A hybrid supervisory system to support WWTP operation: implementation and validation

Abstract

Integrated operation of Wastewater Treatment Plants is still far from being solved. A reasonable proposal should link advanced and robust control algorithms to some knowledge-based techniques, allocating the detailed engineering to numerical computations, while delegating the logical analysis and reasoning to supervisory intelligent systems. This paper describes the development and implementation of a knowledge-based Hybrid Supervisory System to support the operation of a real Wastewater Treatment Plant. The system integrates different reasoning modules, overcoming the limitations in the use of each single technique, while providing an agent based architecture with additional modularity and independence. It is structured into three separated levels: data gathering, diagnosis, and decision support. The different tasks of the system are performed in a seven-step cycle: data gathering and update, diagnosis, supervision, prediction, communication, actuation, and evaluation phase. In spite of certain reservations of the scientific community about the use of these techniques, the system is successfully performing real-time support to the operation of the Granollers facility since September 1999. Results of the first four-month validation period are shown and discussed. An example of the system behavior is also shown in the paper. The conclusions indicate the key steps which are necessary to transfer the system to another facility.

Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

“…Process 3 is described by a parabolic function, valid for the period between the start of the storm event and the moment of the maximum dilution. The length of this period, the duration of the onset a 3 , is determined during model calibration. This process is necessary to account for the delayed dilution observed in the monitoring data, see Figure 5.…”

“…Modelling of wastewater treatment plants (WWTPs) using activated sludge models (ASM) has become a standard in both industry and academia for a range of objectives, amongst others WWTP design [1,2], operation [3], and control [4,5]. Especially the latter objective, developing control strategies and assessing the performance of control strategies requires high frequency influent data [6].…”

Decision Support Systems for Integrated Water Resources Management Under Water Scarcity

“…Others describe optimizing water allocation [45], case-based reasoning systems and rule-based systems for wastewater management of conventional systems [6,8,10,11,[46][47][48][49][50], and natural systems [12,51] or fuzzy systems and genetic algorithms for modeling rainfall [52] and calibrating a rainfall-runoff model [53]. Different AI techniques such as neural networks, genetic algorithms, and intelligent control are also used recently for the modeling and management of rain water reservoirs in real time [54].…”

Constructed Wetlands: Prediction of Performance with Case-based Reasoning (Part B)

“…Case-based reasoning has been successfully applied to the development and implementation of a knowledgebased hybrid supervisory system to support the operation of a real wastewater treatment plant (Rodriguez- Roda et al, 2002). The CBR system can be structured into three separated levels: data gathering, diagnosis, and decision support.…”

“…The CBR system can be structured into three separated levels: data gathering, diagnosis, and decision support. The different tasks of the system can be performed in a seven-step cycle: data gathering and update, diagnosis, supervision, prediction, communication, actuation, and evaluation (Rodriguez- Roda et al, 2002).…”