基于神经网络的水泥比表面积标准化检测研究

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Abstract： This paper proposes a neural network-based method for the detection of cement specific surface area to realize the standardization and intelligence of the detection of cement specific surface area.Using the Blaine permeability method as a foundation，it integrates deep learning technologies to construct a dual hiden-layer neural network model.Bycombining traditional detection parameters with SEM image features of cement particles，the method establishes a precise mapping between detection parameters and specific surface area.Through data collection， model training，and optimization，this paper achieves rapid and accurate detection of cement specific surface area. Results demonstrate that the methodhas theadvantages ofhigh efficiency，goodaccuracy，andeasyoperation，and has significant improvements inmeasurement time，batch processngcapacity，andrepeatabilitycompared with traditional methods， providing reliable support for quality control in cement production.

Keywords：neural network;cement specific surface area; standardized detection

0 引言

业的快速发展，传统的Blaine透气法检测方式已难以满足现代化生产对检测效率和精度的要求。（剩余3017字）