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

WANG Weilong; ZHANG Mengna; LIU Zhaopeng; ZHEN Shengli; LI Qin; LONG Shaopeng

doi:10.13205/j.hjgc.202507007

Volume 43 Issue 7

Jul. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(7): 64-72

WANG Weilong, ZHANG Mengna, LIU Zhaopeng, ZHEN Shengli, LI Qin, LONG Shaopeng. Application of a sludge drying system with AI-coupled solar technology under Dual Carbon goals[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(7): 64-72. doi: 10.13205/j.hjgc.202507007

Citation:

WANG Weilong, ZHANG Mengna, LIU Zhaopeng, ZHEN Shengli, LI Qin, LONG Shaopeng. Application of a sludge drying system with AI-coupled solar technology under Dual Carbon goals[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(7): 64-72. doi: 10.13205/j.hjgc.202507007

Citation:

WANG Weilong, ZHANG Mengna, LIU Zhaopeng, ZHEN Shengli, LI Qin, LONG Shaopeng. Application of a sludge drying system with AI-coupled solar technology under Dual Carbon goals[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(7): 64-72. doi: 10.13205/j.hjgc.202507007

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

doi: 10.13205/j.hjgc.202507007

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

Received Date: 2024-09-03
Accepted Date: 2024-11-15
Rev Recd Date: 2024-10-26

Available Online: 2025-09-11

Abstract

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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References(15)

References

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