In some systems, tanks contain a section known as the bio-selector, which consists of a series of walls or baffles which direct the flow either from side to side of the tank or under and over consecutive baffles. Impact of uneven flow wastewater distribution on the technological efficiency of a sequencing batch reactor. As a result of the non-synchronous nature of the changes in these two parameters, predicting COD using ammonia as a variable may introduce uncertainty into the analysis. Junfei, Q., Gaitang, H., Honggui, H. & Wei, C. Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network. Soft sensor has proven to be a valuable tool for process optimization and control63,64,65,66. The following are detailed discussions: Variables such as conductivity, MLSS, DO and ammonia can also be used as premises to predict COD. (2) For each training set, k attributes are randomly selected from the attribute set (m × attribute), \(k = \log 2m\), and then cart trees are established:\(f_{1} (x),f_{2} (x), \ldots ,f_{r} (x)\). Process Environ. 58(2), 345 (2008). In addition, an empirical judgment system can be established, such as the sewage treatment time generally being within a certain range, if predicted results exceeds this range, the output results of the proposed methodology are deemed to require modification. These decision trees are then combined to predict the results, with the final prediction being the average of the predictions generated by all the trees29. @article{Zhang2023EffectOC, title={Effect of Cd(II) shock loading on performance, microbial enzymatic activity and microbial community in a sequencing batch reactor. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Control 56(1–2), 295–303 (2023). A sequencing batch reactor (SBR) treats wastewater in optimised batches using tried and tested biological processes that deliver exceptional reliability and efficiency. Aerobic biomass converts organics in the wastewater into carbon dioxide and new biomass. In order to evaluate the performance of the COD concentration prediction model, mean square error (MSE) and determination coefficient (r2) are selected as evaluation indexes. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. Furthermore, the accuracy of MLSS sensor is easily affected by the color of wastewater, this will obviously increase the uncertainty of the data measured by the sensors; (3) The SBR works according to aeration-agitation periodicity, DO presents increase–decrease periodicity change, which obviously has no correlation with COD value change trend; (4) During the sewage treatment process, the ammonia concentration in the solution generally exhibits a gradual decrease, similar to the trend observed in COD. It uses proven biological processes to remove contaminants. Atmosphere 11(3), 1 (2020). [4] In this case, the reactors are purged of oxygen by flushing with inert gas and there is no aeration. Prog. Reduce organic matter found in wastewater with Evoqua's sequencing batch reactors (SBR). Figure 10 shows the comparison of predicted and measured COD concentrations on the test set. Artificial neural network (ANN) is a mathematical model that simulates the behavior of animal neural networks, and performs distributed and parallel information processing. Photogram. Dimitriadis, S. I. Basing on the fact, accuracy requirements are different at each stage in a controlled scenario: in the medium and end stage, especially when approaching the stage of effluent standard compliance, greater emphasis is placed on precision and accuracy. Modeling the response of negative air ions to environmental factors using multiple linear regression and random forest. Robust soft sensor systems for industry: Evaluated through real-time case study. So the pH values were observed to decrease or increase according to different organic matter and was considered useful in identifying the residual quantity of COD. Environ. CAS  The sequencing batch reactor (SBR) is perhaps one of the most promising of the activated sludge process modifications today for the removal of both organic matter and nutrients. Juni 2022 um 14:18, Ausführliche Beschreibung von SBR in Form einer Diplomarbeit zu diesem Thema, Funktionsübersicht SBR-Kläranlage incl. Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor, \(f_{1} (x),f_{2} (x), \ldots ,f_{r} (x)\), \(f(x) = \frac{1}{r}\sum\nolimits_{i = 1}^{r} {f_{i} (x)}\), $$ MSE = \frac{1}{N}\sum\limits_{i = 1}^{N} {(y_{i} - \hat{y}_{i} )^{2} } $$, $$ R^{2} = 1 - \frac{{\sum\limits_{i = 1}^{N} {(y_{i} - \hat{y}_{i} )^{2} } }}{{\sum\limits_{i = 1}^{N} {(y_{i} - \overline{y})^{2} } }} $$, $$ \begin{gathered} {\text{NH}}_{{4}}^{ + } + {\text{2O}}_{{2}} = {\text{NO}}_{{2}}^{ - } + {\text{H}}_{{2}} {\text{O}} + {\text{2H}}^{ + } ({\text{pH decreases period}}) \hfill \\ {\text{NO}}_{{3}}^{ - } + {\text{5H}} = {1}/{\text{2N}}_{{2}} + {\text{2H}}_{{2}} {\text{O}} + {\text{OH}}^{ - } ({\text{pH increases period}}) \hfill \\ {\text{NO}}_{{2}}^{ - } + {\text{3H}} = {1}/{\text{2N}}_{{2}} + {\text{H}}_{{2}} {\text{O}} + {\text{OH}}^{ - } ({\text{pH increases period}}) \hfill \\ \end{gathered} $$, https://doi.org/10.1038/s41598-023-36333-8. Artificial intelligence, including machine learning, has been applied to sewage treatment processes to effectively solve non-linear problems. [2]: 8–10. 