Papermaking Wastewater Treatment Research Articles

Preparation of super high concentration cationic polyacrylamide emulsion and its flocculation with cationic polymers

A super high concentration cationic polyacrylamide emulsion (CPAME‐uhc) was prepared by inverse emulsion polymerization. The influence of its cationic degree and molecular weight on flocculation property was discussed. Then, the chemical structure and micromorphology of the obtained CPAME‐uhc were characterized by Fourier infrared spectrum (FT‐IR), NMR hydrogen spectrum (1H NMR), gel permeation chromatography (GPC), and scanning electron microscope (SEM). Finally, the synergistic flocculation effects of CPAME‐uhc respectively with polydimethyldiallylammonium chloride (PDADMAC), poly methacryloyloxyethyl trimethyl ammonium chloride (PDMC), cationic starch (CS), cationic guar gum (CHPG), cationic chitosan (CTS), and polyamine (PA) on the CPAME‐uhc were investigated. The results showed that the optimum cationic degree and molecular weight of CPAME‐uhc were 25% and 12 million, respectively. FT‐IR and 1H NMR analysis showed that the product was a copolymer of acryloxyethyl trimethyl ammonium chloride and acrylamide. GPC analysis confirmed that the molecular weight distribution of CPAME‐uhc product was very narrow. SEM shows that the latex particles of CPAME‐uhc are regular spheres with a diameter of 200 to 500 nm. When CPAME‐uhc is respectively combined with five cationic polymers in a certain proportion, all of them show good synergistic flocculation to papermaking wastewater. The order of synergistic flocculation effect is: CPAME‐uhc/PDADMAC > CPAME‐uhc/PDMC > CPAME‐uhc/CTS > CPAME‐uhc/CS > CPAME‐uhc/CHPG > CPAME‐uhc/PA. Finally, the mechanism of synergistic flocculation was proposed. This study provides a theoretical and research basis for the preparation of CPAME‐uhc and its application in the treatment of papermaking wastewater.

Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine

Papermaking wastewater accounts for a large proportion of industrial wastewater, and it is essential to obtain accurate and reliable effluent indices in real-time. Considering the complexity, nonlinearity, and time variability of wastewater treatment processes, a dynamic kernel extreme learning machine (DKELM) method is proposed to predict the key quality indices of effluent chemical oxygen demand (COD). A time lag coefficient is introduced and a kernel function is embedded into the extreme learning machine (ELM) to extract dynamic information and obtain better prediction accuracy. A case study for modeling a wastewater treatment process is demonstrated to evaluate the performance of the proposed DKELM. The results illustrate that both training and prediction accuracy of the DKELM model is superior to other models. For the prediction of the quality indices of effluent COD, the determinate coefficient of the DKELM model is increased by 27.52 %, 21.36 %, 10.42 %, and 10.81 %, compared with partial least squares, ELM, dynamic ELM, and kernel ELM, respectively.

Catalytic ozonation treatment of papermaking wastewater by Ag-doped NiFe2O4: Performance and mechanism

The catalytic ozonation treatment of secondary biochemical effluent for papermaking wastewater by Ag-doped nickel ferrite was investigated. Ag-doped catalysts prepared by sol-gel method were characterized, illustrating that Ag entirely entered the crystalline of NiFe2O4 and changed the surface properties. The addition of catalyst enhanced the removal efficiency of chemical oxygen demand and total organic carbon. The results of gas chromatography-mass spectrometer, ultraviolet light absorbance at 254 nm and three-dimensional fluorescence excitation-emission matrix suggested that aromatic compounds were efficiently degraded and toxic substances, such as dibutyl phthalate. In addition, the radical scavenging experiments confirmed the hydroxyl radicals acted as the main reactive oxygen species and the surface properties of catalysts played an important role in the reaction. Overall, this work validated potential applications of Ag-doped NiFe2O4 catalyzed ozonation process of biologically recalcitrant wastewater.

