scholarly journals On-line chemical oxygen demand estimation models for the photoelectrocatalytic oxidation advanced treatment of papermaking wastewater

2018 ◽  
Vol 78 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Wenhao Shen ◽  
Feini Huang ◽  
Xuewen Zhang ◽  
Yuefei Zhu ◽  
Xiaoquan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Chemical Oxygen Demand ◽  
Batch Reactor ◽  
Estimation Method ◽  
Oxygen Demand ◽  
Paper Mill ◽  
Advanced Treatment ◽  
On Line ◽  
Line Analysis ◽  
Papermaking Wastewater

Abstract Chemical oxygen demand (COD), an important indicative measure of the amount of oxidizable pollutants in wastewater, is often analyzed off-line due to the expensive sensor required for on-line analysis. However, its off-line analysis is time-consuming. An on-line COD estimation method was developed with photoelectrocatalytic (PEC) technology. Based on the on-line data of the oxidation–reduction potential (ORP), dissolved oxygen (DO) and pH of wastewater, four different artificial neural network methods were applied to develop working models for COD estimation. Six different batches of sequence batch reactor (SBR) effluent from a paper mill were treated with PEC oxidation for 90 minutes, and 546 data points were collected from the on-line measurements of ORP, DO and pH, and the off-line COD analysis. After having training and validation with 75% and 25% of data, and evaluation with four statistical criteria (R2, RMSE, MAE and MAPE), the estimation results indicated that the developed radial basis neural network (RBNN) model demonstrated the highest precision. Subsequently, the application of the RBNN model to a new batch of SBR effluent from the paper mill revealed that the RBNN model was acceptable for COD estimation during the PEC advanced treatment process of papermaking wastewater, which implied its possible application in the future.

Degradation of wine distillery wastewaters by the combination of aerobic biological treatment with chemical oxidation by Fenton's reagent

2005 ◽  
Vol 51 (1) ◽  
pp. 167-174 ◽  
Author(s):  
J. Beltran de Heredia ◽  
J. Torregrosa ◽  
J.R. Dominguez ◽  
E. Partido
Keyword(s):  
Chemical Oxygen Demand ◽  
Aromatic Compounds ◽  
Biological Treatment ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Chemical Oxidation ◽  
Utilization Rate ◽  
Fenton’S Reagent ◽  
Kinetic Rate Constant ◽  
Fenton's Reagent

The degradation of wine distillery wastewaters by aerobic biological treatment has been investigated in a batch reactor. The evolution of the chemical oxygen demand, biomass and total contents of polyphenolic and aromatic compounds was followed through each experiment. According to the Contois model, a kinetic expression for the substrate utilization rate is derived, and its biokinetic constant is evaluated. The final effluents of the aerobic biological experiments were oxidized by Fenton's reagent. The evolution of chemical oxygen demand, hydrogen peroxide concentration and total contents of polyphenolic and aromatic compounds was followed through each experiment. A kinetic model to interpret the experimental data is proposed. The kinetic rate constant of the global reaction is determined.

scholarly journals TiO2 BEADS FOR PHOTOCATALYTIC DEGRADATION OF HUMIC ACID IN PEAT WATER

2011 ◽  
Vol 11 (3) ◽  
pp. 253-257 ◽  
Author(s):  
Winarti Andayani ◽  
Agustin N M Bagyo
Keyword(s):  
Aqueous Solution ◽  
Humic Acid ◽  
Oxalic Acid ◽  
Chemical Oxygen Demand ◽  
Photocatalytic Degradation ◽  
Uv Light ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Color Intensity ◽  
Before And After

Degradation of humic acid in aqueous solution containing TiO2 coated on ceramics beads under irradiation of 254 nm UV light has been conducted in batch reactor. The aim of this experiment was to study photocatalytic degradation of humic acid in peat water. The irradiation of the humic acid in aqueous solution was conducted in various conditions i.e solely uv, in the presence of TiO2-slurry and TiO2 beads. The color intensity, humic acid residue, conductivity and COD (chemical oxygen demand) of the solution were analyzed before and after irradiation.  The compounds produced during photodegradation were identified using HPLC. The results showed that after photocatalytic degradation, the color intensity and the COD value of the solution decreased, while the conductivity of water increased indicating mineralization of the peat water occurred. In addition, oxalic acid as the product of degradation was observed.

