New deterministic tools to systematically investigate fouling occurrence in membrane bioreactors

Hamedi, Hamideh; Ehteshami, Majid; Mirbagheri, Seyed Ahmad; Zendehboudi, Sohrab

doi:10.1016/j.cherd.2019.02.003

Cited by 42 publications

(12 citation statements)

References 112 publications

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“…To provide a better judgement on the suitability of the developed models, other statistical parameters are also presented as follows [ 31,47–51 ] :…”

Section: Modelling Proceduresmentioning

confidence: 99%

“…We employ all these useful techniques to ensure that the model developed in our study does not experience the overfitting phenomenon. [ 31,48 ]…”

Section: Modelling Proceduresmentioning

confidence: 99%

“…y i,exp and y i,pre symbolize the mean value of experimental and predicted outputs, respectively. [44] To provide a better judgement on the suitability of the developed models, other statistical parameters are also presented as follows [31,[47][48][49][50][51] :…”

Section: Ann-bp Modelmentioning

confidence: 99%

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Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

Nejad

Amin

Mohsenipour³

et al. 2022

Can J Chem Eng

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The natural gases produced from underground reserves may contain sour gases such as H 2 S and CO 2 . If the amount of these gases is greater than the standard level, it will be harmful to humans and the environment. Hence, it appears important to accurately determine the concentration of H 2 S and CO 2 in various equipment/units of a natural gas sweetening plant. In this study, a new connectionist approach is introduced to obtain the concentration of outlet acid gases of the sweetening tower of the South Pars gas refinery (in Iran), by utilizing a conventional connectionist tool optimized by an imperialist competitive algorithm (ICA). This ICA strategy is applied to optimize the weights, biases, and number of neurons of the artificial neural network (ANN) model.The input parameters in this modelling strategy include time, the amount of H 2 S in the output amine, the flow rate of input sour gas, the temperature of sea water for cooling, the mass flow rate of low-pressure steam to the amineamine heat exchanger, and the flow rate of input amine. The performance of the hybrid deterministic tool, ANN-ICA, is compared with the ANN-back propagation (BP) method. The coefficient of determination and mean squared error to forecast the concentration of output H 2 S are 0.8931 and 0.0125 for the ANN-BP algorithm and 0.9057 and 0.0104 for the ANN-ICA algorithm, respectively. These statistical values are 0.8428 and 0.0001 for the ANN-BP model and 0.9307 and 0.000 05 for the ANN-ICA model, respectively, while predicting output CO 2 concentration. The results confirm that the ANN-ICA is a more reliable approach, compared to ANN-BP, to forecast the concentration of acidic gases leaving the absorption tower.

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“…To provide a better judgement on the suitability of the developed models, other statistical parameters are also presented as follows [ 31,47–51 ] :…”

Section: Modelling Proceduresmentioning

confidence: 99%

“…We employ all these useful techniques to ensure that the model developed in our study does not experience the overfitting phenomenon. [ 31,48 ]…”

Section: Modelling Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

Nejad

Amin

Mohsenipour³

et al. 2022

Can J Chem Eng

Self Cite

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“…In this study, it mainly considers three influential factors of membrane fouling, including the property of membrane, operation condition and the characteristic of filtrate. Recently, researchers found that the SVM technique outperforms the other models in estimating the fouling resistance in MBR processes [34]. Therefore, the application analysis of SVM in membrane fouling is presented from this perspective.…”

Section: Advances In Modeling Of Membrane Fouling: Traditional Versus Nontraditional Approachesmentioning

confidence: 99%

The Limitations in Current Studies of Organic Fouling and Future Prospects

Meng

Liu

2021

Membranes

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Microfiltration and ultrafiltration for water/wastewater treatment have gained global attention due to their high separation efficiency, while membrane fouling still remains one of their bottlenecks. In such a situation, many researchers attempt to obtain a deep understanding of fouling mechanisms and to develop effective fouling controls. Therefore, this article intends to trigger discussions on the appropriate choice of foulant surrogates and the application of mathematic models to analyze fouling mechanisms in these filtration processes. It has been found that the commonly used foulant surrogate (sodium alginate) cannot ideally represent the organic foulants in practical feed water to explore the fouling mechanisms. More surrogate foulants or extracellular polymeric substance (EPS) extracted from practical source water may be more suitable for use in the studies of membrane fouling problems. On the other hand, the support vector machine (SVM) which focuses on the general trends of filtration data may work as a more powerful simulation tool than traditional empirical models to predict complex filtration behaviors. Careful selection of foulant surrogate substances and the application of accurate mathematical modeling for fouling mechanisms would provide deep insights into the fouling problems.

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“…Smart computational tools have been extensively utilized for the prediction of various properties and parameters in health, safety, and chemical and petroleum engineering, including reservoir fluid and rock properties, process optimization, and performance assessment of EOR techniques [27][28][29][30][31][32]. For instance, machine learning and artificial intelligence have been employed to investigate/forecast the unloading gradient pressure in continuous gas-lift systems [33], air specific heat ratios [34], CO 2 absorption in piperazine [35], CO 2 conversion to urea [36], permeate flux during the filtration [37], CO 2 storage efficiency [38], fouling occurrence in membrane bioreactors [39], and the recovery performance of CO 2 -WAG injection processes [40,41]. In recent years, various optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) have been widely used as reliable approaches to optimize different upstream and downstream processes in the oil and gas industry [42].…”

Section: Introductionmentioning

confidence: 99%

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

2021

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Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R2 = 92.85% and MSE = 1.38 × 10−3 and R2 = 91.93% and MSE = 4.30 × 10−3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.

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New deterministic tools to systematically investigate fouling occurrence in membrane bioreactors

Cited by 42 publications

References 112 publications

Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

The Limitations in Current Studies of Organic Fouling and Future Prospects

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

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