A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment

Recycling wastewater of the pulping and paper-making industry are widely considered for clean production, which heavily rely on the timely and accurate monitoring in paper-making wastewater treatment processes. A novel predicting model based on genetic-deep belief network algorithm was proposed to improve the predictive accuracy and reliability for process monitoring. Considering the deep belief networks (DBN) as a deep learning model is aiming to describe the relationship among variables in a complex process modeling, genetic algorithm (GA) was employed to reduce the input variables dimensionality, simplify the network structure and overcome the dynamic characteristic difficulties of process data in monitoring. Compared with DBN and back propagation neural network (BPNN), the GA-DBN effectively achieved a better predictive accuracy than other tests models in complex wastewater treatment processes. The value of the coefficient of determination of GA-DBN model is increased by 1.71–1.86% and 5.21–9.32%, respectively. The GA-DBN model can be structured with fewer variables or samples to describe the complex paper-making wastewater treatment process, obtaining the better model performance and predictive accuracy.

Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes.

The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SSeff) and effluent chemical oxygen demand (CODeff). In terms of SSeff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of CODeff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.

Preparation, characterization, and application of magnetic activated carbon for treatment of biologically treated papermaking wastewater

In view of the urgent need for tertiary treatment of papermaking wastewater and the difficulty in separating powdered activated carbon (PAC) from water, the magnetic activated carbon (33%-MPAC, 50%-MPAC and 67%-MPAC) were prepared by chemical coprecipitation method for adsorption of biologically treated papermaking wastewater (BTPW). A series of characterization of MPAC and PAC were carried out and show that the content of iron oxides is negatively related to the proportion of micropores in MPAC. The loaded iron oxides is mainly the mixture of magnetite and maghemite, and the maximum saturation magnetization of MPAC can reach 29.68 emu/g. Batch mode experiments were performed, and found that the adsorption effect of MPAC is slightly worse than that of PAC, the adsorption capacity of COD in MPAC can reach about 65 mg/g, and pH = 2 and 10 °C are more favorable for adsorption. The adsorption isotherms and kinetics were well fitted by the Freundlich model and pseudo-second-order kinetic model, respectively. The selective adsorption was studied by using the excitation emission matrix (EEM) fluorescence spectrum and high-performance size exclusion chromatography (HPSEC). It is concluded that all adsorbents are preferred to adsorb humic acid-like substances (HA). And all adsorbents are preferred to adsorb low apparent molecular weight substances (LAMW, AMW < 1500 Da), with the increase of iron oxides content, the phenomenon of MPAC preferentially adsorbed LAMW became less obvious.

Effluent Quality Prediction of Papermaking Wastewater Treatment Processes Using Stacking Ensemble Learning

Advanced process modeling methods have been used for prediction and monitoring of key quality indices in wastewater treatment processes. However, single conventional models usually have limited precision accuracy when predicting the effluent indices in papermaking wastewater treatment processes. To achieve a better prediction accuracy and robustness, we propose a stacking ensemble learning (SEL) method which utilizes the advantages of the internal base-learning models. The method combines base-learning algorithms including partial least squares, support vector regression, and artificial neural networks with a meta-learning algorithm, which is a multiple-response linear regression in this work. To evaluate the model performance in practical applications, both real wastewater data and simulation wastewater data are used for modeling. The predicted effluent indices include effluent suspended solid (SS $_{\mathrm {eff}}$ ), effluent chemical oxygen demand (COD $_{\mathrm {eff}}$ ), effluent ammonia concentration ( $\text{S}_{\mathrm {NHeff}}$ ), and effluent nitrate concentration ( $\text{S}_{\mathrm {NOeff}}$ ). Compared with base-learning algorithms and other ensemble learning methods, the results demonstrate that SEL significantly improves the prediction accuracy and reduces the prediction errors, which provides a new way to achieve real-time monitoring of wastewater treatment processes.

Synthesis of Petal-Like MnO2 Nanosheets on Hollow Fe3O4 Nanospheres for Heterogeneous Photocatalysis of Biotreated Papermaking Effluent.

