一种蒸发工艺调控指令生成方法、系统、设备及介质
By using a stacked integrated prediction model with multiple learners and a cost function for switching operating conditions, the problems of large concentration measurement errors and operating condition fluctuations in the alumina evaporation process were solved. This enabled accurate prediction of sodium aluminate solution concentration and stable system control, thereby improving the intelligent operation level of the alumina evaporation process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN POLYTECHNIC
- Filing Date
- 2025-08-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies in alumina evaporation processes suffer from large concentration measurement errors and significant fluctuations in operating conditions. Traditional integrated models struggle to adapt to complex operating conditions and sudden disturbances, resulting in long parameter adjustment feedback cycles and failing to meet the robustness and generalization requirements of industrial-grade predictions.
A stacked ensemble prediction model with multiple learners is adopted. By combining energy consumption parameters and temperature parameters, nonlinear time-series correlation feature vectors are extracted, and pre-trained base learners and GBDT meta-learners are used to generate predicted values of sodium aluminate solution concentration. A working condition switching cost function is established to optimize the control path and generate working condition control commands for the automatic control system.
It improves the accuracy and generalization ability of sodium aluminate solution concentration prediction, avoids unreasonable operating condition jumps, ensures the path stability and system controllability of the evaporation system, significantly enhances the level of intelligent operation, and supports stable production and energy conservation and emission reduction.
Smart Images
Figure CN120909248B_ABST