Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

IF 7.7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Bowen Li , Li Liu , Zikang Xu , Kexun Li
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Abstract

Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) has become a powerful tool for WWTP, and is a challenge due to more complex multidimensional pattern recognition. Herein, weighted average ensemble strategy was conducted to assemble multiple diverse basic models to obtain better prediction capability to optimize carbon source addition (Model-1) and further control surplus sludge yield (Model-2). The ensemble models significantly outperformed all single models with MAE of 5.82 g/m3, MSE of 60.59 and R2 value of 0.98 in Model-1 and MAE of 15.09 g/m3, MSE of 449.01 and R2 value of 0.93 in Model-2. The optimal input feature subset was explored to reduce model complexity, indicating that the final ensemble models can predict with high precision using relatively few features with MAE of 6.41 g/m3, MSE of 78.49 and R2 value of 0.97 in Model-1 and MAE of 12.82 g/m3, MSE of 232.71 and R2 value of 0.95 in Model-2. Furthermore, the final models were deployed into an offline web application to facilitate their utility in real-world settings, demonstrating 47.25 % savings in carbon source addition and 15.89 % reductions in surplus sludge yield for an extra month of running. This work offers an efficient approach for the WWTP to optimize carbon source addition and provides new insights into controlling surplus sludge yield.
基于机器学习的可解释集成模型优化碳源添加以控制剩余污泥产量。
适当的碳源添加可以节省运行成本,减少污水处理厂的剩余污泥产量。然而,尽管机器学习(ML)已成为污水处理的强大工具,但碳源和剩余污泥产量之间的联系仍然被忽视,并且由于更复杂的多维模式识别而成为一项挑战。本文采用加权平均集合策略,对多个不同的基础模型进行组合,以获得更好的预测能力,从而优化碳源添加(模型1),进一步控制剩余污泥产量(模型2)。模型1的MAE为5.82 g/m3, MSE为60.59,R2值为0.98;模型2的MAE为15.09 g/m3, MSE为449.01,R2值为0.93,集合模型显著优于所有单一模型。为降低模型复杂度,探索了最优输入特征子集,结果表明,模型1的MAE为6.41 g/m3, MSE为78.49,R2值为0.97,模型2的MAE为12.82 g/m3, MSE为232.71,R2值为0.95,最终的集成模型可以使用相对较少的特征实现较高的预测精度。此外,最终模型被部署到离线web应用程序中,以促进其在实际环境中的应用,结果表明,在额外一个月的运行中,碳源添加减少了47.25%,剩余污泥产出量减少了15.89%。本研究为污水处理厂优化碳源添加提供了有效途径,并为控制剩余污泥产量提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Research
Environmental Research 环境科学-公共卫生、环境卫生与职业卫生
CiteScore
12.60
自引率
8.40%
发文量
2480
审稿时长
4.7 months
期刊介绍: The Environmental Research journal presents a broad range of interdisciplinary research, focused on addressing worldwide environmental concerns and featuring innovative findings. Our publication strives to explore relevant anthropogenic issues across various environmental sectors, showcasing practical applications in real-life settings.
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