High-fidelity profiling and modeling of heterogeneity in wastewater systems using milli-electrode array (MEA): Toward high-efficiency and energy-saving operation
High-fidelity profiling and modeling of heterogeneity in wastewater systems using milli-electrode array (MEA): Toward high-efficiency and energy-saving operation
复制标题
使用毫电极阵列 (MEA) 对废水系统中的异质性进行高保真分析和建模：实现高效节能运行
DOI：
10.1016/j.watres.2019.114971
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发表时间：
2019
期刊：
影响因子：
12.8
通讯作者：
Stuber, Matthew D.
中科院分区：
文献类型：
--
作者：
Xu, Zhiheng;MahmoodPoor Dehkordy, Farzaneh;Li, Yan;Fan, Yingzheng;Wang, Tianbao;Huang, Yuankai;Zhou, Wangchi;Dong, Qiuchen;Lei, Yu;Stuber, Matthew D.
对于目前使用传统“单点”探头监测并采用手动或自动开环控制策略操作的废水处理系统来说，高能耗是一个关键问题，表现出明显的时滞。本研究通过使用扁平薄毫电极阵列 (MEA) 传感器以高时空分辨率实时模式分析三个关键水质参数（电导率、温度和 pH 值）沿反应器深度的变化来解决这一挑战。该分析准确地捕获了反应器在瞬态冲击（电导率和 pH 值）和缓慢持续冲击（温度）下的异质状态，为优化废水处理系统的化学品剂量和能源需求提供了有效的数据集。开发瞬态冲击模型是为了验证 MEA 剖面并计算传质系数。蒙特卡罗模拟显示，与只能通过单一过程状态监控整个系统的传统“单点”传感器相比，高分辨率 MEA 分析与快速闭环控制策略相结合，可以节省 59.50% 的能耗（温度和耗氧量控制）和 45.29% 的化学剂量，并且比基准（理想条件定义）性能提高 16.28%。这项研究证明了 MEA 传感器能够描绘反应器非均质性、以高分辨率可视化水质变化、为精确控制提供完整的数据集，并最终实现具有高弹性的节能运行。
High energy consumption is a critical problem for wastewater treatment systems currently monitored using conventional “single point” probes and operated with manual or automatic open-loop control strategies, exhibiting significant time lag. This challenge is addressed in this study by profiling the variation of three critical water quality parameters (conductivity, temperature and pH) along the depth of a reactor at high spatiotemporal resolution in a real-time mode using flat thin milli-electrode array (MEA) sensors. The profiling accurately captured the heterogeneous status of the reactor under transient shocks (conductivity and pH) and slow lingering shock (temperature), providing an effective dataset to optimize the chemical dosage and energy requirement of wastewater treatment systems. Transient shock models were developed to validate the MEA profiles and calculate mass transfer coefficients. Monte Carlo simulation revealed high-resolution MEA profiling combined with fast closed-loop control strategies can save 59.50% of energy consumption (Temperature and oxygen consumption controls) and 45.29% of chemical dosage, and reach 16.28% performance improvement over the benchmark (defined with ideal conditions), compared with traditional “single-point” sensors that could only monitor the entire system through a single process state. This study demonstrated the capability of MEA sensors to profile reactor heterogeneity, visualize the variation of water quality at high resolution, provide complete datasets for accurate control, and ultimately lead to energy-saving operation with high resilience.