In this study, three types of adaptive neuro fuzzy inference system (ANFIS) were employed to predict effluent suspended solids (SS
eff), chemical oxygen demand (COD
eff), and pH
eff from a wastewater ...treatment plant in industrial park. For comparison, artificial neural network (ANN) was also used. The results indicated that ANFIS statistically outperformed ANN in terms of effluent prediction. The minimum mean absolute percentage errors of 2.67%, 2.80%, and 0.42% for SS
eff, COD
eff, and pH
eff could be achieved using ANFIS. The maximum values of correlation coefficient for SS
eff, COD
eff, and pH
eff were 0.96, 0.93, and 0.95, respectively. The minimum mean square errors of 0.19, 2.25, and 0.00, and the minimum root mean square errors of 0.43, 1.48, and 0.04 for SS
eff, COD
eff, and pH
eff could also be achieved. ANFIS’s architecture can overcome the limitations of traditional neural network. It also revealed that the influent indices could be applied to the prediction of effluent quality.
The presence of heavy metals in wastewater is one of the main causes of water and soil pollution. The aim of the present study was to investigate the removal of Cd, Cu, Pb, Hg, Mn, Cr and Zn in urban ...effluent by a biological wastewater treatment, as well as to quantify the levels of As, Be, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sn, Tl, V and Zn in dewatering sludge from the Biological Wastewater Treatment Plant of Ribeirão Preto (RPBWTP), Brazil.
Concentrations of Cd, Cr, Cu, Mn and Pb in wastewater and those of Ni in sludge were determined by atomic absorption spectrophotometry with graphite furnace atomization. Mercury concentrations in wastewater were measured by hydride generation atomic spectrophotometry, and Zn levels were determined by atomic absorption spectrophotometry using acetylene flame. In sludge, the levels of As, Be, Cd, Cr, Cu, Fe, Hg, Mn, Pb, Sn, TI, V and Zn were determined by inductively coupled plasma-mass spectrometry. Results. The percentages of removal efficiency (RE) were the following: Hg 61.5%, Cd 60.0%, Zn 44.9%, Cu 44.2%, Pb 39.7%, Cr 16,5% and Mn 10.4%. In turn, the mean concentrations (mg/kg) of metals in dewatering sludge followed this increasing order: Tl (< 0.03), Hg (0.31), Be (0.43), As (1.14), Cd (1.34), V (59.2), Pb (132.1), Sn (166.1), Cr (195.0), Mn (208.1), Ni (239.4), Cu (391.7), Zn (864.4) and Fe (20537).
The relationship between metal levels in untreated wastewater, as well as the removal efficiency are in agreement with previous data from various investigators. It is important to note that metal removal efficiency is not only affected by metal ion species and concentration, but also by other conditions such as operating parameters, physical, chemical, and biological factors.
Metal values recorded for treated wastewater and sludge were within the maximum permitted levels established by the Environmental Sanitation Company (CETESB), São Paulo, Brazil.
There is an urgent need for the authorities who are responsible for legislation on sludge uses in agriculture of establishing safety levels for As, Be, Hg, Sn, Tl and V.
According to the current metal levels, RP-BWTP sludge might be used for agriculture purposes. However, for an environmentally safe use of sewage sludge, further studies including systematic monitoring are recommended. Annual metal concentrations and predicted variations of those elements in the sludge should be monitored.
Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SSeff), chemical ...oxygen demand (CODeff), and pHeff in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% for SSeff, CODeff and pHeff could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff, CODeff, and pHeff were 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SSeff, CODeff, and pHeff could also be achieved.
Celem niniejszej pracy była analiza zmienności koncentracji metali ciężkich (Cu, Ni, Cd, Pb, Zn, Cr, Hg) w ściekach surowych (dopływających), osadzie czynnym oraz ściekach oczyszczonych pobieranych z ...terenu Komunalnej mechaniczno – biologicznej Oczyszczalni Ścieków w Reczu. Dokonano także oceny poziomu akumulacji metali ciężkich w odwodnionych i ustabilizowanych osadach ściekowych wcześniej zdeponowanych i aktualnie powstających na terenie Miejskiej Oczyszczalni Ścieków w Reczu oraz przedstawiono możliwości ich zagospodarowania. Podjęto także próbę obniżenia ponadnormatywnych zawartości metali ciężkich w nagromadzonych osadach ściekowych na drodze ich kompostowania z dodatkiem materiałów strukturotwórczych (odpadów organicznych) oraz oceniono właściwości uzyskanych kompostów i możliwości ich przyrodniczego wykorzystania
The aim of this work was to analyze the variability of the concentration of heavy metals (Cu, Ni, Cd, Pb, Zn, Cr, Hg) in raw (incoming) wastewater, active sediment and purified wastewater collected from the municipal mechanical and biological sewage treatment plant in Recza. The level of accumulation of heavy metals in dehydrated and stabilized sewage sludge previously deposited and currently occurring in the Municipal Wastewater Treatment Plant in Recza was also assessed and the possibilities of their development were presented. An attempt was also made to reduce the oversize content of heavy metals in accumulated sewage sludge by composting them with the addition of structural materials (organic waste) and assessed the properties of compostes and the possibilities of their natural use
A biologic wastewater treatment plant was identified as a common source for 2 consecutive Legionnaires' disease clusters in the Netherlands in 2016 and 2017. Sequence typing and transmission modeling ...indicated direct and long-distance transmission of Legionella, indicating this source type should also be investigated in sporadic Legionnaires' disease cases.
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