Purification and utilization of garlic processing wastewater in lotus pond wetlands

Based on the experiments of utilization of garlic processing wastewater in a lotus pond, this study demonstrates that lotus pond wetlands have a remarkable ability to remove organic pollutants and decrease chemical oxygen demand （CODCr）, biochemical oxygen demand （BOD5）, and suspended substances （SS） in garlic processing wastewater. Results also show evident effects of lotus roots on absorption of NH3-N. The pH value in a lotus pond with wastewater discharged was relatively stable. The water quality in the lotus pond reached the class Ⅱ emission standard, according to the Integrated Wastewater Discharge Standard （GB8978-1996）, seven days after pretreated garlic processing wastewater had been discharged into the lotus pond. Garlic processing wastewater irrigation does not produce pollution in the pond sediment and has no negative effect on the growth of lotus roots. Due to utilization of garlic processing wastewater, the output of lotus roots increased by 3.0% to 8.3%, and the quality of lotus roots was improved. Therefore, better purification and utilization results can be achieved.

A modified active disturbance rejection control for a wastewater treatment process

Waste water treatment process(WWTP)control has been attracting more and more attention.However,various undesired factors,such as disturbance,uncertainties,and strong nonlinear couplings,propose big challenges to the control of a WWTP.In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality,rather than take the model dependent control approaches,an active disturbance rejection control(ADRC)is utilized.Based on the control signal and system output,a phase optimized ADRC(POADRC)is designed to control the dissolved oxygen and nitrate concentration in a WWTP.The phase advantage of the phase optimized extended state observer(POESO),convergence of the POESO,and stability of the closed-loop system are analyzed from the theoretical point of view.Finally,a commonly accepted benchmark simulation model no.1.(BSM1)is utilized to test the POESO and POADRC.Linear active disturbance rejection control(LADRC)and the suggested proportion-integration(PI)control are taken to make a comparative research.Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control.Numerical results show that,as a result of the leading phase of the total disturbance estimation,the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.

Non-thermal plasma treatment of Radix aconiti wastewater generated by traditional Chinese medicine processing

The wastewater effluent from Radix aconiti processing, an important step in the production processes of traditional Chinese medicine（TCM）, is a type of toxic wastewater and difficult to treat. Plasma oxidation methods have emerged as feasible techniques for effective decomposition of toxic organic pollutants. This study examined the performance of a plasma reactor operated in a dielectric barrier discharge（DBD） to degrade the effluent from R. aconiti processing. The effects of treatment time, discharge voltage, initial pH value and the feeding gas for the reactor on the degradation of this TCM wastewater were investigated. A bacterium bioluminescence assay was adopted in this study to test the toxicity of the TCM wastewater after non-thermal plasma treatment. The degradation ratio of the main toxic component was 87.77% after 60 min treatment with oxygen used as feed gas and it was 99.59% when the initial p H value was 8.0. High discharge voltage and alkaline solution environment were beneficial for improving the degradation ratio. The treatment process was found to be capable of reducing the toxicity of the wastewater to a low level or even render it non-toxic. These experimental results suggested that the DBD plasma method may be a competitive technology for primary decomposition of biologically undegradable toxic organic pollutants in TCM wastewater.

New application of microbial fuel cell-based biosensor for monitoring the quality of actual potato chips’processing wastewater

In this study,a dual-chamber microbial fuel cell(MFC)fed with actual potato chips’processing wastewater(PCPW)was tested as a biosensor.The performance of MFC-based biosensor was evaluated in terms of the current measurement range,toxicity detection and sensitivity,and the operational stability.The results revealed that the MFC can simply be converted to an online biosensor unit to detect the harmful effect of suspended solids and acidic content in the actual PCPW on the anodic attached biofilm and the values of the generated current as well.A notable decrease in the current values was observed indicating the adverse effects of the harmful matters in the PCPW fed to the biosensor unit.The results proposed a competition between the harmful components and the favorable substrate in binding to the redox complex.An excellent fitting was obtained between the experimental and predicted results by I_(Km) model with determination coefficient(R^(2))and mean-square-error values of 0.927 and 0.363,respectively.Additionally,a new approach was developed based on direct measurement of actual field data to replace the conventional statistical methods.

