A learning-based approach towards the data-driven predictive control of combined wastewater networks – An experimental study

Balla, Krisztian Mark; Bendtsen, Jan Dimon; Schou, Christian; Kallesøe, Carsten Skovmose; Ocampo‐Martínez, Carlos

doi:10.1016/j.watres.2022.118782

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…Inflows, water levels in the basins, accumulated overflow and the control actions are shown regarding the upstream pumping station (left column) and the downstream pumping station (right column). (Source: [Balla et al, 2022a]. )…”

Section: Gp-based Control Resultsmentioning

confidence: 99%

“…The setup and the schematics of the network configuration are shown in Figure 3.4. Source: [Balla et al, 2022b], [Balla et al, 2022a] Throughout the thesis, the laboratory setup enabled us to prototype control algorithms without the risk of compromising the operation of real-world infrastructure. The setup replicates a segment of a combined sewer network, where sewage is transported from an upstream pumping station to a downstream station through a gravity pipe.…”

Section: Experimental Studymentioning

confidence: 99%

“…Threshold values h, h define the maximum and minimum allowed water levels in the storage element, respectively. Source: [Balla et al, 2022a] As depicted in Figure 3.7, the pumps turn on and off according to the levels reaching the higher and lower bounds of the operating region. Violation of the upper bound indicates high load on the system, hence the longer pump cycles.…”

Section: Benchmark Controllermentioning

confidence: 99%

“…Figure 3.7: Rule-based control implemented in real-time on the Experimental Study setup.Source:[Balla et al, 2022a] …”

mentioning

confidence: 99%

“…Sensors are deployed to learn the effect of disturbances through in-sewer variations of the water level. Source:[Balla et al, 2022a].…”

mentioning

confidence: 99%

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Real-time Data-driven Modelling and Predictive Control of Wastewater Networks

Balla¹

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Section: Gp-based Control Resultsmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Section: Benchmark Controllermentioning

confidence: 99%

“…Figure 3.7: Rule-based control implemented in real-time on the Experimental Study setup.Source:[Balla et al, 2022a] …”

mentioning

confidence: 99%

“…Sensors are deployed to learn the effect of disturbances through in-sewer variations of the water level. Source:[Balla et al, 2022a].…”

mentioning