At the core of the SI-Plating platform is a fine-tuned, electroplating-specific LLM developed through domain-adaptive pretraining and parameter-efficient fine-tuning. It is trained on a curated corpus of peer-reviewed sustainability literature, industrial best practices, and environmental regulations. This model understands specialized terminology and sustainability metrics and leverages retrieval-augmented generation (RAG) to provide recommendations grounded in real-time data and current regulations. The SI-Plating platform acts as an intelligent assistant, interpreting simulation results, accessing regulatory and operational knowledge, and communicating insights through natural language.
The MDT of the platform simulates critical unit operations, such as cleaning, rinsing, and plating, using PINNs to solve governing nonlinear ODEs for mass and energy balances. Each PINN-embedded process module interfaces with a centralized unit, referred to as the MasterController, which coordinates parameter updates and state transitions across the system. This modular structure supports dynamic scenario modeling and high-fidelity environmental and economic assessments.
The SI-Plating platform empowers engineers and decision-makers to perform predictive sustainability analyses, explore “what-if” scenarios, compare process configurations, and receive actionable guidance, all through a natural language interface. The system also automates sustainability reporting and helps identify mitigation strategies and clean technology alternatives. This integrated framework transforms the digital twins from a passive simulator into a collaborative, AI-driven agent for sustainability solution derivation. A comprehensive case study will be illustrated to demonstrate methodological efficacy.