Toward Accessible AI Research for Complex Batch Biochemical Processes

Batch biochemical processes are essential in industrial manufacturing, yet their intrinsic complexity poses major challenges for reliable measurement, monitoring, and modeling. Key issues include data scarcity, privacy constraints, and the absence of standardized preprocessing workflows, all of which hinder the application of deep learning (DL) in such settings. To address these challenges, we propose the Privacy-Preserving Preprocessing Pipeline (P2P)—a practical and reproducible framework designed to enhance the accessibility and robustness of DL for complex batch processes. First, a robust data preprocessing module supports batch-wise segmentation, temporal alignment, and variable selection. Second, a privacy-preserving mechanism generates synthetic datasets that retain essential physical characteristics while protecting sensitive information. Furthermore, we introduce Spatiotemporal Kinetic Interaction Modeling (SKIM), a graph-theoretic approach for identifying and characterizing causal dependencies among process variables, contributing directly to process monitoring and system identification. We construct four practical benchmark datasets and release three open-access datasets with graph structures derived via SKIM. Experimental results demonstrate thatP2Psignificantly improves the fidelity and generalizability of DL-based modeling in batch biochemical systems, paving the way for broader adoption of data-driven methods in instrumentation and measurement. All code and datasets are available at: https://github.com/cynara517/Towards-Accessible-AI-Research-for-Complex-Batch-Biochemical-Processes.