School of Automation, Central South University, Changsha, 410 083, China.
School of Automation, Central South University, Changsha, 410 083, China.
J Environ Manage. 2022 Apr 1;307:114491. doi: 10.1016/j.jenvman.2022.114491. Epub 2022 Jan 29.
Parameters monitoring is essential to maintain the stability and efficiency of the wastewater treatment process, which has spurred ubiquitous installation of sensors in wastewater treatment plants (WWTPs). As the rich process data of WWTPs is not effectively transformed into actionable knowledge for system optimization due to improper sensor installation, the sensor placement scheme needs to be optimized. In this paper, a weighted sensor placement optimization model based on sensor cost, information richness and reliability is established to transform the sensor optimization problem to a nonlinear mathematical programming problem. Then a discrete multi-objective state transition algorithm is proposed to find the Pareto optimal solutions. Finally, an evaluation strategy is designed to select the most suitable solution for industrial application. The results of simulation experiments on three different WWTPs demonstrate the validity and superiority of the proposed method, increasing the degree of variable observability and measurement redundancy while keeping the sensor cost at a low level.
参数监测对于维持废水处理过程的稳定性和效率至关重要，这促使在废水处理厂（WWTP）中广泛安装传感器。由于 WWTP 的丰富过程数据由于传感器安装不当而无法有效转化为系统优化的可操作知识，因此需要优化传感器的放置方案。在本文中，建立了一个基于传感器成本、信息丰富度和可靠性的加权传感器放置优化模型，将传感器优化问题转化为非线性数学规划问题。然后提出了一种离散多目标状态转移算法来寻找帕累托最优解。最后，设计了一种评估策略来选择最适合工业应用的解决方案。在三个不同的 WWTP 上的仿真实验结果验证了所提方法的有效性和优越性，在保持传感器成本较低的同时，提高了变量可观测度和测量冗余度。
