查看更多>>摘要：Industries such as non-ferrous metal smelting discharge billions of gallons of highly toxic heavy metal wastewater(HMW)worldwide annually,posing a severe challenge to conventional wastewater treat-ment plants and harming the environment.HMW is traditionally treated via chemical precipitation using lime,caustic,or sulfide,but the effluents do not meet the increasingly stringent discharge standards.This issue has spurred an increase in research and the development of innovative treatment technologies,among which those using nanoparticles receive particular interest.Among such initiatives,treatment using nanoscale zero-valent iron(nZVI)is one of the best developed.While nZVI is already well known for its site-remediation use,this perspective highlights its application in HMW treatment with metal recovery.We demonstrate several advantages of nZVI in this wastewater application,including its mul-tifunctionality in sequestrating a wide array of metal(loid)s(＞30 species);its capability to capture and enrich metal(loid)s at low concentrations(with a removal capacity reaching 500 mg g-1 nZVI);and its operational convenience due to its unique hydrodynamics.All these advantages are attributable to nZVI's diminutive nanoparticle size and/or its unique iron chemistry.We also present the first engineer-ing practice of this application,which has treated millions of cubic meters of HMW and recovered tons of valuable metals(e.g.,Cu and Au).It is concluded that nZVI is a potent reagent for treating HMW and that nZVI technology provides an eco-solution to this toxic waste.
查看更多>>摘要：The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback.This review elucidates the foundational elements of digital twins,covering their concept,entities,domains,and key technologies.More specifically,we investigate the transformative potential of digital twins for the wastewater treatment engineering sector.Our discussion highlights the application of digital twins to wastewater treatment plants(WWTPs)and sewage networks,hardware(i.e.,facilities and pipes,sensors for water quality and activated sludge,hydrodynamics,and power consumption),and software(i.e.,knowledge-based and data-driven models,mechanistic models,hybrid twins,control methods,and the Internet of Things).Furthermore,two cases are provided,followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs.This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.
查看更多>>摘要：Tracing the contamination origins in water sources and identifying the impacts of natural and human processes are essential for ecological safety and public health.However,current analysis approaches are not ideal,as they tend to be laborious,time-consuming,or technically difficult.Disinfection byprod-ucts(DBPs)are a family of well-known secondary pollutants formed by the reactions of chemical disin-fectants with DBP precursors during water disinfection treatment.Since DBP precursors have various origins(e.g.,natural,domestic,industrial,and agricultural sources),and since the formation of DBPs from different precursors in the presence of specific disinfectants is distinctive,we argue that DBPs and DBP precursors can serve as alternative indicators to assess the contamination in water sources and identify pollution origins.After providing a retrospective of the origins of DBPs and DBP precursors,as well as the specific formation patterns of DBPs from different precursors,this article presents an overview of the impacts of various natural and anthropogenic factors on DBPs and DBP precursors in drinking water sources.In practice,the DBPs(i.e.,their concentration and speciation)originally present in source water and the DBP precursors determined using DBP formation potential tests-in which water samples are dosed with a stoichiometric excess of specific disinfectants in order to maximize DBP formation under certain reaction conditions-can be considered as alternative metrics.When jointly used with other water quality parameters(e.g.,dissolved organic carbon,dissolved organic nitrogen,fluorescence,and molecular weight distribution)and specific contaminants of emerging concern(e.g.,certain pharmaceu-ticals and personal care products),DBPs and DBP precursors in drinking water sources can provide a more comprehensive picture of water pollution for better managing water resources and ensuring human health.
查看更多>>摘要：The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising solution.Here,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML models.The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and interpretability.Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4％and a coefficient of deter-mination of 0.948.Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8％compared to traditional fuzzy control methods.The potential application of these strategies in critical water science domains is discussed.To foster accessibility and promote wide-spread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.
查看更多>>摘要：Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the per-spective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by cluster-ing its influent's parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kWh·m-3 despite low influent concentration and volumes,across four consumption levels from low(Ⅰ)to relatively high(Ⅳ),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O2·m-3,1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.
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