Published July 22, 2020
| Version 1
Dataset
Open
pH reactor dataset
- 1. Politecnico di Milano
Description
Dataset of noisy input and output measurements collected from a simulator of a pH reactor. The system is single input single output.
The dataset consists of the following files:
- 'PH_U_Train.csv' and 'PH_Y_Train.csv': training dataset. The training dataset consists of 15 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment.
- 'PH_U_Val.csv' and 'PH_Y_Val.csv': validation dataset. The validation dataset consists of 4 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment.
- 'PH_U_Test.csv' and 'PH_Y_Test.csv': test dataset. The test dataset consists of 1 experiment of 2000 (u, y) samples.
- 'PH_U.csv' and 'PH_Y.csv': concatenation of the training, validation, and test datasets.
Moreover, the parameters of an Incrementally Input-to-State Stable LSTM neural network trained to learn the dynamical system are reported in the file 'LSTM_parameters.mat'.
If you use this data please cite the related paper
Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). Learning model predictive control with long short‐term memory networks. International Journal of Robust and Nonlinear Control.
Files
PH_U.csv
Files
(1.1 MB)
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Additional details
Related works
- Is supplement to
- Journal article: 10.1002/rnc.5519 (DOI)
References
- R. C. Hall, and D. E. Seborg, "Modelling and self-tuning control of a multivariable pH neutralization process part I: Modelling and multiloop control." 1989 American Control Conference. IEEE, 1989.
- F. Bonassi, E. Terzi, M. Farina, and R. Scattolini, "LSTM neural networks: Input to state stability and probabilistic safety verification", arXiv preprint arXiv:1912.04377, 2019
- Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). "Learning model predictive control with long short‐term memory networks". International Journal of Robust and Nonlinear Control