D. Aguado

A voltammetric electronic tongue as tool for water quality monitoring in wastewater treatment plants

The use of a voltammetric electronic tongue as tool for the prediction of concentration levels of certain water quality parameters from influent and effluent wastewater from a Submerged Anaerobic Membrane Bioreactor pilot plant applied to domestic wastewater treatment is proposed here. The electronic tongue consists of a set of noble (Au, Pt, Rh, Ir, and Ag) and non-noble (Ni, Co and Cu) electrodes that were housed inside a stainless steel cylinder which was used as the body of the electronic tongue system. As a previous step an electrochemical study of the response of the ions sulphate, orthophosphate, acetate, bicarbonate and ammonium was carried out in water using the electrodes contained in the electronic tongue. The second part of the work was devoted to the application of the electronic tongue to the characterization of the influent and effluent waters from the wastewater treatment plant. Partial Least Squares analysis was used to obtain a correlation between the data from the tongue and the pollution parameters measured in the laboratory such as soluble chemical oxygen demand (CODs), soluble biological oxygen demand (BODs), ammonia (NH(4)-N), orthophosphate (PO(4)-P), Sulphate (SO(4)-S), acetic acid (HAC) and alkalinity (Alk). A total of 28 and 11 samples were used in the training and the validation steps, respectively, for both influent and effluent water samples. The electronic tongue showed relatively good predictive power for the determination of BOD, COD, NH(4)-N, PO(4)-P, SO(4)-S, and Alk.

Multivariate statistical monitoring of continuous wastewater treatment plants

In this paper, different multivariate statistical approaches for analysing wastewater treatment process data are presented and compared. For this purpose, all the methods have been tested using one-year operational data in a simulation model benchmark. The general monitoring strategy adopted includes a screening stage to improve data quality, an adaptive model to detect and diagnose abnormal events, and two complementary tools for helping in the diagnosis of the faults. The first one is based on the development of a local model that captures the most recent process behaviour and the second one on the application of fuzzy c-means clustering to the scores of the monitoring model. The results have shown that simple scaling parameters adaptation is sufficient to obtain a model useful for monitoring the process during the whole period. Monitoring the deviations from the average daily behaviour showed clear detections of the disturbances in the Hotellings T^2-statistic and this feature was useful to determine different operational states (disturbances) in the process by clustering the PCA scores. On the other hand, the proposed procedure for isolation based on a local model improved the diagnosis results in terms of the responsible variables identified and the indication of the beginning of the fault.

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the data set analysed, both techniques yielded similar results regarding the relationships among the variables and the clustering of the observations (i.e., the same groups of observations were identified) and, therefore, identical process interpretation could be made. The cluster analysis allowed relating the observations to process behaviour, clearly distinguishing start-up, desirable and poor process conditions. The results demonstrate that the applied techniques are highly effective to compress multidimensional data sets and to extract relevant information from the process, making the interpretation and diagnosis much easier and evident.

Occurrence of priority pollutants in WWTP effluents and Mediterranean coastal waters of Spain

A comprehensive study aimed at evaluating the occurrence, significance of concentrations and spatial distribution of priority pollutants (PPs) along the Comunidad Valenciana coastal waters (Spain) was carried out in order to fulfil the European Water Framework Directive (WFD). Additionally, PP concentrations were also analysed in the effluent of 28 WWTPs distributed along the studied area. In coastal waters 36 organic pollutants of the 71 analysed, including 26 PPs were detected although many of them with low frequency of occurrence. Only 13 compounds, which belong to four different classes (VOCs, organochlorinated pesticides, phthalates and tributyltin compounds (TBT)) showed a frequency of occurrence above 20% in coastal waters. In the results obtained until now, octylphenol, pentachlorobenzene, DEHP and TBT exceeded the annual average concentration (EQS-AAC), and only TBT surpassed the maximum allowable concentration (EQS-MAC). The most frequent contaminants determined in coastal waters were also present in WWTP effluents.

Relating ions concentration variations to conductivity variations in a sequencing batch reactor operated for enhanced biological phosphorus removal

In this paper a deterministic relationship between ionic conductivity and phosphorus concentration variations in an enhanced biological phosphorus removal (EBPR) process is established. Conductivity shows a strong correlation with phosphorus in both anaerobic and aerobic stages, increasing or decreasing when phosphorus is released or taken up, respectively. Since the end of these processes can be detected by examining the conductivity profile in a cycle, useful information on the EBPR performance and stability is available. This information can be used for adjusting the length of the anaerobic and aerobic stages. Therefore, online process control based on inexpensive and easy to maintain sensors could be applied. Laboratory scale experiments were conducted to study the most significant ions concentration behaviour in a sequencing batch reactor (SBR) operated for EBPR as well as the conductivity and pH profiles. Statistical analysis of the experimental data also showed a strong correlation between metal cations and phosphorus concentrations (0.28molKmolP^-^1 and 0.36molMgmolP^-^1). The model used in the simulation stage (BNRM1) takes account of all the ions that play an important role in the EBPR process as well as the pH value. Model predictions accurately reproduced the experimental data.

