Recent advancements in transformer-based architectures, originally developed for natural language processing, have opened new avenues for modeling structured chemical systems [2]. Transformers excel at learning context-rich representations and capturing long-range dependencies, making them an ideal candidate for complex, nonlinear chemical process modeling [3,4]. In this work, we propose a novel hybrid modeling framework that integrates a transformer encoder with a physics-based model for improved prediction under diverse operational regimes. The transformer component is pre-trained on a large corpus of simulated and experimental process trajectories and then fine-tuned to specific tasks using small, high-quality datasets [5]. Our architecture encodes process states and historical data using a transformer encoder, which generates latent embeddings capturing the underlying dynamics. These embeddings are fused with the outputs of a mechanistic model through residual connections and dense layers, allowing the hybrid model to adaptively correct the first-principles predictions while retaining physical consistency. By keeping the physics backbone intact and allowing the transformer to learn system-specific corrections, the approach effectively handles uncertainty, noise, and unmodeled phenomena, especially relevant in large-scale industrial systems.
The proposed framework is broadly applicable across chemical processes involving multiscale, nonlinear dynamics, such as crystallization, polymerization, reactor design, and separation systems [6]. As a demonstration of this capability, we apply the method to a case study in industrial fermentation, where transient oxygen gradients and microorganism activity pose a significant modeling challenge. Here, the transformer-based hybrid model outperformed both purely mechanistic and traditional neural hybrid models in predicting biomass growth, substrate uptake, and product formation under fed-batch operations. Notably, the model was able to generalize across multiple operational modes and time horizons, indicating strong potential for deployment in real-time optimization and control scenarios. This transformer-integrated hybrid framework represents a significant evolution in process modeling strategies, marrying interpretability with flexibility, and enabling chemical engineers to make smarter, data-informed decisions across a wide array of complex processes.
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