Owing to the implementation of increasingly stringent water conservation policies and regulations, the pulp and paper mill industry must make increased efforts to meet the limits for pollutant emissions. The primary pretreatment and secondary biochemical treatment methods used currently generally fail to meet the country-specific environmental regulations, and the wastewater must be processed further even after being subjected to secondary biochemical treatments. In this work, we synthesized Fe3O4/MnO2 nanocomposites (FMNs) with a flower-like structure for use in the heterogeneous photocatalytic treatment of biotreated papermaking wastewater. FMNs1.25, which were formed using a KMnO4/Fe3O4 molar ratio of 1.25, could be separated readily using an external magnetic field and exhibited higher photocatalytic activity than those of the other samples as well as MnO2 and Fe3O4. The effects of various experimental parameters on the photocatalytic activity of FMNs1.25, including the initial pH of the wastewater and the catalyst dosage, were determined. The common chemical oxygen demand (CODCr) reduction rate in the case of this sample reached 56.58% within 120 min at a pH of 3, the CODCr of effluent after treatment was 52.10 mg/L. Further, even under neutral conditions, the CODCr of the treated effluent was below the current limit for discharge in China. Moreover, the nanocomposites exhibited good recyclability, and their catalytic activity did not decrease significantly even after five usage cycles. This study should serve as a platform for the fabrication of effective photocatalysts for the advanced treatment of biotreated papermaking effluent and refractory organic wastewater.

Isolation of bacteria capable of hydrogen production in dark fermentation and intensification of anaerobic granular sludge activity

In the anaerobic biological treatment of pulp and papermaking wastewater, the gradual deposition of CaCO3 eventually leads to the inhibition of the activity of anaerobic granular sludge. In this study, a hydrogen production bacterial Raoultella DW01 was isolated from domesticated anaerobic granular sludge. The fermentation conditions were designed using central composite design, and the optimum conditions obtained by response surface analysis encompassed an initial pH 5.77, 4.13 g/L l-glutamic acid and an inoculation amount of 15%. The H2 production yield represented a 29.5% increase over the unoptimized conditions. Finally, the effect of adding DW01 on the biogas production in anaerobic granular sludge with different sludge ages was investigated. The cumulative biogas yield and the max biogas production rate increased by 27.8% and 53.5% after adding DW01 to a sludge with an age of 335 days compared with the on-intensified sludge. This paper provides a way to alleviate the CaCO3 deposition by intensifying the activity of H2 and acid-producing bacteria via improving the activity of granular sludge.

Treatment of paper mill wastewater using a composite inorganic coagulant prepared from steel mill waste pickling liquor

In this study, a waste pickling liquor from a steel mill, which is rich in iron and acid, was used as the main raw material to prepare a composite coagulant: polymeric ferric aluminum sulfate chloride (PFASC). The as-prepared PFASC was used, together with PAM, for the tertiary treatment of papermaking wastewater to decrease its COD and Chroma. The Results from FT-IR and XRD of PFASC, supported the conclusion that polymeric ferric aluminum hydroxide hydrates compounds/ complexes are formed in the synthesized PFASC. When applied to a waste water sample from a paper mill, PFASC, together PAM, leads to decreases in COD and Chroma by 65.3% and 71.2%, respectively (initial pH 7.5, 1 ml/L PFASC, 1.0 ppm PAM). The technology has been implemented at a paper mill, and the obtained results are consistent with those from the laboratory.

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

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.

Effect of the Ca2+ concentration on anaerobic digestion and microbial communities of granular sludge

The effects of substrate Ca2+ concentration were studied relative to the anaerobic digestion and microbial community structure of anaerobic granular sludge. In the treatment of papermaking wastewater by anaerobic granular sludge, the cumulative gas production was maximum at a Ca2+ concentration in the substrate of 120 mg/L, whereas it was the lowest at a concentration of Ca2+ in the substrate of 4000 mg/L. A high Ca2+ concentration in the substrate (≥1200 mg/L) will cause the pH of the fermentation broth to decrease during the fermentation process, which is not conducive to the anaerobic fermentation of the methanogenic process, resulting in unsatisfactory anaerobic digestion of the granular sludge. In addition, when the Ca2+ concentration was below 200 mg/L, the abundance of the important bacterial family Ruminococcaceae (rumen bacteria) in the anaerobic fermentation hydrolysis stage was drastically reduced and methane gas production increased. When the Ca2+ concentration was above 200 mg/L, the abundance of Anaerolineaceae (Anaerobic Streptomyces), which supplies organic acids, was substantially reduced. The methane gas production decreased as the Ca2+ concentration increased. Thus, the results showed that when the concentration of Ca2+ was above 200 mg/L, the methanogenic activity of granular sludge decreased.

