“…When the flow rate is low enough, the functional groups on the resin have adequate time to engage with the FA. The output concentration quickly reaches the initial value when the system flow rate increases, resulting in an early break-through 26 , 49 . Figure 3 Effects of flow rate ( a ), bed height ( b ), and input concentration ( c ) on BTC s of FA adsorption.…”
Section: Resultsmentioning
confidence: 99%
“…Increasing the bed height increases the time required to reach break-through and exhaustion 51 . Therefore, more extended break-through and exhaustion times were seen for the column with the deeper bed height because more DAX-8 adsorbent could be contained within the column, providing more functional groups for binding with FA 26 . The height of the adsorbent bed has a significant impact on the capture of FA during dynamic adsorption.…”
Section: Resultsmentioning
confidence: 99%
“…The fixed-bed column model might predict BTC s accurately in several different situations. These predictions allow us to identify operational conditions without conducting additional full-scale tests 26 . In this study, the BTC s were fitted using the Thomas, Yoon-Nelson, and BDST models.…”
Section: Methodsmentioning
confidence: 99%
“…In this work, the ANN approach was utilized to assess the extraction performance of FA. The ANN model comprises different layers, including the input layer, hidden neurons, the output layer, the connection weight and biases, the activation function, and the summation nod 26 , 35 . The R2020a version of MATLAB software is used for implementing the ANN model.…”
Section: Methodsmentioning
confidence: 99%
“…In addition to the traditional models mentioned, the artificial neural network (ANN) model has been used to analyze the BTC s . ANN, a computer intelligence model influenced by biological and neurological processes, is increasingly popular for managing nonlinear processes 26 . ANN is widely recognized as a robust statistical tool due to a multitude of advantages.…”
Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.
“…When the flow rate is low enough, the functional groups on the resin have adequate time to engage with the FA. The output concentration quickly reaches the initial value when the system flow rate increases, resulting in an early break-through 26 , 49 . Figure 3 Effects of flow rate ( a ), bed height ( b ), and input concentration ( c ) on BTC s of FA adsorption.…”
Section: Resultsmentioning
confidence: 99%
“…Increasing the bed height increases the time required to reach break-through and exhaustion 51 . Therefore, more extended break-through and exhaustion times were seen for the column with the deeper bed height because more DAX-8 adsorbent could be contained within the column, providing more functional groups for binding with FA 26 . The height of the adsorbent bed has a significant impact on the capture of FA during dynamic adsorption.…”
Section: Resultsmentioning
confidence: 99%
“…The fixed-bed column model might predict BTC s accurately in several different situations. These predictions allow us to identify operational conditions without conducting additional full-scale tests 26 . In this study, the BTC s were fitted using the Thomas, Yoon-Nelson, and BDST models.…”
Section: Methodsmentioning
confidence: 99%
“…In this work, the ANN approach was utilized to assess the extraction performance of FA. The ANN model comprises different layers, including the input layer, hidden neurons, the output layer, the connection weight and biases, the activation function, and the summation nod 26 , 35 . The R2020a version of MATLAB software is used for implementing the ANN model.…”
Section: Methodsmentioning
confidence: 99%
“…In addition to the traditional models mentioned, the artificial neural network (ANN) model has been used to analyze the BTC s . ANN, a computer intelligence model influenced by biological and neurological processes, is increasingly popular for managing nonlinear processes 26 . ANN is widely recognized as a robust statistical tool due to a multitude of advantages.…”
Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.
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