Data-Driven Process Optimization Considering Surrogate Model Prediction Uncertainty: A Mixture Density Network-Based Approach
| Autor: | Wei Wu, Zukui Li, Shu Bo Yang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Training set Artificial neural network Computer science General Chemical Engineering Computer Science::Neural and Evolutionary Computation 02 engineering and technology General Chemistry 021001 nanoscience & nanotechnology Industrial and Manufacturing Engineering Data-driven ComputingMethodologies_PATTERNRECOGNITION Surrogate model 020401 chemical engineering Mixture distribution Process optimization 0204 chemical engineering 0210 nano-technology |
| Zdroj: | Industrial & Engineering Chemistry Research. 60:2206-2222 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/acs.iecr.0c04214 |
| Popis: | The artificial neural network (ANN) can be effectively used as a data-driven surrogate model in process optimization. However, there is a problem that the change of training set leads to prediction... |
| Databáze: | OpenAIRE |
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