate Model Applied to C3MR Natural Gas Liquefaction Optimization</a
>
<p>
LF Santos, CBB Costa, JA Caballero, MASS Ravagnani
</p>
<p>
Chemical Engineering Transactions 88, 475-480
</p>
</td>
<td class="pub_tab_col">
0
</td>
<td class="pub_tab_col">
2021
</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://ui.adsabs.harvard.edu/abs/2019APS..MARC25011T/abstract"
target="_blank"
>Diffusion Driven Nonsolvent Induced Phase Separation</a
>
<p>
D Tree, LF Dos Santos, C Wilson, T Scott, JU Garcia, G
Fredrickson
</p>
<p>APS March Meeting Abstracts 2019, C25. 011, 2019 </p>
</td>
<td class="pub_tab_col">0</td>
<td class="pub_tab_col">2019</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://aiche.confex.com/aiche/2018/meetingapp.cgi/Paper/525637"
target="_blank"
>Modeling the Effects of Mass Transfer on Microstructure
Formation in Polymer Membranes</a
>
<p>D Tree, LF Dos Santos, GH Fredrickson</p>
<p>2018 AIChE Annual Meeting</p>
</td>
<td class="pub_tab_col">0</td>
<td class="pub_tab_col">2018</td>
</tr>
<tr class="item">
<td class="pub_tab_col0">
<a
href="https://aiche.confex.com/aiche/2017/meetingapp.cgi/Paper/494135"
target="_blank"
>Modeling the Effects of Mass Transfer on Microstructure
Formation in Phase-Inversion Membranes</a
>
<p>D Tree, LF Dos Santos, GH Fredrickson</p>
<p>2017 AIChE Annual Meeting</p>
</td>
<td class="pub_tab_col">0</td>
<td class="pub_tab_col">2017
</td>
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