Artificial neural network for prediction of color adsorption from an industrial textile effluent using modified sugarcane bagasse: Characterization, kinetics and isotherm studies

Leon, Vitória Brocardo de; Negreiros, Bruno Azevedo Ferraz de; Brusamarello, Claiton Zanini; Petroli, Gustavo; Domenico, Michele Di; Souza, Fernanda Pasquoto de

doi:10.1016/j.enmm.2020.100387

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…In other words, ANN models is a man-made algorithm via programming languages dealing with a specific task by replicating the brain's function. 7 ML is an artificial intelligence application that allows people predict outcomes using software without rigorous programming. 8 Moreover, an important feature of ML is the ability to learn from and deal with a complex and large, multidimensional dataset.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in the evaluation and prediction models of biochar application: A review

et al. 2023

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This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

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Section: Introductionmentioning

confidence: 99%

“…In other words, ANN models is a man-made algorithm via programming languages dealing with a specific task by replicating the brain's function. 7 …”

Section: Introductionmentioning

confidence: 99%

Machine learning in the evaluation and prediction models of biochar application: A review

et al. 2023

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“…They came from the analogy made between the human brain and computer processing. Basically, ANN are a computational system that replicates the function of the brain to carry out a specific task [60]. Input value(s) (i.e., independent variables of the system under study) are provided to the network and are manipulated via internal mathematical operations to produce an output value(s) (i.e., dependent variables of the system under study) [60].…”

Section: Brief Introduction Of Artificial Neural Networkmentioning

confidence: 99%

“…Basically, ANN are a computational system that replicates the function of the brain to carry out a specific task [60]. Input value(s) (i.e., independent variables of the system under study) are provided to the network and are manipulated via internal mathematical operations to produce an output value(s) (i.e., dependent variables of the system under study) [60]. ANN are considered as black-box models useful when a mathematical relationship between the output and input variables is not available to describe the phenomenon to be analyzed and/or where the traditional models may fail [61, 62].…”

Section: Brief Introduction Of Artificial Neural Networkmentioning

confidence: 99%

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Reynel-Ávila

Aguayo‐Villarreal

Díaz-Muñoz

et al. 2022

Adsorption Science & Technology

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The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

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“…To our knowledge, there is no study executed to predict the releasing of microplastic fibers by utilizing of ANFIS model. However, the different prediction models such as mathematical designs, logical regression models as well as artificial neural network (ANN) have been developed to expect the features of textiles like yarn strength [16], weaving operation [17] predicting bursting strength, modeling of UV protection factor [18], mechanical properties [19], EMI shielding [20], wrinkle recovery [21], textile sensor [22], textile effluent [23], color strength, color fastness, mechanical properties and so forth. The advantage of the ANFIS model is to predict the variations in between the randomly chosen parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System to Predict the Release of Microplastic Fibers during Domestic Washing

Muthusamy¹,

Periyasamy

Militký

et al. 2022

Journal of Testing and Evaluation

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Microplastic particles are a burgeoning population crisis in the marine environment. This work is to predict the releasing of microplastic fibers from the jeans made from polyester during domestic washing by using of adaptive neuro-fuzzy inference system (ANFIS) model.The advantage of the ANFIS model is to predict the variations in between the randomly chosen parameters. This prediction model can be cost-effective, slowed down to study behavior more closely. The consequence of washing duration, temperature, spin-speed, detergent types, and conditioner usage was investigated against the microplastic fibers releases. The washing temperature, washing duration, spin-speed, detergent types, and addition of conditioner are the main factors for this research work. The forecast presentations have been exposed by having a considerably lowered RMSE of 3.23 value than the variant of the experiment as exposed by its standard deviation for the ANFIS version. This ANFIS model will be able to provide a theoretical understanding to enhance and inhibit microplastic fibers releases from jeans.

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Artificial neural network for prediction of color adsorption from an industrial textile effluent using modified sugarcane bagasse: Characterization, kinetics and isotherm studies

Cited by 12 publications

References 39 publications

Machine learning in the evaluation and prediction models of biochar application: A review

Machine learning in the evaluation and prediction models of biochar application: A review

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Adaptive Neuro-Fuzzy Inference System to Predict the Release of Microplastic Fibers during Domestic Washing

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