The Study of Prediction of Agricultural Source Pollution Emissions of Ammonia Nitrogen (AN) and Chemical Oxygen Demand (COD) Based on Bionic Neural Network Algorithm

2013 ◽  
Vol 461 ◽  
pp. 544-552 ◽  
Author(s):  
Hong Peng Guo ◽  
Gan Yu Feng ◽  
Chun Xia Liu ◽  
Xiao Yi Zhang
Keyword(s):  
Neural Network ◽  
Chemical Oxygen Demand ◽  
Rapid Development ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
Average Error ◽  
Research Results ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Pollution Emissions

Nearly 40% of Chinese water pollution comes from agricultural sources of pollution, and the annual emissions are difference. If we want to control pollution emissions effectively, we need to accurately predict the amount of agricultural emissions of Ammonia Nitrogen (AN) and Chemical Oxygen Demand (COD). Due to the complex mechanism of the agricultural non-point source pollution, its emissions are very difficult to measure. Currently, the Bionics Research is in a stage of rapid development, and it continues to expand into many new areas of research. So the comprehensive study of Bionics and pollutant control study will be a good choice. This research used bionic BP（Back Propagation） neural network algorithm, and used pollution census data from 2002 to 2007 and established neural network model with neural network algorithm. And we predicted the agricultural sources of emissions of AN and COD with the data from 2008 to 2010. Finally we compared the predicted value and the actual value. Research results showed that, with using the bionic BP neural network, agricultural sources emissions of AN and COD are evaluated actually and the results indicate that the average error is under 5.0%. Research results proved that the model is effective. The neural network is a scientific predict method for the agricultural sources emissions of AN and COD. It can be widely used in the prediction of agricultural sources emissions of AN and COD.

scholarly journals Mn/sepiolite as the heterogeneous ozonation catalysts applied to the advanced treatment of regenerated-papermaking wastewater

2016 ◽  
Vol 75 (5) ◽  
pp. 1025-1033 ◽  
Author(s):  
Zheng Cheng ◽  
Rendang Yang ◽  
Yang Wang
Keyword(s):  
Reaction Pathway ◽  
Removal Rate ◽  
Oxygen Demand ◽  
Catalytic Ozonation ◽  
Treated Wastewater ◽  
Precipitation Method ◽  
Advanced Treatment ◽  
Heterogeneous Catalytic ◽  
High Removal Rate ◽  
Papermaking Wastewater

Herein a Mn-deposited sepiolite catalyst was obtained through a facile co-precipitation method, and then used as the heterogeneous ozonation catalysts applied to the tertiary treatment of regenerated-papermaking wastewater. During the process, the as-prepared catalyst was endowed with higher Brunauer–Emmett–Teller specific surface area of 412.3 m2/g compared to 124.6 m2/g of the natural sepiolite. Hence, in the adsorption of methylene blue, the as-prepared catalyst was observed with a very high removal rate of 96.2% although a little lower than the modified sepiolite of 97.5% in 100 min. And for practical application, the catalyst was used for treating the effluent from regenerated-papermaking industry, via a heterogeneous catalytic ozonation process. Consequently, the highest color removal rate of 99.5%, and the highest chemical oxygen demand (COD) removal efficiency of 73.4% were achieved in 20 and 30 min, respectively. As a result, the treated wastewater was more biodegradable and less toxic; the biochemical oxygen demand (BOD5)/COD value could reach 0.41. Moreover, the catalyst showed superior stability at successive ozonation runs. The main possible reaction pathway is also presented. The results indicate that catalytic ozonation was proved to be effective when Mn/sepiolite was used as catalysts applied to the advanced treatment of regenerated-papermaking wastewater.

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