Predictive Maintenance · Real-time Analytics · Risk Management
Conch needed to scale AI across cement production, but traditional model development was slow, hard to generalize, and too weak to support real-time quality, energy, equipment, and safety optimization at industrial scale.
Reported impact
Conch moved from isolated AI experiments to a reusable cement-industry model system that improved product prediction, cut energy use and emissions, enabled unattended inspection, and raised safety-monitoring accuracy.
Conch Group used Huawei Cloud Stack to build an AI training center for cement and building-material production. The stack hosts a model program built on Pangu predictive, vision, and NLP foundations so the company can train centrally, infer at the edge, and improve models through cloud-edge collaboration. Fed by large volumes of production and expert data from mining, calcination, conveying, packaging, and plant operations, the platform now supports more than 200 candidate scenarios. On top of it, Conch deployed quality models that predict 3-day and 28-day clinker strength, optimization models that learn kiln-control strategies from multi-source process data, vision models plus distributed fiber sensing for belt and roller anomaly monitoring, and NLP-driven intelligent Q&A for workers. The result is a reusable industrial AI system spanning quality, production, equipment, safety, and knowledge support instead of isolated point models.
Production, equipment, and expert knowledge data is collected into the AI training center on Huawei Cloud Stack, where Pangu predictive, vision, and NLP models are trained and updated. Trained models are then served back into quality control, kiln optimization, conveyor inspection, safety monitoring, and employee question-answering workflows.
"Conch and Huawei started the cement-building-material AI large-model program in April 2024 and identified 15 classes with more than 200 AI application scenarios. (original: 2024年4月，为进一步推动AI技术在水泥建材行业的深度应用... 双方梳理出涵盖矿山开采至包装发运全流程的、共计15类200余个人工智能应用场景。)"
"Conch built an AI training center on Huawei Cloud Stack and used Pangu predictive, vision, and NLP large models to form a center-training, edge-inference operating system. (original: 海螺水泥基于华为云Stack建设人工智能训练中心，并利用华为云盘古大模型的预测大模型、视觉大模型、NLP（自然语言处理）大模型三大底座，打造集"中心训练、边缘推理、云边协同、边用边学、持续优化"于一体的人工智能运行体系。)"
"The clinker-strength model predicts 3-day and 28-day strength with deviation within 1 MPa and accuracy above 85%. (original: 海螺集团基于华为云盘古预测大模型，通过实时生产质量关键特征推荐，实现了熟料3天、28天强度预测，强度预测值和检测值偏差在1MPa范围内，准确率超85%。)"
"The production-optimization model lowers standard coal consumption by another 1% and can cut more than 4,500 tons of CO2 per year on a 5,000-ton/day clinker line. (original: 构建烧成全局寻优大模型... 实现标准煤耗在一级能效基础上再下降1%的目标。按5000吨/日熟料线测算，每年减少二氧化碳排放4500余吨。)"
"Vision and safety models cover 28 equipment scenes and more than 20 safety scenes, with violation and anomaly recognition accuracy reaching 95%. (original: 对托辊异常、皮带撕裂等28类场景进行实时管控... 人员违章、设备异常等20余类场景的识别准确率达95%。)"
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