Intelligent upgrade of waste-activated sludge dewatering process based on artificial neural network model: Core influential factor identification and non-experimental prediction of sludge dewatering performance.
Intelligent upgrade of waste-activated sludge dewatering process based on artificial neural network model: Core influential factor identification and non-experimental prediction of sludge dewatering performance.
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DOI：
10.1016/j.jenvman.2023.118968
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发表时间：
2023-09
影响因子：
8.7
通讯作者：
Hewei Li;Chunjiang Li;Kun Zhou;Wei Ye;Yufei Lu;Xiaoli Chai;Xiaohu Dai;Boran Wu
中科院分区：
文献类型：
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作者：
Hewei Li;Chunjiang Li;Kun Zhou;Wei Ye;Yufei Lu;Xiaoli Chai;Xiaohu Dai;Boran Wu
由于活性污泥的组成和来源极其复杂，其脱水性能受到多种理化性质的影响，且多种理化性质之间存在复杂的相互作用关系，难以确定影响脱水性能的控制因素。因此，不同来源的污泥脱水性能最佳的理化性质的适宜范围还没有统一的确定性，导致污泥脱水工艺的技术选择和优化缺乏明确的理论依据。现有污泥调理技术存在调理剂用量大、处理效率低等问题。采用非线性、自适应、自组织的人工神经网络（ANN）模型对影响WAS脱水性能的多种理化性质进行综合，以WAS的多种理化性质和预处理操作参数为输入变量，预测WAS在一定预处理方案下的脱水性能。因此，人工神经网络模型的输入调整可以代替筛选调理剂的繁琐的过滤实验。均方根误差（RMSE）为6.51，决定系数（R2）为0.73，证实了所建立的神经网络模型具有良好的稳定性和准确性。预测-排除法显示，极性界面自由能的排除降低最多，反映了表面亲水性降低在污泥脱水性能改善中的重要性。本文的研究成果对提高WAS脱水工艺的经验操作水平具有一定的指导意义。
Owing to the extremely complex compositions and origins of waste-activated sludge (WAS), the multiple physiochemical properties of WAS have impacts on its dewaterability, and there is a complex interaction relationship among the multiple physiochemical properties, which makes it difficult to identify the controlling factors on WAS dewaterability. Accordingly, there is still no unified certainty in the appropriate ranges of physiochemical properties for the optimal dewaterability of sludge from different sources, resulting in a lack of clear theoretical basis for technical selection and optimization of sludge dewatering processes. The large consumption of conditioning chemicals and low process efficiency stand for the major deficiency of existing sludge conditioning technologies. This study proposed to use a non-linear, adaptive and self-organizing artificial neural network (ANN) model to integrate the multiple physiochemical properties of WAS affecting its dewaterability, and WAS dewatering performance under certain conditioning schemes could be predicated by ANN model with the multiple physiochemical properties and conditioning operation parameters as the input arguments. Thus, the laborious filtration experiments for screening conditioning chemicals could be replaced by the input adjustment of ANN model. Rooted mean squared error (RMSE) of 6.51 and coefficient of determination (R2) of 0.73 confirmed the satisfied stability and accuracy of established ANN model. Furthermore, the predictor-exclusive method revealed that the exclusion of polar interface free energy decreased most, which reflected the importance of surface hydrophilicity reduction in sludge dewaterability improvement. All the contributions presented here were believed to provide an intelligent insight to improve the experience operation status of WAS dewatering process.