模仿珊瑚礁群智，实现碳中和的污水处理新方法。
A Biomimetic Way for Coral-Reef-Inspired Swarm Intelligence for Carbon-Neutral Wastewater Treatment
- 借鉴珊瑚礁生态结构设计群智能网络，提升系统可扩展性。
- 处理效率达96.7%，能耗仅0.31 kWh/m³，碳排放14.2 g/m³。
- 适用于海岛、酿酒厂等复杂场景，适合环境工程与智能系统研究者。
随着污水排放量增加，实现能源自给的净化处理面临挑战。本文提出一种受珊瑚礁启发的群交互网络，结合形态发生抽象与多任务碳感知，实现碳中和污水处理。该方法具备线性令牌复杂度，有效缓解能耗问题。相比七种基线模型，本方法在去除率上达到96.7%，能耗为0.31 kWh/m³，CO₂排放为14.2 g/m³。方差分析显示系统对传感器漂移具有鲁棒性。在岛屿潟湖、啤酒厂峰值负荷及沙漠温室等实地场景中，最高可节省22%柴油。然而，数据科学人员配置仍是瓶颈。未来工作将集成AutoML组件，但治理限制带来可解释性挑战，需进一步发展可视化分析工具。
原文摘要 · Abstract (English)
With increasing wastewater rates, achieving energy-neutral purification is challenging. We introduce a coral-reef-inspired Swarm Interaction Network for carbon-neutral wastewater treatment, combining morphogenetic abstraction with multi-task carbon awareness. Scalability stems from linear token complexity, mitigating the energy-removal problem. Compared with seven baselines, our approach achieves 96.7\% removal efficiency, 0.31~kWh~m$^{-3}$ energy consumption, and 14.2~g~m$^{-3}$ CO$_2$ emissions. Variance analysis demonstrates robustness under sensor drift. Field scenarios--insular lagoons, brewery spikes, and desert greenhouses--show potential diesel savings of up to 22\%. However, data-science staffing remains an impediment. Future work will integrate AutoML wrappers within the project scope, although governance restrictions pose interpretability challenges that require further visual analytics.
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