Conversion of fish processing wastewater into fish feed ingredients through submerged cultivation of Aspergillus oryzae

Fish processing towards production of fillet gives rise to wastewater streams that are ultimately directed to biogas production and/or wastewater treatment.However,these wastewater streams are rich in minerals,fat,and proteins that can be converted to protein-rich feed ingredients through submerged cultivation of edible filamentous fungi.In this study,the origin of wastewater stream,initial pH,cultivation time,and extent of washing during sieving,were found to influence the amount of recovered material from the wastewater streams and its protein content,following cultivation with Aspergillus oryzae.Through culti-vation of the filamentous fungus in sludge,330 kg of material per ton of COD were recovered by sieving,corresponding to 121 kg protein per ton of COD,while through its cultivation in salt brine,210 kg of material were recovered per ton of COD,corresponding to 128 kg protein per ton of COD.Removal ranges of 12-43%,39-92%,and 32-66%for COD,total solids,and nitrogen,respectively,were obtained after A.oryzae growth and harvesting in the wastewater streams.Therefore,the present study shows the versatility that the integration of fungal cultivation provides to fish processing industries,and should be complemented by economic,environmental,and feeding studies,in order to reveal the most promising valorization strategy.

Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is studied.Unlike existing control strategies that control DO concentration at a fixed value, here we develop a different control framework.Under the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.

Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

Wastewater treatment plays a crucial role in alleviating water shortages and protecting the environment from pollution.Due to the strong time variabilities and complex nonlinearities within wastewater treatment systems,devising an efficient optimal controller to reduce energy consumption while ensuring effluent quality is still a bottleneck that needs to be addressed.In this paper,in order to comprehensively consider different needs of the wastewater treatment process(WTTP),a two-objective model is to consider a scope,in which minimizing energy consumption and guaranteeing effluent quality are both considered to improve wastewater treatment efficiency.To efficiently solve the model functions,a grid-based dynamic multi-objective evolutionary decomposition algorithm,namely GD-MOEA/D,is designed.A GD-MOEA/D-based intelligent optimal controller(GD-MOEA/D-IOC)is devised to achieve tracking control of the main operating variables of the WTTP.Finally,the benchmark simulation model No.1(BSM1)is applied to verify the validity of the proposed approach.The experimental results demonstrate that the constructed models can catch the dynamics of WWTP accurately.Moreover,GD-MOEA/D has better optimization ability in solving the designed models.GD-MOEA/D-IOC can achieve a significant improvement in terms of reducing energy consumption and improving effluent quality.Therefore,the designed multi-objective intelligent optimal control method for WWTP has great potential to be applied to practical engineering since it can easily achieve a highly intelligent control in WTTP.

Fate and degradation of nonylphenolic compounds during wastewater treatment process

In order to explore the biodegradation behavior of nonylphenolic compounds during wastewater treatment processing, two full-scale wastewater treatment plants were investigated and batch biodegradation experiments were conducted. The biodegradation pathways under the various operational conditions were identified from batch experiments： shortening of ethoxy-chains dominated under the anaerobic condition, whereas oxidizing of the terminal alcoholic group prevailed over the other routes under the aerobic condition. Results showed that the anoxic condition could accelerate the biodegradation rates of nonylphenolic compounds, but had no influence on the biodegradation pathway. The biodegradation rates of nonylphenol （NP） and short-chain nonylphenol polyethoxylates （NPnEOs, n： number of ethoxy units） increased from the anaerobic condition, then the anoxic, finally to the aerobic condition, while those of long-chain NPnEOs and nonylphenoxy carboxylates （NPECs） seemed similar under the various conditions. Under every operational condition, long-chain NPnEOs showed the highest biodegradation activity, followed by NPECs and short-chain NPnEOs, whereas NP showed relatively recalcitrant characteristics especially under the anaerobic condition. In addition, introducing sulfate and nitrate to the anaerobic condition could enhance the biodegradation of NP and short-chain NPnEOs by supplying more positive redox potentials.