Soft-sensing modeling of chemical oxygen demand in photo-electro-catalytic oxidation treatment of papermaking wastewater

Photo-electro-catalytic (PEC) oxidation has been widely recognized as an effective technology for advanced treatment of papermaking wastewater. To optimize the oxidation process, it is important of monitor continuously the chemical oxygen demand (COD) of inflow and outflow wastewater. However, online COD sensors are expensive difficult to maintain, and therefore COD is usually analyzed off-line in laboratories in most cases. The objective of this study is to develop an inexpensive method for on-line COD measurement. The oxidation-reduction potential (ORP), pH, and dissolved oxygen (DO) of wastewater were selected as the key parameters, which consists of four different types of artificial neural network (ANNs) methods:multi-layer perceptron neural network (MLP), back propagation neural network (BPNN), radial basis neural network (RBNN) and generalized regression neural network (GRNN). These parameters were applied in the development of COD soft-sensing models. Six batches of papermaking wastewater with different pollution loads were treated with PEC technology over a period of 90 minutes, and a total of 546 data points was collected, including the on-line measurements of ORP, pH and DO, as well as off-line COD data. The 546 data points were divided into training set (410 data, 75% of total) and validation set (136 data, 25% of total). Four statistical criteria, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R²) were used to assess the performance of the models developed with the training set of data. The comparison of results for the four ANN models for COD soft-sensing indicated that the RBNN model behaved most favorably, which possessed precise and predictable results with R²=0.913 for the validation set. Lastly, the proposed RBNN model was applied to a new batch of PEC oxidation of papermaking wastewater, and the results indicated that the model could be applied successfully for COD soft-sensing for the wastewater.

Improved removal performance and mechanism investigation of papermaking wastewater treatment using manganese enhanced Fenton reaction.

The effects of Mn(II) on Fenton system to treat papermaking wastewater and the mechanism of Mn(II) enhanced Fenton reaction were investigated in this study. The chemical oxygen demand (COD) removal efficiency was enhanced in the presence of Mn(II), which increased by 19% compared with that of the Fenton system alone. The pseudo-first order reaction kinetic rate constant of Mn(II)/Fenton system was 2.11 times higher than that of Fenton system. 67%-81% COD were removed with the increasing Mn(II) concentration from 0 to 0.8 g/L. COD removal efficiency was also enhanced in a wider pH range (3-7), which indicated the operation parameters of Fenton technology could be broadened to a milder condition. The study of the mechanism showed that Mn(II) participated in the oxidation and coagulation stages in Fenton system. In the oxidation stage, Mn(II) promotes the production of HO2•/ O2•-, then HO2•/ O2•- reacts with Fe(III) to accelerate the formation of Fe(II), and finally accelerates the production of HO•. Meantime MnMnO3 and Fe(OH)3 forms in the coagulation stage, facilitating the removal of suspended substances and a large amount of COD, which enhances the overall COD removal of papermaking wastewater. This study provided a detailed mechanism to improve practical applications of Fenton technology.

Application of Chitin/Chitosan and Their Derivatives in the Papermaking Industry.

Chitin/chitosan and their derivatives have become of great interest as functional materials in many fields within the papermaking industry. They have been employed in papermaking wet-end, paper surface coating, papermaking wastewater treatment, and other sections of the papermaking industry due to their structure and chemical properties. The purpose of this paper is to briefly discuss the application of chitin/chitosan and their derivatives in the papermaking industry. The development of their application in the papermaking area will be reviewed and summarized.

Contrast Experiment on Advanced Treatment of Pharmaceutical and Paper-making Wastewater through Cinder Fenton-like Process

The Fenton-like process of catalyzing H2O2 with Fe2+ and cinder is adopted to subject pharmaceutical and paper-making wastewater to advanced treatment. The influence of each factor is determined using orthogonal experiment and single factor test. The optimal combination of influencing factors is 0.3mmol · L-1 of FeSO4 · 7H2O, [H2O2]:[Fe2+]=8:1 and 10g · L-1 of pyrites cinder. The reaction time in pharmaceutical wastewater and paper-making wastewater is 30min and 60min respectively, testifying to the fact that the reaction in pharmaceutical wastewater is faster than that in paper-making wastewater and the lower utilization rate of cinder in pharmaceutical wastewater. Under the optimal reaction condition, the COD removal rate of these two kinds of wastewater can reach as high as 65% and 72%. Characterized by simple operation and requiring less reagent dosage, this method does not have to regulate the pH of flooding water and allows the repeated usage of cinder.

