Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.

中文翻译：

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铁矿石烧结综合生产指标智能预测与软传感研究进展


铁矿石烧结是钢铁生产中的关键过程，对整体能源消耗和各种环境污染物的排放有重大影响。提高这一过程的效率对于实现钢铁行业的可持续性至关重要。准确预测和实时监控综合生产指标对于优化生产和提高能源效率至关重要。本文对应用于铁矿石烧结过程的智能预测和软传感技术进行了系统综述。它详细介绍了这些技术的机制和工作原理，重点介绍了质量、热状态、产量和能耗等关键指标。本文探讨了四种预测方法的当前进展：基于机构分析的方法、基于数据特征分析的方法、基于多模型融合的方法和基于操作模式识别的方法。最后，指出了当前烧结过程综合生产指标预测面临的挑战，包括难以应对不断变化的运行模式、图像特征分析不完整以及对数据分布差异的考虑不足。未来，运行模式识别方法、深度学习方法、迁移学习方法和计算机视觉技术将在烧结过程的综合生产指标预测中具有广阔的前景。