Microbial community structure in different wastewater treatment processes characterized by single-strand conformation polymorphism (SSCP) technique

In order to investigate microbial community structures in different wastewater treatment processes and understand the relationship between the structures and the status of processes,the microbial community diversity,variety and distribution in five wastewater treatment pro cesses were studied by a culture-independent genetic fingerprinting technique single-strand conformation poly-morphism(SSCP).The five processes included denitrifying and phosphate-removal system(diminished N),Chinese traditional medicine wastewater treatment system(P),beer wastewater treatment system(W),fermentative biohydrogen-producing system(H),and sulfate-reduction system(S).The results indicated that the microbial community profiles in the wastewater bioreactors with the uniform status were very similar.The diversity of microbial populations was correlated with the complexity of organic contaminants in wastewater.Chinese traditional medicine wastewater contained more complex organic components;hence,the population diversity was higher than that of simple nutrient bioreactors fed with molasses wastewater.Compared with the strain bands in a simulated community,the relative proportion of some functional microbial populations in bioreactors was not dom-inant.Fermentative biohydrogen producer Ethanoligenens harbinense in the better condition bioreactor had only a 5% band density,and the Desulfovibrio sp.in the sulfate-reducing bioreactor had less than 1.5%band density.The SSCP profiles could identify the difference in microbial community structures in wastewater treatment processes,monitor some of the functional microbes in these processes,and consequently provide useful guidance for improving their efficiency.

Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process

A fuzzy super-twisting algorithm sliding mode controller is developed for the dissolved oxygen concentration in municipal wastewater nitrification process. First, a fuzzy neural network(FNN) model is designed to approach the oxygen dynamics with unmeasurable disturbances, then the established model consists of the nominal system model and the modelling error. Second,based on the FNN model, a super-twisting sliding mode controller is employed to stabilize the nominal system and to suppress the modelling error. Moreover, the stability of the system is investigated and an adaption law is applied to ensure the robustness of the closed-loop system. Finally, the comparison experiments on benchmark simulation model no. 2(BSM2) of wastewater treatment show the advantages of the proposed method in multiple-units oxygen concentration control.

Selective elimination of chromophoric and fluorescent dissolved organic matter in a full-scale municipal wastewater treatment plant

Effluent organic matter（Ef OM） from municipal wastewater treatment plants potentially has a detrimental effect on both aquatic organisms and humans.This study evaluated the removal and transformation of chromophoric dissolved organic matter（CDOM） and fluorescent dissolved organic matter（FDOM） in a full-scale wastewater treatment plant under different seasons.The results showed that bio-treatment was found to be more efficient in removing bulk DOM（in term of dissolved organic carbon,DOC） than CDOM and FDOM,which was contrary to the disinfection process.CDOM and FDOM were selectively removed at various stages during the treatment.Typically,the low molecular weight fractions of CDOM and protein-like FDOM were more efficiently removed during bio-treatment process,whereas the humic-like FDOM exhibited comparable decreases in both bio-treatment and disinfection processes.Overall,the performance of the WWTP was weak in terms of CDOM and FDOM removal,resulting in enrichment of CDOM and FDOM in effluent.Moreover,the total removal of the bulk DOM（P 〈 0.05） and the protein-like FDOM（P 〈 0.05） displayed a significant seasonal variation,with higher removal efficiencies in summer,whereas removal of CDOM and the humic-like FDOM showed little differences between summer and winter.In all,the results provide useful information for understanding the fate and transformation of DOM,illustrating that sub-fractions of DOM could be selectively removed depending on treatment processes and seasonality.

