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.

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.

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.

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).

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.

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.

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.

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.

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.

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.