16(6), 1 (2018). Environ. The resulting model can then be used for real-time prediction and control. Diese durchlaufen jeweils einen Zyklus, der sich von Kläranlage zu Kläranlage unterscheiden kann. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Mateo, P. V., Mesa, F. J. M., Villanueva, B. J. & Qianglin, L. Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor. The installation consists of one or more tanks that can be operated as plug flow or completely mixed reactors. ANN has become widely used in predicting sewage discharge, as it can adjust the interconnections among a large number of internal nodes to process complex information within the system9,10,11,12,13. Harrison, J. W., Lucius, M. A., Farrell, J. L., Eichler, L. W. & Relyea, R. A. Res. Sci. A random forest algorithm applied to condition-based wastewater deterioration modeling and forecasting. CAS  These fundamental topics in reactor processes are well known for the ideal batch reactor, the con-tinuous stirred tank reactor, and the plug flow reactor. Therefore, Cyclor is a relevant response to land and environmental (need to cover over structures) restrictions … thus positioning itself between classic activated sludge and biofilters; controlled entirely automatically with the possibility of easy timer adjustment. Clean Technol. (3) The final prediction value of random forest is determined by the average method:\(f(x) = \frac{1}{r}\sum\nolimits_{i = 1}^{r} {f_{i} (x)}\). As found, during the early stage of the cycle, temperature increased harder than the later, it is probably because the pollutants concentration differs: higher level pollutants concentration results in more activity of microorganisms which is the main reason for temperature change. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. Sharghi, E., Nourani, V., Ashrafi, A. The kinetics of these reactions may be given by one of the . The random forest regression (RFR) is a critical application of the random forest (RF) algorithm, which is a statistical learning theory developed by Breiman28. MathSciNet  The resulting normalized score of variable importance ordering diagram shows the 12 factors affecting COD concentration (Fig. 4). Chen, D. & Li, H. Enhanced simultaneous partial nitrification and denitrification performance of aerobic granular sludge via tapered aeration in sequencing batch reactor for treating low strength and low COD/TN ratio municipal wastewater. Compared with conventional activated sludge, the Cyclor has the following main advantages: The drawbacks, as in the case of all SBR units: SUEZ's degremont® water handbook offers to water treatment professionals, fundamental concepts of water treatment processes and technologies as well as degremont® solutions applied to treatment line and adapted to each use of water. elimination of the secondary settling tank and of sludge recirculation (in principle); these systems tolerate flow rate and pollution loading variations; very good clarification conditions; in particular, excellent control of sludge anoxic and even anaerobic timing during clarification, resulting in excellent sludge indices and low suspended solids contents; simple and compact construction, creating noticeable savings in terms of civil engineering works. Sci. Type of activated sludge process for the treatment of wastewater. Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city. Sci. Data from 40 treatment cycles were collected. Due to the non-linearity and uncertainty of the variation of pH value with time in SBR process, predict results are unstable because of different algorithm and over-fitting by ANN method. Scientific Reports (Sci Rep) a weir with a scum baffle positioned above the floater; a rigid, hinged pipe used to remove treated water (gravity flow). Change 22(1), 1 (2022). 3(16), 1 (2019). An anoxic SBR can be used for anaerobic processes, such as the removal of ammonia via Anammox, or the study of slow-growing microorganisms. Adv. Das Zeitintervall vom Beginn des Füllvorgangs bis zum Ende des Klarwasserabzugs und einer eventuellen Ruhephase wird als Zyklus bezeichnet. Sensors 21(10), 3426 (2021). Dieses Verfahren wird oftmals bei industriellen Kläranlagen verwendet. By submitting a comment you agree to abide by our Terms and Community Guidelines. Tanwar, P., Nandy, T., Ukey, P. & Manekar, P. Correlating on-line monitoring parameters, pH, DO and ORP with nutrient removal in an intermittent cyclic process bioreactor system. The source of domestic sewage is from the Shuyuan Community in Pidu District, Chengdu . In the context of wastewater treatment plants, RFR soft sensor model can be used to predict water quality (COD) through simpler diameters, in this way complex and expensive sensors will be replaced. The effectiveness of the RFR soft sensor model was assessed across 5 criteria. 