Bioavailability of wastewater derived dissolved organic nitrogen to green microalgae Selenastrum capricornutum,Chlamydomonas reinhardtii,and Chlorella vulgaris with/without presence of bacteria

Effluent dissolved organic nitrogen（DON） is problematic in nutrient sensitive surface waters and needs to be reduced to meet demanding total dissolved nitrogen discharge limits.Bioavailable DON（ABDON） is a portion of DON utilized by algae or algae ＋ bacteria,while biodegradable DON（BDON） is a portion of DON decomposable by bacteria.ABDON and BDON in a two-stage trickling filter（TF） wastewater treatment plant was evaluated using three different microalgal species,Selenastrum capricornutum,Chlamydomonas reinhardtii and Chlorella vulgaris and mixed cultured bacteria.Results showed that up to80% of DON was bioavailable to algae or algae ＋ bacteria inoculum while up to 60% of DON was biodegradable in all the samples.Results showed that C.reinhardtii and C.vulgaris can be used as a test species the same as S.capricornutum since there were no significant differences among these three algae species based on their ability to remove nitrogen species.

Double-cycle weighted imputation method for wastewater treatment process data with multiple missing patterns

Due to sensor malfunctions and communication faults,multiple missing patterns frequently happen in wastewater treatment process(WWTP).Nevertheless,the existing missing data imputation works cannot stand multiple missing patterns because they have not sufficiently utilized of data information.In this article,a double-cycle weighted imputation(DCWI)method is proposed to deal with multiple missing patterns by maximizing the utilization of the available information in variables and instances.The proposed DCWI is comprised of two components:a double-cycle-based imputation sorting and a weighted K nearest neighbor-based imputation estimator.First,the double-cycle mechanism,associated with missing variable sorting and missing instance sorting,is applied to direct the missing values imputation.Second,the weighted K nearest neighbor-based imputation estimator is used to acquire the global similar instances and capture the volatility in the local region.The estimator preserves the original data characteristics as much as possible and enhances the imputation accuracy.Finally,experimental results on simulated and real WWTP datasets with non-stationarity and nonlinearity demonstrate that the proposed DCWI produces more accurate imputation results than comparison methods under different missing patterns and missing ratios.

Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares

Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system.In this work,a new dynamic multiblock partial least squares(DMBPLS)is pro-posed to extract the time-varying information in a large-scale papermaking wastewater treatment process.By introducing augmented matrices to input and output data,the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection,but enhances the interpretability of model.In addition,the DMBPLS provides a capability of fault location,which has certain guiding significance for fault recovery.In comparison with other mod-els,the DMBPLS has a superior fault detection result.Specifically,the maximum fault detection rate of the DMBPLS is improved by 35.93%and 12.5%for bias and drifting faults,respectively,in comparison with partial least squares(PLS).

Elucidating the removal mechanism of N,N-dimethyldithiocarbamate in an anaerobic-anoxic-oxic activated sludge system

N,N-Dimethyldithiocarbamate （DMDTC） is a typical precursor of N-nitrosodimethylamine （NDMA）. Based on separate hydrolysis, sorption and biodegradation studies of DMDTC, a laboratory-scale anaerobic-anoxic-oxic （AAO） system was established to investigate the removal mechanism of DMDTC in this nutrient removal biological treatment system. DMDTC hydrolyzed easily in water solution under either acidic conditions or strong alkaline conditions, and dimethylamine （DMA） was the main hydrolysate. Under anaerobic, anoxic or oxic conditions, DMDTC was biodegraded and completely mineralized. Furthermore, DMA was the main intermediate in DMDTC biodegradation. In the AAO system, the optimal conditions for both nutrient and DMDTC removal were hydraulic retention time 8 hr, sludge retention time 20 day, mixed-liquor return ratio 3：1 and sludge return ratio 1：1. Under these conditions, the removal efficiency of DMDTC reached 99.5%; the removal efficiencies of chemical organic demand, ammonium nitrogen, total nitrogen and total phosphorus were 90%, 98%, 81% and 93%, respectively. Biodegradation is the dominant mechanism for DMDTC removal in the AAO system, which was elucidated as consisting of two steps： first, DMDTC is transformed to DMA in the anaerobic and anoxic units, and then DMA is mineralized to CO2 and NH3 in the anoxic and oxic units. The mineralization of DMDTC in the biological treatment system can effectively avoid the formation of NDMA during subsequent disinfection processes.

