Generally, PINNs integrate physical laws directly into neural network architecture, enabling the model to accurately capture complex, nonlinear process dynamics even in conditions of sparse or noisy data. This capability is particularly beneficial for electroplating processes, where system dynamics involve complex chemical reactions, contaminant removal mechanisms, and transport phenomena that conventional data-driven models alone often inadequately represent. As a result, PINNs substantially enhance predictive accuracy, facilitate real-time adjustments, and allow for reliable long-term forecasts that are critical for process control and sustainability optimization.
The modular architecture of the MDT is a key strength, offering both flexibility and scalability. Each process step is modeled as an independent PINN-embedded module, which can be readily adapted to various operational configurations and process policies. This enables systematic exploration of diverse scenarios through comprehensive "what-if" analyses, effectively capturing dynamic interactions within the process and identifying trade-offs between environmental sustainability (e.g., reduced chemical and water usage) and operational cost-efficiency. By integrating high-fidelity simulation with adaptive, real-time parameter adjustment, the MDT framework serves as a dynamic decision-support tool that continuously evolves alongside the physical processes, facilitating proactive optimization and sustainable operational strategy development. A case study will be presented to demonstrate that the introduced methodology can provide electroplating facilities with a practical, reliable, and data-efficient platform to meet stringent environmental regulations while maintaining economic competitiveness.