README.md

MeLOn - Machine Learning models for Optimization

Thank you for using the beta version of MeLOn! If you have any issues, concerns, or comments, please communicate them using the "Issues" functionality in GitLab or send an e-mail to artur.schweidtmann@rwth-aachen.de.

About

MeLOn provides scripts for the training of various machine-learning models and their C++ implementation which can be used in the open-source solver MAiNGO. The machine-learning module git repository currently contains the following models:

• Artificial neural networks for regression
• Gaussian processes for regression (also known as Kriging)
• Support vector machine for regression
• One-class support vector classification
• Convex hull of point cloud

Further models are under current develpment and will be published soon.

Optimization Methods

• Deterministic global optimization with neural networks embedded (Schweidtmann and Mitsos, 2019)
• Deterministic global optimization with Gaussian processes embedded (Schweidtmann et al., 2020)
• Obey validity domain of data-driven models (Schweidtmann et al., 2020b)

Example Applications

The proposed machine-learning models have been used in various applications.

Applications of deterministic global optimization with artificial neural networks embedded

• Hybrid modeling of chemical processes and process optimization (Schweidtmann and Mitsos, 2019)

• Rational design of ion separation membranes (Rall et al., 2019, Rall et al., 2020,Rall et al., 2020b)

• Optimization of energy processes where accurate thermodynamic is learned by neural networks. Applications to organic Rankine cycle optimization (Schweidtmann et al., 2019, Huster et al., 2019), working fluid selection (Huster et al., 2019), working fluid mixtures (Huster et al., 2020b), superstructure (Huster et al., 2020c)

• Using of neural networks as a surrogate with a guaranteed accuracy with application to flash models (Schweidtmann et al., 2019)

• Scheduling of a compressed air energy storage system where the efficiency map of compressors and turbines is learned by neural networks (Schäfer et al., 2020)

• Design of multistage layer melt crystallization where the crystal growth is learned by neural networks (Kunde et al., 2022)

Applications of deterministic global optimization with Gaussian processes embedded

• Chance-constrained programming with Gaussian processes (Schweidtmann et al., 2020)

• Bayesian optimization with global optimization of the acquisition function (Schweidtmann et al., 2020)

How to Cite This Work

@article{schweidtmann2019deterministic,
  title={Deterministic global optimization with artificial neural networks embedded},
  author={Schweidtmann, Artur M and Mitsos, Alexander},
  journal={Journal of Optimization Theory and Applications},
  volume={180},
  number={3},
  pages={925--948},
  year={2019},
  publisher={Springer},
  doi={10.1007/s10957-018-1396-0},
  url={https://doi.org/10.1007/s10957-018-1396-0}
}

References

• Rall, D., Menne, D., Schweidtmann, A. M., Kamp, J., von Kolzenberg, L., Mitsos, A., & Wessling, M. (2019). Rational design of ion separation membranes. Journal of membrane science, 569, 209-219. https://doi.org/10.1016/j.memsci.2018.10.013

• Rall, D., Schweidtmann, A. M., Aumeier, B. M., Kamp, J., Karwe, J., Ostendorf, K., Mitsos, A. & Wessling, M. (2020). Simultaneous rational design of ion separation membranes and processes. Journal of Membrane Science, 117860. https://doi.org/10.1016/j.memsci.2020.117860

• Rall, D., Schweidtmann, A. M., Kruse, M., Evdochenko, E., Mitsos, A. & Wessling, M. (2020). Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning. Journal of Membrane Science, In Press. https://doi.org/10.1016/j.memsci.2020.117860

• Schweidtmann, A. M., & Mitsos, A. (2019). Deterministic global optimization with artificial neural networks embedded. Journal of Optimization Theory and Applications, 180(3), 925-948. https://doi.org/10.1007/s10957-018-1396-0

• Schweidtmann, A. M., Huster, W. R., Lüthje, J. T., & Mitsos, A. (2019). Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks. Computers & Chemical Engineering, 121, 67-74. https://doi.org/10.1016/j.compchemeng.2018.10.007

• Schweidtmann, A. M., Bongartz, D., Huster, W. R., & Mitsos, A. (2019). Deterministic Global Process Optimization: Flash Calculations via Artificial Neural Networks. In Computer Aided Chemical Engineering (Vol. 46, pp. 937-942). Elsevier. https://doi.org/10.1016/B978-0-12-818634-3.50157-0

• Schweidtmann, A. M., Bongartz, D., Grothe, D., Kerkenhoff, T., Lin, X., Najman, J., & Mitsos, A. (2020). Global optimization of Gaussian processes. Submitted. Preprint available on https://arxiv.org/abs/2005.10902.

• Schweidtmann, A. M., Weber, J., Wende, C., Netze, L., & Mitsos, A. (2020). Obey validity domains of data-driven models. Submitted. Preprint available on https://arxiv.org/abs/2010.03405.

• Huster, W. R., Schweidtmann, A. M., & Mitsos, A. (2019). Impact of accurate working fluid properties on the globally optimal design of an organic Rankine cycle. In Computer Aided Chemical Engineering (Vol. 47, pp. 427-432). Elsevier.https://doi.org/10.1016/B978-0-12-818597-1.50068-0

• Huster, W. R., Schweidtmann, A. M., & Mitsos, A. (2020). Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation. Optimization and Engineering, (Vol. 21, pp. 517-536).https://doi.org/10.1007/s11081-019-09454-1

• Huster, W. R., Schweidtmann, A. M., & Mitsos, A. (2020). Globally optimal working fluid mixture composition for geothermal power cycles. Energy, (Vol. 212).https://doi.org/10.1016/j.energy.2020.118731

• Huster, W. R., Schweidtmann, A. M., & Mitsos, A. (2020). Deterministic global superstructure-based optimization of an organic Rankine cycle. Computers and Chemical Engineering, (Vol. 141).https://doi.org/10.1016/j.compchemeng.2020.106996

• Schäfer, P., Schweidtmann, A. M., Lenz, P. H., Markgraf, H. M., & Mitsos, A. (2020). Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Computers & Chemical Engineering, 132, 106598. https://doi.org/10.1016/j.compchemeng.2019.106598

• Kunde, C., Mendez, R., & Kienle, A. (2022). Deterministic global optimization of multistage layer melt crystallization using surrogate models and reduced space formulations. In Computer Aided Chemical Engineering (Vol. 51, pp. 727-732). Elsevier.https://doi.org/10.1016/B978-0-323-95879-0.50122-3