双碳背景下AI耦合太阳能技术的污泥干化系统应用

王伟龙; 张蒙纳; 刘兆鹏; 甄胜利; 李骎; 龙少鹏

doi:10.13205/j.hjgc.202507007

双碳背景下AI耦合太阳能技术的污泥干化系统应用

doi: 10.13205/j.hjgc.202507007

王伟龙^1,2,
张蒙纳^1,2,
刘兆鹏^1,2,
甄胜利^1,2,
李骎^1,2,
龙少鹏^1,2, ,

1. 北京高能时代环境技术股份有限公司, 北京 100095;
2. 天津高能时代水处理科技有限公司, 天津 300041

详细信息

作者简介:
王伟龙（1988—），男，高级工程师，主要研究方向为工业污水强化生化处理关键技术及装备。dwl1021@163.com

通讯作者:
龙少鹏（1986—），男，高级工程师，主要研究方向为双碳背景下节能降碳型环保新技术。lsp@bgechina.cn

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出版历程
- 收稿日期: 2024-09-03
- 录用日期: 2024-11-15
- 修回日期: 2024-10-26
- 网络出版日期: 2025-09-11

Application of a sludge drying system with AI-coupled solar technology under Dual Carbon goals

WANG Weilong^1,2,
ZHANG Mengna^1,2,
LIU Zhaopeng^1,2,
ZHEN Shengli^1,2,
LI Qin^1,2,
LONG Shaopeng^{1,2
, ,}

1. Beijing GeoEnviron Engineering & Technology, Inc., Beijing 100095, China;
2. Tianjin GeoEnviron Water Treatment Technology Co., Ltd., Tianjin 300041, China

摘要

摘要: 研究AI对污泥太阳能干化系统的赋能作用，用智能视觉系统提升污泥太阳能干化系统运行效率和安防智慧化程度，简化配置，提升系统安全性和稳定性。以此为基础，运用大数据孪生模型，分层次自动比对分析各层因子的影响作用，找出关键表征性控制指标，简化控制流程，提升系统运行效率，加强节能降耗效果。运行结果表明，出泥含水率可以用造粒干化区长度高度表征调控，二者具有高度线性关系；在系统孪生最佳运行工况下，系统非冬季污泥日处理量达设计值2.4倍，日脱水量4.85 t，平均脱水能力达8.84 kg/（m²·d）；系统冬季运转较非智慧化污泥太阳能干化系统能耗降低29.9%，较低温带式干化技术能耗降低49.4%，出泥含水率均达标；系统运转稳定，目标出泥含水率控制精准，综合节能降耗效果明显。
- 人工智能 /
- AI /
- 孪生 /
- 太阳能 /
- 污泥干化 /
- 优化调控 /
- 智能视觉
Abstract: In the context of clean energy development， AI intelligent innovation， and achieving carbon neutrality， there is an increasing amount of research on the application of artificial intelligence technology in energy conservation and consumption reduction in water treatment. However， there are currently no cases of integrating artificial intelligence technology with solar sludge drying technology. This study explores the application of AI in a sludge solar drying system， aiming to enhance the efficiency and security， simplify the system configuration， and improve safety and stability with intelligent visual systems. Based on this， the study applied a big data digital twin model to automatically compare and analyze the influence of each layer factor， identified key representative indicators for control， simplified the control process， and enhanced system efficiency. The results showed that the dewatered sludge moisture content could be efficiently regulated by the length of the granulation drying zone， which exhibited a highly linear relationship； under the optimal operating conditions， the non-winter sludge treatment capacity increased from the designed 3.00 t/d to 7.21 t/d（2.4 times the design value）， the water removal amout reached 4.85 t/d， and the average water removal capacity was 8.84 kg/（m²·d）. The energy consumption of the system during winter operation decreased from 240.5 kW·h/t to 168.5 kW·h/t， which was 29.9% lower than that of the non-smart sludge solar drying systems and 49.4% lower than that of low-temperature belt drying systems. The system operated stably， the target sludge moisture content was controlled accurately， and the comprehensive energy-saving and emission-reduction effects were significant. Through a comparative analysis of multiple cases， the economical ranking of various sludge drying technologies is as follows： solar drying > low-temperature belt drying > steam aeration drying > thin-layer drying machine.
- artificial intelligence /
- AI /
- digital twin /
- solar energy /
- sludge drying /
- optimization and regulation /
- intelligent vision

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参考文献(15)

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