Journal: Journal of Safety Science and Engineering DOI: 10.32629/jsse.v4i4.21719
Abstract
绿色甲醇是航运燃料和化工原料低碳转型的重要选项,生物质气化合成路线因原料来 源广、碳减排潜力大而受到持续关注。该工艺链条长、耦合强,原料性质波动频繁, 传统依赖人工经验的操作方式难以维持装置在最优区间运行。本文围绕AI优化功能 在生物气化制绿色甲醇工艺中的应用展开分析：首先梳理工艺流程及其优化难点,随 后给出一个由数据层、模型层和决策控制层构成的AI优化体系架构,并结合公开研究 与工程实践,分别讨论原料波动感知与配伍优化、气化操作参数寻优、合成气氢碳比 调节、催化剂性能预测、设备故障诊断以及数字孪生协同优化等六类典型功能的实 现方式和应用效果。已有研究表明,机器学习模型对合成气组分和甲醇收率的预测精 度普遍可达R²不低于0.96,气化温度、水蒸气与生物质配比等关键参数的寻优结果与 机理模拟吻合良好。AI优化功能可将装置运行从“事后纠偏”转变为“事前预测、实时 寻优”,但目前仍受数据质量、模型外推能力和安全约束验证等因素制约。文章最后 对该方向的发展趋势作出展望。
Keywords
生物质气化；绿色甲醇；人工智能；机器学习；工艺优化；数字孪生
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