Industrial Automation & Sustainability
Research on the international intelligent operation model of “digitalization + greening” dual-track collaboration of industrial electrical enterprises under intelligent drive
This research outlines an intelligent operation model for international industrial electrical enterprises, integrating digitalization and greening. It establishes a four-level data and carbon accounting system adhering to GHG Protocol/ISO standards, implements multi-objective optimization (cost, energy, carbon intensity, delivery) using MILP/MPC/RL, and utilizes digital twins for verification. The model supports cross-border traceability and compliance (CBAM/ETS) with quasi-experimental methods (A/B testing, DiD/PSM). Results show significant reductions in energy consumption and carbon emissions across multiple factories while maintaining cost control, demonstrating its replicability.
Executive Impact
Key performance indicators demonstrating the tangible benefits of intelligent operations in industrial electrical enterprises.
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Average Carbon Emission Reduction (across lines)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Article Insights
14.8%
Average Carbon Emission Reduction (across lines)
Enterprise Process Flow
Edge Data Collection & ETL
→
Factory Data Processing & Features
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Group Factor Library & Models
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MILP/MPC/RL Optimization
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Digital Twin Verification
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Cross-Border Compliance & Reporting
| Line | kWh/unit Reduction | kg CO2e/unit Reduction | Cost/Unit Reduction | Key Benefits |
|---|---|---|---|---|
| L1 | 10.0% | 14.8% | 5.6% |
|
| L2 | 12.1% | 15.8% | 3.5% |
|
| L3 | 12.7% | 16.5% | 4.9% |
|
Successful Implementation: Peak-shift DR + MPC
Summary: An A/B testing experiment demonstrated the effectiveness of Peak-shift Demand Response combined with Model Predictive Control in Line A.
Problem: High energy costs during peak hours and volatile carbon prices impacted profitability and compliance.
Solution: Implemented a combined strategy of Peak-shift DR to optimize energy consumption patterns and MPC for real-time load adjustments based on market prices and carbon intensity.
Outcome: Achieved a 0.62 kWh/unit reduction and 0.31 kg CO2e/unit reduction, leading to a 0.18 USD/unit cost reduction. The P-value of 0.011 confirmed statistical significance, leading to a 'Scale-up' decision for broader deployment. This significantly improved operational efficiency and reduced carbon footprint while maintaining profitability.
Calculate Your Potential ROI
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Your AI Implementation Roadmap
A phased approach to integrate intelligent operations, digitalization, and greening for sustainable growth.
Phase 1: Data Foundation & Carbon Accounting Setup
Establish a four-level data system (edge, workshop, factory, group), implement GHG Protocol/ISO 14064/14067 for Scope 1/2/3 accounting, and integrate data collection protocols (OPC UA/Modbus/IEC 61850/MQTT).
Phase 2: Multi-Objective Optimization & Control
Develop and deploy MILP/MPC/RL models for simultaneous optimization of cost, energy consumption, carbon intensity, and delivery. Integrate with digital twin environments for verification and rapid iteration.
Phase 3: Cross-Border Traceability & Compliance
Implement 'Batch/Serial ID - Evidence - Factors/Rules' framework for end-to-end supply chain traceability. Set up CBAM/ETS declaration and risk quantification mechanisms, ensuring compliance across multiple jurisdictions.
Phase 4: Governance & Continuous Improvement
Establish a closed-loop governance structure with unified indicators, algorithms, processes, and responsibilities. Implement A/B testing and causal inference for ongoing performance evaluation and progressive rollout of validated strategies.
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