由综合知识图和基于概率的算法推的页岩废水处理政策
Li He1, Yugeng Luo2, Mengxi He2
1State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, 300350, China. helix111@tju.edu.cn.
Environmental monitoring and assessment
|April 8, 2025
概括
这项研究引入了一个新的框架,使用知识图和人工智能来推页岩废水处理技术. 该模型提供稳定和强大的政策建议,而不需要广泛的数据或物理模型.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 化学工程是化学工程的重要组成部分.
背景情况:
- 页岩废水 (SWW) 构成重大环境和健康风险.
- 在没有专家知识或数据的情况下,选择合适的SWW处理技术是具有挑战性的.
- 现有的方法通常依赖于复杂的物理模型和大量的定量数据.
研究的目的:
- 为推页岩废水处理政策制定数据驱动的建模框架.
- 将知识图 (KG) 与基于概率的算法集成为政策生成.
- 用蒙特卡洛 (MC) 技术评估推政策的稳定性和稳定性.
主要方法:
- 开发了一个建模框架,将知识图卷积网络 (KGCN) 和RippleNet与KG结合起来.
- 将该模型应用于中国的页岩地区,包括重庆,四川,云南,贵州,西和内蒙古.
- 使用蒙特卡洛 (MC) 技术来评估推治疗策略的稳定性.
主要成果:
- 该框架成功推了特定的SWW处理技术,包括化学沉,超,膜生物反应器,电透析,电凝,反透和电催化氧化.
- 推政策表现出高稳定性,基于MC的概率超过0.86.
- 该模型被证明是强大的,在没有传统的基于物理的模型的情况下提供可靠的政策建议.
结论:
- 综合的KG和AI框架为开发页岩废水处理战略提供了一个可行的替代方案.
- 该模型的强度和稳定性得到了MC模拟的证实.
- 未来的工作重点是加强KG,算法,并提供政策解释机制.
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