Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

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.

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.

Effect of Mineral Processing Wastewater on Electrochemistry of Galena

Electrochemical technology was introduced to study the floatability of galena in some wastewater samples from different processes of mineral processing plant in Fankou Lead-Zinc Mine. It is shown that the residual collector molecules in the wastewater from the thickening of lead and zinc concentrates can benefit the formation of lead xanthanate onto the surface of galena, yet, some special chemical components in the wastewater from zinc tailings and effluent may induce some surface reactions on galena, and herewith the direct reuse of this water may bring disadvantageous influence on galena flotation.

Treatment of methanol wastewater with two-stage and two-phase anaerobic process

The two-stage and two-phase anaerobic process (TSTP) composed of hydrolytic acidification reactor,first-order and second-order external circulation anaerobic reactors (EC) was taken to treat methanol wastewater. Test results show that TSTP process is quick start-up in 51 d, and the maximum VFA of hydrolytic acidification reactor effluent reaches 876 mg/L. Under the condition of volume loading of 6.56 kgCOD/m3·d, COD removal rate of the first-order EC reactor is about 85%, and under the condition of volume loading of 1.02 kgCOD/m3·d, COD removal rate of the second-order EC reactor is about 50%. When the inflow COD of TSTP process is between 7000-11000 mg/L, its effluent COD is lower than 600 mg/L. In the biological conversion process of methanol into methane,the production of acetic acids as an intermediate product can be ignored and the direct production of methane from methanol is predominant.

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features.To solve this issue,a multi-timescale feature extraction method based on adaptive entropy is proposed.Firstly,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among them.Secondly,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the timescale.Thirdly,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph.The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

Development of a new cleaner production process for cassava ethanol

A new cleaner production process for cassava ethanol has been developed, in which the thin stillage by-product was treated initially by anaerobic digestion, and the digestate further processed by hydrogen-form cation exchange resin before being recycled as process water to make mash for the next ethanol fermentation batch.Thus wastewater was eliminated and freshwater and energy consumption was significantly reduced. To evaluate the new process, ten consecutive batches of ethanol fermentation and anaerobic digestion at lab scale were carried out. Average ethanol production in the recycling batches was 11.43%(v/v) which was similar to the first batch, where deionized(DI) water was used as process water. The chemical oxygen demand(COD) removal rate reached 98% and the methane yield was 322 ml per gram of COD removed, suggesting an efficient and stable operation of the anaerobic digestion. In conclusion, the application of the new process can contribute to sustainable development of the cassava ethanol industry.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

A data-derived soft-sensor method for monitoring effluent total phosphorus

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

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.

Effect of wastewater treatment processes on the pyrolysis properties of the pyrolysis tars from sewage sludges

The pyrolysis properties of five different pyrolysis tars, which the tars from 1# to 5# are obtained by pyrolyzing the sewage sludges of anaerobic digestion and indigestion from the A2/O wastewater treatment process, those from the activated sludge process and the indigested sludge from the continuous SBR process respectively, were studied by thermal gravimetric analysis at a heating rate of 10 ℃/min in the nitrogen atmosphere. The results show that the pyrolysis processes of the pyrolysis tars of 1#, 2#, 3# and 5# all can be divided into four stages: the stages of light organic compounds releasing, heavy polar organic compounds decomposition, heavy organic compounds decomposition and the residual organic compounds decomposition. However, the process of 4# pyrolysis tar is only divided into three stages: the stages of light organic compounds releasing, decomposition of heavy polar organic compounds and the residual heavy organic compounds respectively. Both the sludge anaerobic digestion and the "anaerobic" process in wastewater treatment processes make the content of light organic compounds in tars decrease, but make that of heavy organic compounds with complex structure increase. Besides, both make the pyrolysis properties of the tars become worse. The pyrolysis reaction mechanisms of the five pyrolysis tars have been studied with Coats-Redfern equation. It shows that there are the same mechanism functions in the first stage for the five tars and in the second and third stage for the tars of 1#, 2#, 3# and 5#, which is different with the function in the second stage for 4# tar. The five tars are easy to volatile.

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.

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.

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.