Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Raji, Mahdieh; Tahroudi, Mohammad Nazeri; Ye, Fei; Dutta, Joydeep

doi:10.1016/j.jenvman.2022.114518

Cited by 28 publications

(7 citation statements)

References 39 publications

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“…It implies that the models' algorithms efficiently learn the non-linear relationship between the various input variables and the biohydrogen produced from the wastewaters. The performances of the BNN, RQGPR, SEGPR, and EGPR are comparable with other machine learning algorithms such as random forest, Adaptive neuro-fuzzy inference system (ANFIS) (Hosseinzadeh et al, 2022), Backpropagation neural network (BPNN) (Sridevi, Sivaraman and Mullai, 2014), multilayer perceptron neural network (MLPNN) (Yogeswari, Dharmalingam and Mullai, 2019) and SVM (Raji et al, 2022). The modeling of biohydrogen production from industrial wastewaters, distillery wastewater, confectionery wastewater, and fermentative medium results in an accurate prediction with high R 2 and low RMSE.…”

Section: Comparison Of the Best Models With Literature And Practical ...mentioning

confidence: 83%

Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater

Hossain

Ali²,

Cheng³

et al. 2022

Front. Energy Res.

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Significant volumes of wastewater are routinely generated during agro-industry processing, amounting to millions of tonnes annually. In line with the circular economy concept, there could be a possibility of simultaneously treating the wastewater and recovering bio-energy resources such as bio-hydrogen. This study aimed to model the effect of different process parameters that could influence wastewater treatment and bio-energy recovery from agro-industrial wastewaters. Three agro-industrial wastewaters from dairy, chicken processing, and palm oil mills were investigated. Eight data-driven machine learning algorithms namely linear support vector machine (LSVM), quadratic support vector machine (QSVM), cubic support vector machine (CSVM), fine Gaussian support vector machine (FGSVM), binary neural network (BNN), rotation quadratic Gaussian process regression (RQGPR), exponential quadratic Gaussian process regression (EQGPR) and exponential Gaussian process regression (EGPR) were employed for the modeling process. The datasets obtained from the three agro-industrial processes were employed to train and test the models. The LSVM, QSVM, and CSVM did not show an impressive performance as indicated by the coefficient of determination (R2) < 0.7 for the prediction of hydrogen produced from wastewaters using the three agro-industrial processes. The LSVM, QSVM, and CSVM models were also characterized by high prediction errors. Superior performance was displayed by FGSVM, BNN, RQGPR, EQGPR, and EQGPR models as indicated by the high R2 > 0.9, an indication of better predictability with minimized prediction errors as indicated by the low root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).

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Section: Comparison Of the Best Models With Literature And Practical ...mentioning

confidence: 83%

Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater

Hossain

Ali²,

Cheng³

et al. 2022

Front. Energy Res.

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“…A NN is one of the methods for machine learning which can be used for modeling or predicting linear and non‐linear parameters 44–46 . Noise is more tolerated in this method 47…”

Section: Methodsmentioning

confidence: 99%

“…A NN is one of the methods for machine learning which can be used for modeling or predicting linear and non-linear parameters. [44][45][46] Noise is more tolerated in this method. 47 NNs discover the relation between empirical data and their hidden rule by processing them and transferring their learning to the network.…”

Section: Modeling Artificial Neural Networkmentioning

confidence: 97%

Optimization of an artificial neural network topology using response surface methodology for microbial fuel cell power prediction

Malekmohammadi

Mirbagheri

2022

Biotechnology Progress

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Microbial fuel cells (MFCs) are among the newest bioelectrical devices that have attracted significant attention because they convert biodegradable organic matter to electricity. MFC design can be improved by understanding and predicting the performance of MFC under different conditions and substrate concentrations. However, few mathematical models have been investigated due to problems caused by the high sensitivity of MFC systems. In this research, a multilayer neural network (NN) was used to predict the generated power of a cell with three inputs (concentration, time, and resistance). Response surface methodology with factors including the number of first layer neurons, number of second layer neurons, training epochs, validation check, and training percentage was used to obtain the optimum structure of the network, and mean squared error (MSE). NN had the minimum MSE when the Number of neurons in the first and second hidden layers, the training epochs, validation check, training percentage were 28, 20, 1000, 100, and 70, respectively. This builtnetwork had an excellent ability to predict, and R 2 was 98%. According to the results, increasing COD concentration increases generated power and system utilization time. In addition, reducing the external resistance up to 100 Ω can lead to more power obtained.

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“…The Bayesian information criterion (BIC), root mean square error (RMSE), Akaike information criterion (AIC) and log likelihood commonly applied to select the best copula (Nash and Sutcliff, 1970;Harville, 1970;Zhang and Singh, 2006, Maa and San, 2011, Khozeymehnezhad and Nazeri-Tahroudi, 2020, Raji et al, 2022Nazeri Tahroudi et al, 2021).…”

Section: Evaluation Criteriamentioning

confidence: 99%

Application of vine copulas to estimate dew point temperature

Ramezani

Tahroudi

Sedighi

2023

Atm.

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In this study, using simulations based on vine copulas such as C-, D- and R-vine copulas, the accuracy of the copula-based model in simulation of the dew point temperature in various climates of Iran was investigated. By examining the various vine copulas and their tree sequences, the selection of the best copula and best tree sequence based on AIC, BIC and Loglike criteria was done. The results showed that among the C-, D- and R-vine copulas, C- and D-vine copulas fit well on the minimum and maximum air temperatures and dew point temperature. The simulation results were analyzed using RMSE, NSE and violin plot. The results showed that the copula-based model has a high accuracy at all stations. The min (max) RMSE is related to Kerman (Ahvaz) station with RMSE= 0.396 oC (0.617 oC). Also, the min (max) NSE is related to Ahvaz (Urmia) station with NSE= 0.925 (0.955). Also, according to the violin plot, it is possible to see the acceptable certainty of the copula-based model. Due to the diversity of the tree sequences of vine copulas and the use of the rotational states of the internal vine copulas, as well as the possibility of interfering with the effective parameters in high dimensions, the simulation results are reliable and have no restrictions. This model can be used as the best model to estimate dew point temperature due to the full coverage of the range of changes in data.

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Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Cited by 28 publications

References 39 publications

Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater

Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater

Optimization of an artificial neural network topology using response surface methodology for microbial fuel cell power prediction

Application of vine copulas to estimate dew point temperature

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