In the Chemical Processing Industry, shifts in market demand often require the implementation of new operational modes that balance economic advantages with the need to meet quality and sustainability targets. Hybrid modeling can support these process changes by combining physics-based and data-driven modeling principles to improve prediction accuracy and interpretation. In this work, the use of hybrid modeling for improved extrapolation and transfer learning activities is examined in the context of simulated biodiesel production. We study and compare different configurations of hybrid modeling, including the parallel and serial structures. We also compare hybrid modeling approaches with physics-based and data-driven models, and find that hybrid modeling consistently outperforms these benchmarks in both extrapolation and transfer learning tasks. Hybrid modeling also requires fewer samples than other benchmarks for the transfer learning task. These results suggest that hybrid modeling is an effective approach for supporting decision-making and optimizing process changes in the CPI.

中文翻译：

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用于改进化学加工行业中的外推和迁移学习的混合建模


在化学加工行业，市场需求的变化通常需要实施新的运营模式，以平衡经济优势与满足质量和可持续性目标的需要。混合建模可以通过结合基于物理和数据驱动的建模原理来支持这些过程变化，以提高预测准确性和解释。在这项工作中，在模拟生物柴油生产的背景下检查了使用混合建模来改进外推和迁移学习活动。我们研究和比较混合建模的不同配置，包括并行和串行结构。我们还将混合建模方法与基于物理和数据驱动的模型进行比较，发现混合建模在外推和迁移学习任务中始终优于这些基准。与迁移学习任务的其他基准相比，混合建模所需的样本也更少。这些结果表明，混合建模是支持决策和优化 CPI 流程变化的有效方法。