Virtual sensors for the Hias process

Tiina M. Komulainen, A. M. Baqeri, Katrine Marsteng Jansen, T. Saltnes, Axel Tveiten Bech, Olga Korostynska
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Abstract

This article presents the development of virtual sensors for estimation of phosphates (PO4) and soluble COD profiles in a novel, continuous flow, moving bed bioreactor with enhanced biological phosphorus removal and simultaneous nitrification and denitrification, the Hias process. The virtual sensors combine online measurements with additional electrical conductivity (EC), oxidation–reduction potential (ORP) measurements, and state-space models at inlet, zone 3 and zone 7. The data were collected from Hias municipal WRRF, Norway from March to July 2023, and include both online data and laboratory data. Input variables were selected using correlation plots. Linear measurement equations were fitted to relate PO4 and COD concentrations in the laboratory data set with the online measurements including EC/ORP measurements. The state-space models were identified from the online data with model accuracy from moderate to strong. The estimated PO4 and COD concentrations correspond to most of the scarce laboratory data points at inlet and zone 3, whereas the model in zone 7 requires more work. A Kalman filter was developed for zone 3 and implemented in KYB industrial internet of things (IIoT) platform. Future work is suggested on improvement of the model accuracy in zone 7, and development of energy-efficient control strategies using the virtual sensors.
希阿斯工艺的虚拟传感器
本文介绍了虚拟传感器的开发情况,该传感器用于估算新型连续流移动床生物反应器中的磷酸盐(PO4)和可溶性化学需氧量曲线,该生物反应器具有增强的生物除磷功能,可同时进行硝化和反硝化,即 Hias 工艺。虚拟传感器将在线测量与额外的电导率 (EC)、氧化还原电位 (ORP) 测量以及入口、3 区和 7 区的状态空间模型相结合。数据收集自 2023 年 3 月至 7 月的挪威希阿斯市 WRRF,包括在线数据和实验室数据。输入变量通过相关图进行选择。通过拟合线性测量方程,将实验室数据集中的 PO4 和 COD 浓度与在线测量值(包括 EC/ORP 测量值)联系起来。根据在线数据确定了状态空间模型,模型精度从中等到较高不等。估算出的 PO4 和 COD 浓度与进水口和 3 区大部分稀缺的实验室数据点相吻合,而 7 区的模型则需要更多的工作。针对第 3 区开发了卡尔曼滤波器,并在 KYB 工业物联网 (IIoT) 平台上实施。未来的工作建议是提高第 7 区的模型精度,并利用虚拟传感器开发节能控制策略。
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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