Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency
The increasing reliance on neural networks (NN) in chemical process modeling highlights their capability for accurate predictions, yet their standalone application often struggles to adhere to fundamental physical laws such as equilibrium constraints and mass balance. Addressing this limitation, hyb...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508125000407 |
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