19, 1 (2020). This stage is also called the anoxic stage. & Chen, W. Cleaner production assessment for wastewater treatment plants based on backpropagation artificial neural network. & Anders, K. Semi-automated classification of exposed bedrock cover in British Columbia’s Southern Mountains using a Random Forest approach. In the practical application of the proposed methodology, it is possible that the COD value meet the standard while other indicators such as ammonia or phosphorus do not. Biores. Sci Rep 13, 9149 (2023). Three types of standard cycles can be used: short, long and very long. OPTO conference, 2020. However, training a neural network can be computationally expensive and require a large amount of data. Once in a real engineering case, due to its complexity, the model's performance will not be so outstanding18,19. Proc. The RFR technique involves using Bootstrap resampling to extract multiple samples from the original data and construct decision trees for each Bootstrap sample. 45(3), 981–994 (2022). The COD degradation trend, as well as the deviation between predicted and measured values, can be observed from the variations in the curves depicted in Fig. Article  Inf. & Liu, R. Establishing and verification a temperature model for the process of water treatment. Kronholm, S. C., Capel, P. D. & Terziotti, S. statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams. This has effectively improved sewage treatment efficiency and quality while reducing treatment costs. Figure 1 shows the diagram of RFR. 67, 27–38 (1996). J. Environ. Based on the operation process data, RFR soft sensor model is used to establish the COD prediction model of SBR effluent, which realizes the rapid prediction of effluent quality and provides the basis for the efficient and stable operation of the wastewater treatment process as shown in Fig. [2]: 3–8, 19. 30(8), 1100–1112 (2007). In addition to using artificial neural network (ANN) methods, other techniques such as linear regression (LR), support vector regression (SVR), and neuro-fuzzy network methods have also been used in pollutant removal technology to predict changes in pollutant concentrations or other process parameters14,15,16,17,18,19. The cycle, which lasts for 480 min, includes the following stages: 30 min fill in and aeration stage, 330 min oxidation and agitation (alternating aeration and agitation, with aeration lasting for 10 min and agitation 20 min) stage, 60 min settlement stage and 60 min discharge stage. Environ. However, constructing accurate simulation models for rural domestic wastewater treatment can be challenging due to the non-linearity and hysteresis characteristics exhibited by the SBR process7,8. Revealing factors influencing spatial variation in the quantity and quality of rural domestic sewage discharge across China. & Hu, H. Understanding the distribution and drivers of PM2.5 concentrations in the Yangtze River Delta from 2015 to 2020 using random forest regression. feed (raw or clarified water) and reaction (aeration/mixing in the tank); settle (separating out the suspended solids); decant (drawing off the treated water) and then idle (extracting excess sludge). Water 14(3), 368 (2022). Machine learning encompasses a range of methods, such as neural networks and support vector regression, which can be used to analyze and model the complex data generated during sewage treatment. Different environments are created in the SBR by controlling process equipment such as aerators, mixers, pumps, and decanters during a specific timed cycle. Increase conductivity or other readily available parameters as input variables to improve prediction accuracy. Appl. Eine separate Nachklärung ist beim SBR-Verfahren nicht notwendig. Eng. Methodology of discretization was effectively used for the model development and calculations. The RFR model solves practical problems such as small samples, high dimensions and multi-classification, and can handle both discrete data and continuous data70. RFR is an ensemble learning method that combines multiple decision tree models to create a more robust and accurate predictor. Neural networks are powerful models that can learn complex patterns in data. Emon, M. & Mohaiminul, I. Soft sensor is a commonly used method in process monitoring and control, which estimates the process variable of interest based on the measurements of other variables that are easy to acquire. This process encourages the conversion of nitrogen from its reduced ammonia form to oxidized nitrite and nitrate forms, a process known as nitrification. h–1. In contrast, the random forest regression (RFR) model is another machine learning algorithm used for predicting sewage water treatment effects. A simple and practical review of over-fitting in neural network learning. Mater. Energy 34(5), 1322–1331 (2015). Description Activated sludge process where various treatment events occur in a single vessel. Artificial intelligence and multivariate statistics for comprehensive assessment of filamentous bacteria in wastewater treatment plants experiencing sludge bulking. Following an evaluation of equipment provided by various SBR manufacturers, the project team elected to design the SBR using equipment provided by Jet Tech, a Siemens Water Technologies' company. Admin. Model. Chen, P., Zhao, W., Chen, D., Huang, Z. reduce the risk of bulking by preventing the development of filament type bacteria and by improving sludge settleability; encourage biological phosphorus removal and denitrification reactions. wrote the main manuscript text and all figures and tables. Res. The true-batch design will also allow for a perfect quiescent settling step, promoting ideal sludge settling and compaction before the decant of the treated effluent. A., Mahmoud, N., Ismail, R. & Faizal, B. J. Comput. 