(510c) Scenario-Based Environmental and Economic Impact Assessment By Pinn-Based Modular Digital Twin for Sustainable Electroplating

Electroplating facilities are under increasing pressure to mitigate their environmental footprint without compromising operational performance and to improve their profitability. Achieving this balance is particularly challenging due to the inherent complexity and variability of hazardous and toxic chemical-intensive processes, for which traditional modeling approaches often fail to represent accurately. This study presents a novel modular digital twin (MDT) framework powered by Physics-Informed Neural Networks (PINNs) for conducting dynamic, scenario-based environmental and economic impact assessments. Focusing on the critical unit operations, where chemical consumption, water usage, and emission and wastewater generation are most pronounced, this approach offers a transformative approach to sustainable system design, and optimal operational strategy derivation for sustainable manufacturing.

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.