The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

Taoufik, Nawal; Boumya, W.; Achak, M.; Chennouk, Hamid; Dewil, Raf; Barka, Noureddine

doi:10.1016/j.scitotenv.2021.150554

Cited by 80 publications

(30 citation statements)

References 86 publications

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“…It should be noted that physical interpretability of the results is an important question when ML models are applied in atmospheric studies (Hou et al, 2022). However, explanations of ML results (e.g., RI) are somewhat vague because ML is a "black-box" model from the point view of chemical mechanism (Hou et al, 2022;Taoufik et al, 2022). In this study, we used the RF model to evaluate the prediction performance of atmospheric O 3 using the TVOCs, measured VOC species, and photochemical initial concentration (PIC) of VOC species, which is calculated based on the photochemical-age approach (Shao et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Zhan

Liu

et al. 2022

Atmos. Meas. Tech.

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Abstract. The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing, China, from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using concentrations of measured/initial VOC species (R2=0.82/0.81) was better than that using total VOC (TVOC) concentrations (R2=0.77). Meanwhile, the RIs of initial VOC species correlated well with their O3 formation potentials (OFPs), which indicate that the model results can be partially explained by the maximum incremental reactivity (MIR) method. O3 formation presented a negative response to nitrogen oxides (NOx) and relative humidity (RH), and a positive response to temperature (T), solar radiation (SR), and VOCs. The O3 isopleth calculated by the RF model was generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis.

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Section: Introductionmentioning

confidence: 99%

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Zhan

Liu

et al. 2022

Atmos. Meas. Tech.

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“…A few examples of such applications are presented in Table 1. The mostly used ML algorithm for modeling various adsorption processes is the artificial neural network (ANN) [28,29]. It has been used all across the world for classification and prediction purposes in a wide range of real-time adsorption applications [14][15][16][17][18][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

2022

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Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.

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“…Different algorithms are trained to develope predicting models using statistical approaches, revealing crucial insights in data mining initiatives [6]. Voluminous data can be efficiently analized by machine learning algorithms in order to identify complex patterns, and extract conclusions [7]. The traditional machine learning models such as Bayesian networks, linear regression, logistic regression, support vector machine (SVM), radial basic function, and single-layer artificial neural networks cannot scale effectively, when the amount of training data is excessive.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction applying artificial neural network at micro machining

Adizue

Balázs

Takács

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Micro machining forms an integral part of modern manufacturing technology. In order to satisfy the most important demands, like cost reduction, quality improvement, and production efficiency, this process has to be implemented into the framework of Industry 4.0, too. Defect reduction of products can be realized by beneficial trends of Industry 4.0, such as application of sensors driven AI based predictive models. Surface roughness is one of the main indicators, which can be used for evaluation of the machined surface. The aim of this research is to develop a predictive surface roughness model in the case of micro milling of hard materials. Most important scientific knowledge related to micro milling, and application possibilities of AI based predictive models in the case of chip removal processes were summarised. Micro milling experiments on hot work tool steel (AISI H13) were carried out in order to determine the effect of different cutting parameters on surface roughness. The effects of the cutting parameters were investigated by the analysis of variance (ANOVA). Two-layer feedforward artificial neural network (ANN) based model was trained, validated and tested by means of experimental results. It was found that the trained model has an average MSE, RMSE and R values of 0.00063850, 0.019116835 and 0.9948, while the additional validation test provided 0.00038148, 0.019531513 and 0.8545, respectively, which are statistically significant. The developed model can effectively predict the surface roughness under the investigated circumstances.

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The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

Cited by 80 publications

References 86 publications

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

Surface roughness prediction applying artificial neural network at micro machining

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