A methodology for sequencing batch reactor identification with artificial neural networks: A case study

This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR) identification. The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. The proposed approach makes optimal use of the available data during the training stage and it is aimed at achieving high generalization ability. For this purpose, a wide range of experimental conditions, including different solids retention times and influent characteristics, has been used. The methodology is successfully applied to develop a soft-sensor for monitoring a laboratory-scale SBR operated for enhanced biological phosphorus removal. The main interest is the utilization of the soft-sensor to determine the optimal length of the SBR stages within each cycle according to the actual process requirements. Note that SBRs are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANNs. The ANNs were trained for on-line prediction of phosphorus (P) concentration in the SBR. One ANN uses only inexpensive and reliable on-line measurements as input data and the other one also includes as input the previous P measurement (lag −1), thus considering the quality variable dynamics. The latter ANN can be used to overcome the delay introduced by the measurement procedure of phosphorus concentration. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed, since they accurately reproduced the phosphorus behaviour in the SBR.

Performance evaluation of fault detection methods for wastewater treatment processes

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non‐detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long‐term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods. Biotechnol. Bioeng. 2011;108: 333–344.

Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor.

Ammonia-oxidizing bacteria (AOB) are very sensitive to environmental conditions and wastewater treatment plant operational parameters. One of the most important factors affecting their activity is pH. Its effect is associated with: NH3/NH4(+) and HNO2/NO2(-) chemical equilibriums and biological reaction rates. The aim of this study was to quantify and model the effect of pH and free nitrous acid (FNA) concentration on the activity of AOB present in a lab-scale partial nitritation reactor. For this purpose, two sets of batch experiments were carried out using biomass from this reactor. Fluorescent in situ hybridization analysis showed that Nitrosomona eutropha and Nitrosomona europaea species were dominant in the partial nitritation reactor (>94%). The experimental results showed that FNA inhibits the AOB activity. This inhibition was properly modelled by the non-competitive inhibition function and the half inhibition constant value was determined as 1.32 mg HNO2-N L(-1). The optimal pH for these AOB was found to be in the range 7.4-7.8. The pH inhibitory effect was stronger at high pH values than at low pH values. Therefore, an asymmetric inhibition function was proposed to represent the pH effect on these bacteria. A combination of two sigmoidal functions was able to reproduce the experimental results obtained.

Potential use of the organic fraction of municipal solid waste in anaerobic co-digestion with wastewater in submerged anaerobic membrane technology

Food waste was characterized for its potential use as substrate for anaerobic co-digestion in a submerged anaerobic membrane bioreactor pilot plant that treats urban wastewater (WW). 90% of the particles had sizes under 0.5mm after grinding the food waste in a commercial food waste disposer. COD, nitrogen and phosphorus concentrations were 100, 2 and 20 times higher in food waste than their average concentrations in WW, but the relative flow contribution of both streams made COD the only pollutant that increased significantly when both substrates were mixed. As sulphate concentration in food waste was in the same range as WW, co-digestion of both substrates would increase the COD/SO4-S ratio and favour methanogenic activity in anaerobic treatments. The average methane potential of the food waste was 421±15mLCH4g(-1)VS, achieving 73% anaerobic biodegradability. The anaerobic co-digestion of food waste with WW is expected to increase methane production 2.9-fold. The settleable solids tests and the particle size distribution analyses confirmed that both treatment lines of a conventional WWTP (water and sludge lines) would be clearly impacted by the incorporation of food waste into its influent. Anaerobic processes are therefore preferred over their aerobic counterparts due to their ability to valorise the high COD content to produce biogas (a renewable energy) instead of increasing the energetic costs associated with the aeration process for aerobic COD oxidation.

Short-term effect of ammonia concentration and salinity on activity of ammonia oxidizing bacteria

A continuously aerated SHARON (single reactor high activity ammonia removal over nitrite) system has been operated to achieve partial nitritation. Two sets of batch experiments were carried out to study the effect of ammonia concentration and salinity on the activity of ammonia-oxidizing bacteria (AOB). Activity of AOB raised as free ammonia concentration was increased reaching its maximum value at 4.5 mg NH3-N l(-1). The half saturation constant for free ammonia was determined (K(NH3)=0.32 mg NH3-N l(-1)). Activity decreased at TAN (total ammonium-nitrogen) concentration over 2,000 mg NH4-N l(-1). No free ammonia inhibition was detected. The effect of salinity was studied by adding different concentrations of different salts to the biomass. No significant differences were observed between the experiments carried out with a salt containing or not containing NH4. These results support that AOB are inhibited by salinity, not by free ammonia. A mathematical expression to represent this inhibition is proposed. To compare substrate affinity and salinity inhibitory effect on different AOB populations, similar experiments were carried out with biomass from a biological nutrient removal pilot plant. The AOB activity reached its maximum value at 0.008 mg NH3-N l(-1) and decreased at TAN concentration over 400 mg NH4-N l(-1). These differences can be explained by the different AOB predominating species: Nitrosomonas europaea and N. eutropha in the SHARON biomass and Nitrosomonas oligotropha in the pilot plant.