The platform integrates production plans, energy consumption, environmental facility operations, and carbon quota data to build a "production efficiency-environmental cost-carbon constraint" multi-objective game model.
Machine learning predicts energy consumption peaks and emission fluctuations under different production schedules, dynamically recommending solutions such as sinter machine oxygen adjustment, dust removal fan frequency parameters, and green power procurement timing. This ensures stable output while reducing per-ton steel energy costs by 10% and unplanned downtime emission losses by 42%.
Like Tai Chi, it resolves conflicts between environmental compliance, energy use, and productivity, redefining industrial operations with harmony and flexibility.
Tracks raw material consumption (e.g., iron ore, coke) and product efficiency.
Analyzes electricity, gas, and heat synergies, optimizing production scheduling based on peak/off-peak tariffs.
Links process emission intensity with governance facility energy use, generating cost-optimal emission reduction paths. Includes carbon flow tracking and dynamic carbon quota optimization.
Balances production stability vs. environmental compliance vs. cost efficiency, recommending key parameter combinations (e.g., sinter machine speed, casting speed) to maximize comprehensive benefits.
Synthex redefines industrial metabolism through "yin-yang equilibrium"—dynamic coupling of material-energy-pollution-carbon flows ensures stable blast furnace operations while intelligently adjusting production rhythms and governance intensity. Achieves a 10% reduction in per-ton energy consumption and 20% lower carbon intensity, breaking the "zero-sum game" between environmental and economic goals.