dwsimopt: DWSIM simulation optimization with Python!

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The DWSIM Optimization (dwsimopt) is a Python library that automates DWSIM simulations for process optimization. The simulation dlls are embedded in the programming environment so that they can be accessed and modified by the optimization algorithms.

Mathematical background

Although very efficient to describe in detail 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. 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">[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">[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">[3]</a>. A simplified version of this class of problems can be described as finding an that solves globally the following constrained problem

in which the objective function and constraints , being q the number of constraints, are somewhat expensive to calculate, slightly noisy, and black-box functions, i.e. there is no available mathematical expression for f or g, but for a given the values of f(x) and f(x) are calculated in a computer code simulation with some noise.

Requirements

• Python <= 3.9 (python 3.8 recommended -- using python 3.9 requires installing dwsimopt from setup.py)
• DWSIM v7+ (open-source chemical process simulation. <a href="https://dwsim.org/index.php/download/">Download here</a>)
• pythonnet == 2.5.2 (on Python 3.9 you'll need to <a href="https://www.lfd.uci.edu/~gohlke/pythonlibs/#pythonnet">download the pythonnet2.5.2 wheel</a> and pip install path\to\pythonnet_wheel)
• pywin32
• numpy
• scipy
• scikit-opt

It is recommendable to start from a fresh environment and let the dwsimopt install the dependencies, see <a href=#installation>Installation</a> section. DWSIM must be downloaded and installed manually.

Installation

Install the latest version of this repository to your machine

<pre>pip install dwsimopt</pre>

or

<pre>git clone https://github.com/lf-santos/dwsimopt.git cd dwsimopt python setup.py install </pre>

Make sure you have all the required packages and software. Navigate through the jupyter notebook examples. Use the OptimiSim class to embed your DMSWIM simulation into Python. Add degrees of freedom, objective function and constraints from your simulation optimization problem with the py2dwim python-dwsim data exchange interface. Solve the problem with a suitable optimization solver (surrogate-based optimization or global optimization meta-heuristics recommended) that you can find methods in the OptimiSim class (e.g. GA, PSO, DE)

Citing us

If you use dwsimopt, please cite the following paper: <a href="https://doi.org/10.1016/j.apenergy.2022.118537" title="simulation optimization paper">L. F. Santos, C. B. B. Costa, J. A. Caballero, M. A. S. S. Ravagnani, Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization, Applied Energy, 310, 118537 (2022).</a>

<pre> @article{Santos2022, title = {Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization}, author = {Lucas F. Santos and Caliane B.B. Costa and José A. Caballero and Mauro A.S.S. Ravagnani}, journal = {Applied Energy}, volume = {310}, pages = {118537}, year = {2022}, issn = {0306-2619}, doi = {https://doi.org/10.1016/j.apenergy.2022.118537}, </pre>