Efficient dynamic sampling of batch processes through operation recipes

doi:10.1016/j.compchemeng.2023.108433

2023-11-01

PRE

Gerardo Brand Rihm *

Erik Esche

Jens‐Uwe Repke

DOI：10.1016/j.compchemeng.2023.108433

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摘要

En 中文

The complexity of dynamic phenomena present in chemical processes often results in high evaluation costs of accurate first-principles models. This limits their real-time applicability, e.g. for advanced process control. A common solution is the derivation of simpler but faster data-driven surrogate models trained on simulated time series generated from dynamic samplings of a mechanistic model. For batch processes, known non-adaptive dynamic sampling methods lead to unrealistic or even infeasible operation cycles, raising the cost of gener-ating simulated datasets with sufficient information content to train accurate surrogate models. An alternative sampling strategy is developed and analyzed, where sampled input trajectories are constrained to process knowledge in the form of parametrized operation recipes. The proposed methodology is tested for the case studies of full batch cycles of a crystallizer and a batch distillation column, showing that it is more efficient in terms of convergent simulations compared to an established dynamic sampling strategy.

Keyword:

Dynamic sampling

Surrogate model

Batch processes