Département de Génie Chimique, École Polytechnique Montréal, C.P.6079 Succ., Centre-Ville Montréal, Montreal, QC, H3C 3A7, Canada.
National Research Council of Canada, 6100 Royalmount Ave., Montreal, QC, H4P 2R2, Canada.
Bioprocess Biosyst Eng. 2018 Apr;41(4):543-553. doi: 10.1007/s00449-017-1889-5. Epub 2018 Feb 2.
Efforts in developing microbial electrolysis cells (MECs) resulted in several novel approaches for wastewater treatment and bioelectrosynthesis. Practical implementation of these approaches necessitates the development of an adequate system for real-time (on-line) monitoring and diagnostics of MEC performance. This study describes a simple MEC equivalent electrical circuit (EEC) model and a parameter estimation procedure, which enable such real-time monitoring. The proposed approach involves MEC voltage and current measurements during its operation with periodic power supply connection/disconnection (on/off operation) followed by parameter estimation using either numerical or analytical solution of the model. The proposed monitoring approach is demonstrated using a membraneless MEC with flow-through porous electrodes. Laboratory tests showed that changes in the influent carbon source concentration and composition significantly affect MEC total internal resistance and capacitance estimated by the model. Fast response of these EEC model parameters to changes in operating conditions enables the development of a model-based approach for real-time monitoring and fault detection.
开发微生物电解池 (MEC) 的努力产生了几种新颖的废水处理和生物电合成方法。这些方法的实际实施需要开发一种适当的系统，以便对 MEC 性能进行实时（在线）监测和诊断。本研究描述了一种简单的 MEC 等效电路 (EEC) 模型和参数估计程序，可实现这种实时监测。所提出的方法涉及在周期性电源连接/断开（开/关操作）期间进行 MEC 电压和电流测量，然后使用模型的数值或解析解进行参数估计。该监测方法使用带有流动多孔电极的无膜 MEC 进行了演示。实验室测试表明，进水碳源浓度和组成的变化会显著影响模型估算的 MEC 总内阻和电容。这些 EEC 模型参数对操作条件变化的快速响应使基于模型的实时监测和故障检测方法的开发成为可能。