Advanced Treatment of Paper-Making Wastewater Using Catalytic Ozonation with Waste Rice Straw-Derived Activated Carbon-Supported Manganese Oxides as a Novel and Efficient Catalyst

Advanced Treatment of Paper-Making Wastewater Using Catalytic Ozonation with Waste Rice Straw-Derived Activated Carbon-Supported Manganese Oxides as a Novel and Efficient Catalyst

Study on the Mass Transfer Enhancement in Biofilms Applied in Papermaking Wastewater Treatment

The research and refinement of papermaking wastewater treatment and reuse technology are important measures for energy conservation and emission reduction in the papermaking industry. This paper studied the process of biofilm formation and dissolved oxygen mass transfer of biofilms cultivated under different aeration intensities and attempted to enhance the biofilm reactor performance. The removal efficiencies of the chemical oxygen demand, total nitrogen, and ammonia nitrogen through biofilm treatment in two parallel biofilm reactors were higher under the larger aeration intensity (8 L/min) than under the smaller intensity (4 L/min). Macroscopically, this reflected the effect of dissolved oxygen on nitrogen removal. Microscopically, in terms of the dissolved oxygen profiles inside of the biofilms determined using a microelectrode probe, both aerobic and anaerobic layers occurred inside the biofilms, which suggested that simultaneous nitrification and denitrification occurred. The different aeration intensities led to differences in the internal and external dissolved oxygen concentrations in the biofilms, which affected the biofilm growth. This led to different micro-structures, and so the internal metabolism and wastewater treatment performance of the biofilms were not identical.

Modeling and simulation of the industrial sequencing batch reactor wastewater treatment process for cleaner production in pulp and paper mills

Being an internationally accepted standard for the activated sludge modeling, the Activated Sludge Model No.1 (ASM1) was used to simulate the treatment of paper mills effluent in an industrial full-scale sequencing batch reactor (SBR). Key characteristic parameters were estimated and corrected with the wastewater temperature: maximum heterotrophic growth rate μH (9.69/day), heterotrophic yield YH (0.625 g cell COD/g COD oxidized) and heterotrophic decay rate bH (1.98/day). The wastewater chemical oxygen demand (COD) was fractionated as slowly biodegradable substrate XS (20%), particulate inert organic matter XI (58%), readily biodegradable substrate SS (18%) and soluble inert organic matter SI (4%). Finally, the SBR operation was simulated with field data from the paper mill. The average relative error of the simulated effluent COD was 12.7%. The results showed that the ASM1 could be reasonably used in the papermaking wastewater treatment simulation.

Effect of Silicon Dose and pH on Coagulation Performance and Floc Fractal Dimension of Poly-Silicic-Cation Coagulant

Abstract Poly-silicic-cation coagulants (PSiCs) were prepared from industrial wastes. Optimal conditions for synthesis of PSiCs were determined from orthogonal experiments. Coagulation performance was evaluated through jar tests in treatment of pulping and papermaking wastewater, and fractal dimensions of the flocs during coagulation were analyzed on basis of floc images on GRViewer. Results showed that coagulation performance and floc fractal dimension were significantly influenced by the Si/(Fe + Al) molar ratio and the pH of PSiCs. Optimal Si/(Fe + Al) ratio and pH were 0.8 and 1.5, respectively. Turbidity removal rate from pulping and papermaking wastewater decreased with the increasing Si/(Al + Fe) ratio, which increased with the rise of pH, but decreased at high pH. The floc fractal dimension was improved with the increase of silicon dose or pH, but decreased when the Si/(Fe + Al) ratio or pH was high. Adsorption bridging ability of PSiCs played a key role in the aggregation and breakup of flocs.