89, 401–410 (2014). Some studies have shown that the metabolic activity of microbial communities in wastewater treatment bioreactors can cause an increase in water temperature78,79. Intell. The essence of the RFR algorithm is multi-decision tree model, which makes prediction by combining multiple decision trees. Environ. Pearson correlation coefficient is a statistical measure used to determine the strength and direction of the linear relationship between two variables. 194(4), 284 (2022). The proposed methodology replaced fixed-time control, which was uncontrolled. Neustadt in Niederösterreich: Auch Kleinkläranlagen werden seit dem Jahr 2000 mit der SBR-Technik gebaut. This pump was called pump A which made 0.1856m3 sewage fed to the SBR every cycle. Proc. Atmosphere 12(5), 552 (2021). The characteristic of the RFR model were set as Table 3. for small size plants, the Bio S (see compact units. However, this approach is limited to artificial intelligence methods only. Equali zation, aeration, and clarification can all be achieved using a single batch reactor. Data Sci. Google Scholar. During prediction, the RFR model aggregates the output of individual decision trees to produce a final prediction. J. Integrated soft sensor of COD for WWTP based on ASP model and RBF neural network. Hao, X., Sun, S., Li, J. Evoqua offers aerobic treatment systems for both municipal and industrial facilities looking to treat raw wastewater and/or polish anaerobically . Water Res. Der SBR-Zyklus ist durch eine aufeinanderfolgende zeitliche Prozessphasenfolge gekennzeichnet. However, there are comparatively fewer studies on the prediction and control of rural domestic sewage treatment effects using the RFR model. Shi, G. Y. et al. The decanting stage most commonly involves the slow lowering of a scoop or “trough” into the basin. While there are several configurations of SBRs, the basic process is similar. The source of domestic sewage is from the Shuyuan Community in Pidu District, Chengdu, Sichuan, China (longitude: 103.88, latitude: 30.82). Int. Cookies ensure that our website works properly. Environ. Since biofilm system exhibits a notable potential for the removal of recalcitrant contaminants, a sequencing batch biofilm reactor (SBBR) was employed to treat coagulated recycled paper mill effluent in this study. Verfahren zur biologischen Abwasserreinigung in Kläranlagen, Zuletzt bearbeitet am 12. Environ. Article  63, 8–19 (2016). CAS  The working volume of the reactor was 0.576 m3, respectively (Fig. Die hydraulische Entkopplung des SBR-Verfahrens macht es möglich, Dauer, Häufigkeit und Anordnung der Phasen des Zyklus variabel zu gestalten. Within each 480 min cycle, data collected 0 ~ 30 min and 361 ~ 480 min were excluded to eliminate the effects of filling and settlement periods (as these phases were not part of the biological reaction phases of the treatment cycle). Eng. Chapman and Hall Press:London, UK 1, 10 (2017). Earth Sci. Sequencing batch reactors (SBRs) are devices widely used in wastewater treatment, chemical engineering, and other areas. were constructed and added to the set of independent variables. Environ. C.Q., C.Z. A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden. Prot. 131(1), 86–92 (2005). & Zhang, C. Research progress on integrated treatment technologies of rural domestic sewage: A review. This can be achieved by reducing energy consumption and enhancing the efficiency of chemical and biological processes. Solid Waste Wastewater Manag. The non-linear problems in sewage treatment refer to the complex, diverse, and non-linear relationships that arise from the interactions of various chemical reactions, biological reactions, and physical effects during sewage treatment. Conditions in the tank, especially near the bottom are now more suitable for the anaerobic bacteria to flourish. Res. The SBR reactor (stainless steel, 800 mm × 800 mm × 1200 mm) was designed and manufactured. Table 2 shows related applications of random forest regression. Google Scholar. However, one of the major weaknesses of ANN is overfitting, which can lead to a reduction in the model's generalizability50,51,52. Extended aeration plants are more flexible in flow rate, eliminating restrictions presented by pumps located throughout the SBR systems. PubMed  The proposed methodology aims to achieve improved prediction and effective control of the treatment effect of rural domestic sewage through the development and utilization of RFR soft sensor