A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Abstract

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

The Bellinge data set: open data and models for community-wide urban drainage systems research

“…A comprehensive data set of one structure, heavily monitored during 5 days of experiment has been released (Moy De Vitry et al, 2017), and similar is needed for networks systems. Time 55 series of observations of levels and flows in the system will also be necessary for many investigations of e.g., model calibration techniques (Krebs et al, 2013;Vonach et al, 2019), development of improved skill scores (Bennett et al, 2013), uncertainty analysis (Deletic et al, 2012), techniques for data quality control (Kirstein et al, 2019;Therrien et al, 2020), development of data-driven models and machine learning (Carbajal et al, 2017;Eggimann et al, 2017;Palmitessa et al, 2021) and software sensors (Fencl et al, 2019). Other areas that can be inspired by open data sharing could be the construction of digital ecosystems 60 (Sarni et al, 2019) and digital twins (Pedersen et al, 2021b;Therrien et al, 2020).…”

Living and Prototyping Digital Twins for Urban Water Systems: Towards Multi-Purpose Value Creation Using Models and Sensors

“…There is currently no clear consensus in the scientific literature on the meaning of the term DT, which results in a very broad and fuzzy application of the DT concept and a dilution of the terminology related to the DT concept [5,23]. Models play important roles in DTs, but as explained below, a DT has many features, including simulation models.…”

“…Autonomous cars [37], water distribution systems [4], oil and gas industry [38], or urban drainage systems (as discussed in this paper). Plant WRRF [5] or drinking water facilities [12] Unit Process/Operation, Hydraulic Structure DTs of overflow structures, other complicated hydraulic constructions, or biochemical processes in the WRRF treatment step [39] Component e.g., pumping devices [40] guided by the DT for maintenance of the product.…”

“…These have been identified as key elements for water treatment and distribution systems, but the importance of being "hydraulically accurate" and using "short time steps" has also been highlighted [12]. Therrien et al (2020) [5] discussed the DT concept from a WRRF perspective and suggested that the key components of a DT are a virtual system capable of simulating a physical system, measurements, the real-time exchange of data, predictions, and intelligent actions. The authors concluded that a consensus on what components are required to call a digital system a DT is strongly needed to avoid misusing this powerful concept and added that this consensus-building has already begun in the field of water and wastewater networks.…”

“…The DT concept was originally developed in the manufacturing industry with, among other things, the intent to prolong the lifetime of products by not only looking at the design of products, but also exploring how the products work in reality by transmitting information from each product back to the manufacturer during the use phase [2]. The DT concept is currently being explored in several application areas, such as health, meteorology, education, and cities [3], as well as the area of urban water systems [4,5], where investments are high, and incorrect investments can be detrimental to public health and the environment. For example, utilities in Denmark spend 1 billion euros annually for the expansion and rehabilitation of urban drainage systems [6].…”