Aiming at the problem that the accuracy and economy of the traditional off-line batching method are not high, the online batching system (BSMLIA) based on machine learning and intelligent algorithms was put forward from three aspects: real-time, technical requirements and economic benefits. The accurate solution and on-line fast calculation of sintering raw material ratio under the influence of multiple factors are solved. Specifically, a BSMLIA architecture with three levels of data communication layer (DCL), parameter prediction and batching optimization layer (PPBOL), and diagnostic decision layer (DDL) was first designed to realize online monitoring and abnormal diagnosis of sinter performance. Then, the sintering batching adjustment and optimization module (SBAOM) was elaborated. The mixture performance prediction model was developed by MLR and LightGBM algorithm, the model can be based on sinter composition and quality index requirements and current sintering production process parameters to calculate the appropriate mixture performance. In addition, the pre-batching model and the sintering batching model were established to achieve the solution of the lowest raw material cost ratio for a given mixture performance. Finally, the actual production data was used to verify the SBAOM. The results proved that the online batching system can not only quickly calculate the batching plan that meets the requirements, but also reduce the batching cost by RMB 29.54/ton.

针对传统离线配料方法准确性和经济性不高的问题，从实时性、技术要求和经济性三个方面提出了基于机器学习和智能算法的在线配料系统（BSMLIA）。好处。解决了多因素影响下烧结原料配比的精确求解和在线快速计算。具体而言，首先设计了具有数据通信层（DCL）、参数预测和批处理优化层（PPBOL）和诊断决策层（DDL）三个层次的BSMLIA架构，以实现烧结性能的在线监测和异常诊断。然后，详细阐述了烧结配料调整和优化模块（SBAOM）。混合料性能预测模型由MLR和LightGBM算法开发，该模型可以根据烧结矿成分和质量指标要求以及当前烧结生产工艺参数计算出合适的混合料性能。此外，还建立了预配料模型和烧结配料模型，以实现给定混合物性能的最低原材料成本比的解决方案。最后，使用实际生产数据来验证 SBAOM。结果证明，在线配料系统不仅可以快速计算出符合要求的配料方案，还可以降低配料成本29.54元/吨。该模型可以根据烧结矿成分和质量指标要求以及当前的烧结生产工艺参数计算出合适的混合料性能。此外，还建立了预配料模型和烧结配料模型，以实现给定混合物性能的最低原材料成本比的解决方案。最后，使用实际生产数据来验证 SBAOM。结果证明，在线配料系统不仅可以快速计算出符合要求的配料方案，还可以降低配料成本29.54元/吨。该模型可以根据烧结矿成分和质量指标要求以及当前的烧结生产工艺参数计算出合适的混合料性能。此外，还建立了预配料模型和烧结配料模型，以实现给定混合物性能的最低原材料成本比的解决方案。最后，使用实际生产数据来验证 SBAOM。结果证明，在线配料系统不仅可以快速计算出符合要求的配料方案，还可以降低配料成本29.54元/吨。实际生产数据用于验证 SBAOM。结果证明，在线配料系统不仅可以快速计算出符合要求的配料方案，还可以降低配料成本29.54元/吨。实际生产数据用于验证 SBAOM。结果证明，在线配料系统不仅可以快速计算出符合要求的配料方案，还可以降低配料成本29.54元/吨。