Abatement of Sewer Overflow Pollution Based on Distributed Optimal Control Approach

Abstract

Optimal control is promising to mitigate the pollution from combined sewer overflows during wet-weather days. In this study, a distributed optimal control approach to regulating multipoint controlling units of the urban sewer system was developed. By introducing a differential evolution algorithm with ranking-based mutation operators, good simulation results with lower overflow mass loadings can obtain higher evolutionary probabilities. Thus, the optimal solutions to minimize the overflow mass from a large sewer network can be efficiently obtained. The developed approach was demonstrated in a catchment (total coverage area of 22.4 km 2 ) served by one wastewater treatment plant in Chaohu City, Anhui Province of China, with five pumping stations and 18 associated sewage or stormwater pumps as the distributed controllers. After a global optimization among various controlling units, performance of the sewer network in reducing its overflow was improved obviously through fully utilizing in-sewer storage and adjusting the sewer flow transportation harmoniously (i.e., an alternative mode of full storing and fast transportation in sequence). Accordingly, the overflow volume and the overflow mass loading of total nitrogen was reduced by 32.3% and 37.6%, respectively, without retrofitting the current sewer network. On the basis of the presented approach, decision-makers can develop a database for optimal regulation of sewer network under various rainfall scenarios and respond to sewer overflow control flexibly and cost-effectiveness.

Retrofitting low-performance units to abate sewer overflow pollution based on mathematical model and Sobol algorithm

“…As seen in Figure 1, the subcatchments of the sewer system are connected by five pumping stations, including two stations with both stormwater and sewage lifting pumps (pumping stations I and II), two stations with sewage lifting pumps (pumping stations III and IV), and one terminal station for lifting WWTP inflow (pumping station V). The detailed information concerning the five pumping stations is provided in our previous study [2] . Operational records of the terminal influent pumping system show that the daily WWTP-treated volume in the dry weather period was approximately 47,000 m 3 , while the maximum daily treatment capacity of WWTP during wet weather periods was about 61,000 m 3 (i.e., 1.3 times to dry weather days' capacity).…”

“…For this study site, the developed model consists of 12 subcatchments, 212 sewage pipes, 5 pumping stations, and 7 overflow outlets. Information for each divided subcatchment and background discharges connecting to the sewer network under dryweather period are presented in our previous study [2,30] .…”

“…Therefore, the design scenario of a one-year return period was employed to study the retrofit of LPUs and CSO pollution reduction. The Chicago approach was used to generate a synthetic hyetograph of a one-year event based on the local stormwater intensity formula [2] . Accordingly, the designed instantaneous rainfall intensity under a one-year return period is provided in Figure 6(a).…”

“…Combined sewer systems that collect sewage and stormwater runoff are often overloaded during wet weathers owing to the insufficient interception capacities [1,2] . As a result, a large amount of untreated sewage overflows into receiving waterbodies instead of being intercepted into wastewater treatment plant (WWTP).…”

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…Other contributions introduce novel methodologies that combine ML with statistical approaches to address various environmental problems, such as sewer overflow pollution abatement, fault detection in water and wastewater treatment, , an assay for source apportionment of per- and polyfluorinated substances (PFAS), detection of freshwater algae, and influent water data . Beyond water quality, ML has been applied to model water quantity, exploring dominant factors influencing urban industrial wastewater discharges, model energy consumption of wastewater treatment, identify endocrine-active pollutants in the organic Unregulated Contaminant Monitoring Rule (UCMR 1–4) and their toxic potentials, and employ quantitative biodescriptors to predict in vivo toxicity …”