Optimization of depth of filler media in horizontal flow constructed wetlands for maximizing removal rate coefficients of targeted pollutant(s)

Singh, Saurabh; Soti, Abhishek; Kulshreshtha, Niha Mohan; Kumar, Nikhil; Brighu, Urmila; Gupta, Akhilendra Bhushan; Bezbaruah, Achintya N.

doi:10.1016/j.biortech.2023.128898

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…Low-OLR systems are those receiving BOD loading rates of less than 30 g/m 3 -d . The low-OLR CWs generally receive a good quality secondary-treated wastewater and are designed predominantly for the removal of coliforms and nutrients to conform to stringent standards. , In this paper, the HFCWs receiving a low organic loading were analyzed in detail to identify the possible approaches for optimizing the prediction of k values. The main focus here was nitrogen and phosphorus removal and not organic removal.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Shallower depths (<0.2 m) offer more oxygen penetration but less support for microbial growth, thus affecting the treatment process . Medium depths (0.2–0.5 m) balance oxygen access and the microbial growth space, optimizing aerobic and anaerobic processes . Deeper beds (>0.5 m) enhance anoxic/anaerobic conditions, suited for the removal of specific pollutants. , Controlling the media depth at the design stage can help in the HFCW design for specific treatment needs.…”

Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Further reduction of the SDs could not be achieved. 11 The use of these k values may lead to overdesigned or under-designed systems. 16,18 Machine learning (ML) was used in this study to optimize SDs in selected datasets focusing on optimizing the system for TN, TKN, and TP removal.…”

Section: Introductionmentioning

confidence: 99%

“…Setting the design guidelines for CWs is a challenging task, owing to the complex and nonlinear nature of biological reactions and the inter-reliant physical, chemical, and biological parameters. , The P - k - C * first-order kinetic model is most frequently used for the design of HFCWs. , In addition to the inlet and outlet pollutant concentrations, the area calculation using the P - k - C * approach depends on the areal removal rate coefficient k . , These k values can be calculated from the observed kinetic parameters of field-scale systems as reported in our previous studies that carried out extensive statistical analysis of the large dataset pertaining to the performance of 74 HFCWs . However, the dependencies of the k value on multiple factors like temperature, depth of media, the concentration of pollutants, detention time, and design flow make their design extremely complex. , In our previous study, datasets were classified based on factors like organic loading rate, temperature, and depth, and they significantly reduced the standard deviations (SDs) of k values, decreasing them from 84.74 to 34.85%; 116.48 to 31.42%; and 114.70 to 36.89%, respectively, for the three subcategories (BOD, TN, TP). Further reduction of the SDs could not be achieved .…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Singh,

Soti,

Kulshreshtha

et al. 2024

ACS EST Water

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Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically with the OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied to predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, and the k-values calculated for these wetlands were found to vary markedly (0.059−0.249 with an average of 0.113 ± 0.090 m/day). The classification of k-values based on the OLR, applied loading rate, and media depth leads to the reduction in standard deviations (SDs) from 83.40 to 35.27%. kvalues with the least SDs are needed for optimal design for low-OLR CWs. The SVR, RF, and ANN models were tested, and the best prediction efficiency on testing datasets was achieved through the ANN model with R 2 (k TKN )= 0.768 (RMSE = 0.067) for total Kjeldahl nitrogen (TKN), R 2 (k TN )= 0.835 (RMSE = 0.043) for total nitrogen (TN), and R 2 (k TP ) = 0.723 (RMSE = 0.087) for total phosphorus (TP). The outcome was validated using primary data from HFCWs, which also confirmed the superiority of the ANNbased model, which can be used for design customization of low-OLR HFCWs.

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Section: Results and Discussionmentioning

confidence: 99%