model. & Mahmoud, N. Treatment of water contaminated with diazinon by electro-Fenton process: Effect of operating parameters, and artificial neural network modeling. The advantages of soft sensor include cost-effectiveness, flexibility and ability to handle complex nonlinear systems. A typical profile for COD saw an increase in concentrations as influent was mixed with the treated sewage water remaining in the reactor from the previous cycle. The CAS is the most common process and consists, in its simplest configuration, of a biological reactor followed by a . DESCRIPTION The sequencing batch reactor (SBR) is a fill-and-draw activated sludge system for wastewatertreatment. Wang, Q. et al. }, author={Hanlin Zhang and Duosen Yan and Ya Qi Zhu and Yun Li and Guodong Zhang and Yan Jiao and Qinghua Chen and Shanshan Li}, journal={Journal of environmental management}, year . 2). The COD sensor method mainly uses optical sensors, which are significantly affected by the chromaticity and turbidity of wastewater. The speaker shares case studies: sequencing batch reactor (SBR) wastewater treatment plants that are operated differently than designed to achieve notable reductions in effluent nitrogen and phosphorus. & Alonso, Á. C. A Random forest model for the prediction of FOG content in inlet wastewater from urban WWTPs. the air system has to be over-designed because of its cyclical operation (limited aeration time); tank capacity has to be over-designed when a high hydraulic peak coefficient applies (over 3); in some cases, downstream storage will be required when tertiary treatment is included. 211, 113054 (2022). Medium and long-term runoff forecasting based on a random forest regression model. The assessment criteria are listed in Table 5. Annu. B. Res. Vitorino, D. et al. Int. & Zhou, Z. J. Chemosphere 290, 133388 (2021). In 12 test cases, percentage of COD removal is about 91. PDH-Pro.com 396 Washington Street, Suite 159, Wellesley, MA 02481 Telephone - (508) 298-4787 www.PDH-Pro.com This document is the course text. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Conversion to SBR will create a longer sludge age, minimizing sludge handling requirements downstream of the SBR. Desalin. Technol. CAS  Clarifiers can be retrofitted in the equalization tanks of the SBR. R2 on the test set is around 0.791, although it is not too high, but the accuracy at the cut-off threshold value of COD is around 91% which is acceptable for the prediction. Moreover, the COD sensor is expensive for dispersed small equipment, making it difficult to popularize. Artificial neural network and techno-economic estimation with algae-based tertiary wastewater treatment. Internet Explorer). et al. The batch kinetics with adjustable process event times result in extra flexibility compared to conventional activated sludge systems. 22(18), 6887–6899 (2022). The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Im Gegensatz zu kontinuierlich durchflossenen Reaktoren wird der SBR im Satzbetrieb befüllt und geleert. The sequencing batch reactor (SBR) is a fill and draw type modified activated sludge process, where four basic steps of filling, aeration, settling and decantation take place sequentially in a batch reactor. Appl. 25% of time or energy was saved. Expert Syst. volume 13, Article number: 9149 (2023) This is because the microorganisms in the reactor produce a large amount of heat through the degradation and metabolism of organic matter, leading to an increase in the temperature inside the reactor. The sequencing batch reactor process ( SBR ) involves a single complete mix type reactor in which aeration takes place followed by clarification, whence the designation "sequential". Env. Chang, C. H. & Hao, O. J. Sequencing batch reactor system for nutrient removal: ORP and pH profiles. Furthermore, it should be noted that while pH is indeed a contributing factor, its significance is not as strong as that of pHapex-nadir. Therefore, in the proposed methodology, the magnitude of the error between predicted and measured values is mainly affected by the processing stage. Volume 317, 1 September 2022, 115305 Review Advancements of sequencing batch reactor for industrial wastewater treatment: Major focus on modifications, critical operational parameters, and future perspectives Adarsh Singh, Ashish Srivastava, Saidulu, Ashok Kumar Gupta Add to Mendeley Designed by field men for field men, this valuable tool is essential for site managers, environmental managers, quality managers, maintenance managers, stakeholders in sustainable development, water agencies, documentation centers in universities, consultants, local authority technical departments, water management companies, etc. Authors Neurocomputing 214, 242–268 (2016). In general, pH decreases as alkalinity is consumed during the nitrification progress. In order to prevent the occurrence of invalid variables, avoid overfitting and improve the training performance of the model, any variable with a normalized Pearson correlation coefficient value, that is regarded as the normalized score of variable importance, less than 0.01 was removed.