通过人工智能驱动的生物气体生产优化框架,提高水资源回收设施的能源循环性和实现碳中和
Farzad Farzin1, Shabnam Sadri Moghaddam1
1Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Journal of environmental management
|August 22, 2025
概括
人工智能利用LSTM和PSO-XGBoost模型优化生物气体生产,大大提高产量并减少CO2排放,以实现可持续的废水处理.
科学领域:
- 环境工程
- 生物技术
- 人工智能
背景情况:
- 废水处理厂在优化生物气生产以实现能源可持续性方面面临着挑战.
- 人工智能用于生物气体优化的现实实施需要最小的日常流程变化.
- 现有的方法通常缺乏对时间密集的无氧消化参数的实时控制.
研究的目的:
- 开发和评估人工智能驱动的方法,以优化WWTP中的生物气体生产.
- 加强废水处理过程中的能源循环和可持续性.
- 通过预测AI模型实现无氧消化参数的实时控制.
主要方法:
- 集成的长期短期记忆 (LSTM) 网络作为预测实验值的软传感器.
- 使用极端梯度提升 (XGBoost) 的粒子群优化 (PSO) 来优化操作参数 (温度,pH,度,污泥流量).
- 实施了优化后的平滑流程,以确保运营的一致性,并防止每日大幅波动.
主要成果:
- 实现了生物气产量的显著增加.
- 净二氧化碳排放量减少了156%.
- 将能源循环指数从0.71提高到1.15,表示能源自给自足.
结论:
- 通过使用LSTM和PSO-XGBoost进行人工智能驱动的优化,对现实世界生物气体生产有效.
- 开发的方法提高了WWTP的能源可持续性和效率.
- 这种方法通过智能流程控制将水处理设施转化为节能设施.
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