Synthesis,characterization and application of ZnO-Ag as a nanophotocatalyst for organic compounds degradation,mechanism and economic study

The current work deals with ZnO-Ag nanocomposites（in the wide range of x in the Zn1-x O-Ag x chemical composition） synthesized using microwave assisted solution combustion method.The structural, morphological and optical properties of the samples were characterized by XRD（X-ray diffraction）, FTIR（Fourier transform infrared spectrometry）, SEM（scanning electron microscopy technique）, EDX（energy dispersive X-ray spectrum）, ICP（inductively coupled plasma technique）, TEM（transmission electron microscopy）, BET（Brunauer–Emmett–Teller method）, UV–Vis（ultraviolet–visible spectrophotometer） and photoluminescence spectrophotometer. The photocatalytic activity of the ZnO-Ag was investigated by photo-degradation of Acid Blue 113（AB 113） under UV illumination in a semi-batch reactor. This experiment showed that ZnO-Ag has much more excellent photocatalytic properties than ZnO synthesized by the same method. The enhanced photocatalytic activity was due to the decrease in recombination of photogenerated electron-holes. The results showed the improvement of ZnO photocatalytic activity and there is an optimum amount of Ag（3.5 mol%） that needs to be doped with ZnO.The effect of operating parameters such as p H, catalyst dose and dye concentration were investigated. The reaction byproducts were identified by LC/MS（liquid chromatography/mass spectrometry） analysis and a pathway was proposed as well. Kinetic studies indicated that the decolorization process follows the first order kinetics. Also, the degradation percentage of AB113 was determined using a total organic carbon（TOC） analyzer. Additionally, cost analysis of the process, the mechanism and the role of Ag were discussed.

Removal of C.I.Reactive Red 2 by low pressure UV/chlorine advanced oxidation

Azo dyes are commonly found as pollutants in wastewater from the textile industry,and can cause environmental problems because of their color and toxicity.The removal of a typical azo dye named C.I.Reactive Red 2（RR2） during low pressure ultraviolet（UV）/chlorine oxidation was investigated in this study.UV irradiation at 254 nm and addition of free chlorine provided much higher removal rates of RR2 and color than UV irradiation or chlorination alone.Increasing the free chlorine dose enhanced the removal efficiency of RR2 and color by UV/chlorine oxidation.Experiments performed with nitrobenzene（NB）or benzoic acid（BA） as scavengers showed that radicals（especially OH） formed during UV/chlorine oxidation are important in the RR2 removal.Addition of HCO_3^- and Cl^- to the RR2 solution did not inhibit the removal of RR2 during UV/chlorine oxidation.

Fe203-CeO2-Bi203/y-AI203 catalyst in the catalytic wet air oxidation （CWAO） of cationic red GTL under mild reaction conditions

The Fe203-CeO2-Bi203/-A1203 catalyst, a novel environmental-friendly material, was used to investigate the catalytic wet air oxidation （CWAO） of cationic red GTL under mild operating conditions in a batch reactor. The catalyst was prepared by wet impregnation, and characterized by special surface area （BET measurement）, X-ray diffraction （XRD） and X-ray photoelectron spectroscopy （XPS）. The Fe203-CeO2-]]i203/qt-A1203 catalyst exhibited good catalytic activity and stability in the CWAO under atmosphere pressure. The effect of the reaction conditions （catalyst loading, degradation temperature, solution concentration and initial solution pH value） was studied. The result showed that the decolorization efficiency of cationic red GTL was improved with increasing the initial solution pH value and the degradation temperature. The apparent activation energy for the reaction was 79 kJ. mo1-1. Hydroperoxy radicals （HO2.） and superoxide radicals （O2-） appeared as the main reactive species upon the CWAO of cationic red GTL.