Data-derived soft-sensors for biological wastewater treatment plants: An overview

被引:184
|
作者
Haimi, Henri [1 ]
Mulas, Michela [1 ]
Corona, Francesco [2 ]
Vahala, Riku [1 ]
机构
[1] Aalto Univ, Sch Engn, Dept Civil & Environm Engn, FI-00076 Aalto, Finland
[2] Aalto Univ, Sch Sci, Dept Informat & Comp Sci, FI-00076 Aalto, Finland
关键词
Water quality monitoring; Soft-sensors; Data-driven models; Wastewater treatment; PARTIAL LEAST-SQUARES; SEQUENCING BATCH REACTOR; ACTIVATED-SLUDGE PROCESS; ARTIFICIAL NEURAL-NETWORKS; PRINCIPAL COMPONENT ANALYSIS; PERFORMANCE EVALUATION; SYSTEM-IDENTIFICATION; PATTERN-RECOGNITION; PHOSPHORUS REMOVAL; ONLINE ESTIMATION;
D O I
10.1016/j.envsoft.2013.05.009
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper surveys and discusses the application of data-derived soft-sensing techniques in biological wastewater treatment plants. Emphasis is given to an extensive overview of the current status and to the specific challenges and potential that allow for an effective application of these soft-sensors in full-scale scenarios. The soft-sensors presented in the case studies have been found to be effective and inexpensive technologies for extracting and modelling relevant process information directly from the process and laboratory data routinely acquired in biological wastewater treatment facilities. The extracted information is in the form of timely analysis of hard-to-measure primary process variables and process diagnostics that characterize the operation of the plants and their instrumentation. The information is invaluable for an effective utilization of advanced control and optimization strategies. (c) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:88 / 107
页数:20
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