Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.

水泥是建筑业的主要建筑材料。其凝结时间直接影响混凝土的凝结时间和强度，进而影响施工进度和建筑质量。但是，传统的测量技术不仅劳动强度大，耗时长，而且对技术的要求也很高。各种人为因素，例如操作不充分，将导致测量中的重大误差。准确的凝结时间预测可以节省人力，避免因操作不足而造成的大错误，并指导高性能水泥的生产。本文提出了一种基于广泛学习系统的有效模型来预测初始和最终设置时间。它致力于根据熟料的成分和物理特性直接预测凝固时间，这对优化熟料配方具有重要意义。实验结果表明，该算法能够准确预测整定时间，具有良好的泛化能力，解决了测量中的劳动强度问题，节省了大量资源。此外，广泛的学习系统可以快速建立几乎没有误差的凝结时间预测模型，满足各种特殊水泥快速建模的工业需求。