基于物联网的过管理系统使用加强的ANFIS来减少城市工业设施的VOC
Keunyoung Kim1, Donghyuk Chun2, Woosung Yang3
1Department of Robotics, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea.
Scientific reports
|May 20, 2025
概括
本研究介绍了一种负担得起的物联网系统,用于监测挥发性有机化合物 (VOC) 排放,并预测工业中的活性炭过器更换情况. 该系统使用先进的AI模型进行准确的预测,从而实现自主过器维护.
科学领域:
- 环境科学 环境科学
- 工业化学 工业化学 工业化学
- 传感器技术 传感器技术
背景情况:
- 挥发性有机化合物 (VOC) 排放带来环境和健康风险.
- 小规模产业需要成本高效的排放监测和控制解决方案.
- 活性炭过器对于减少挥发性有机化合物至关重要,但需要及时更换以保持效率.
研究的目的:
- 开发一个低成本,基于物联网的多传感器系统,用于实时监测VOC排放.
- 在小型工业环境中预测活性炭过器更换需求.
- 提高VOC排放控制系统的准确性和效率.
主要方法:
- 在吸附塔排气管部署物联网传感器模块,以实时监测VOC.
- 强化适应性神经模糊推理系统 (RANFIS) 的开发,以准确预测VOC度,并进行异常值校正.
- 实施决策树 (DT) 模型,以估计用于预测性维护的过器突破点.
主要成果:
- 在深度神经网络 (DNN) 和ANFIS上,RANFIS模型表现出优异的性能,根平均平方误差 (RMSE) 降低了高达82.4%.
- 在预测活性炭过器更换时,DT模型的准确度超过80%.
- 集成系统使用经济的传感器硬件提供实时,自主过器维护.
结论:
- 拟议的物联网系统为小型工业环境中的VOC管理提供了一个可扩展和具有成本效益的解决方案.
- 这种方法通过预测性过器维护,使排放控制系统的高效和可持续运行成为可能.
- 该研究强调了人工智能驱动的传感器网络在环境监测和工业流程优化方面的潜力.
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