SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration

Arshad, Ushtar; Taqvi, Syed Ali Ammar; Buang, Azizul; Awad, Ali S.

doi:10.1016/j.psep.2021.06.001

Cited by 27 publications

(7 citation statements)

References 48 publications

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“…Subsequently, a theory-guided LSTM was proposed for pipeline shutdown pressure predictions. Arshad et al (2021) combined machine learning and statistical methods to establish a variety of prediction models for predicting the minimum ignition temperature of iron dust. Wang et al (2019a) established a multivariate non-linear regression mathematical model using multiple influencing factors to predict the Sauter mean diameter.…”

Section: Open Accessmentioning

confidence: 99%

“…The prediction technique employed in this study is based on machine learning. The existing popular research methods in machine learning include the GA-BP neural network model, Grey neural network model, BP neural network, extreme learning machine, support vector machine, artificial neural network, grid search algorithm, a gradient boosting decision tree, and generative time intervals imputation network (Huang et al, 2019;Tan et al, 2019;Arshad et al, 2021;Kim et al, 2021;Wang et al, 2022a;Liu et al, 2022;Weng and German Paal, 2022). However, the existing prediction methods are only applicable to data features with a single data quantity or evident data characteristic.…”

Section: Open Accessmentioning

confidence: 99%

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Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Dong

Xie²,

Jiang³

et al. 2023

Front. Earth Sci.

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Introduction: In the coal mining process, the intense mining pressure is an important factor hindering the safe and efficient production of the working face. In severe cases, it causes deformations in roadways such as roof breakages and rockbursts, and leads to instability. This can result in the roof falling over a large area and the coal wall, thereby inducing dynamic disasters. These aspects have restricted the economic benefits of coal.Methods: In this study, we set four model limitations based on the limited scope of action of the mining pressure itself and the quantitative relationships between mining pressures in different regions. A multiple linear regression model with these limitations is proposed for predicting the mining pressure for preventing roof breakages and rockbursts. Based on a hydraulic support monitoring dataset from a fully mechanized caving face of coal mining, the mining pressure prediction model is trained by using the first 70% of the dataset. And the linear regression coefficient of the model and the predicted value of the mining pressure are obtained. Then, the last 30% of the dataset was used for the validation of the model.Results: The research results show that the constrained multiple linear regression model can achieve remarkable prediction results. According to predictions of tens of thousands of on-site mining pressure datasets, the predicted data and actual pressure data have the same change trend and maintain a low relative error.Discussion: Therefore, after real-time mining pressure monitoring, the system obtains the roof pressure of the fully mechanized mining face. According to the dataset, the proposed prediction model algorithm quickly predicts the roof pressure value of the next mining section and effectively forewarns roof breakages and other accidents.

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Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Dong

Xie²,

Jiang³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…However, ARIMA methods mainly focus on linear chemical process data. To adapt to the nonlinear characteristics of chemical process data, some more flexible data prediction methods have been proposed, such as artificial neural network (ANN) [75], support vector machine (SVM) [76], and autoencoder (AE) [77]. However, these methods cannot effectively extract time series features.…”

Section: Early Prediction and Warningmentioning

confidence: 99%

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

et al. 2023

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In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.

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“…The principal advantage of an ANN is in its ability to learn from the observed data and approximate a function [39]. Mostly, this technique is used as a black box approach to model the linear as well as complex non-linear systems [40][41][42]. In this study, the series-parallel structure of the NARX (nonlinear autoregressive with exogenous inputs) network is used to develop the ANN models for the top and bottom composition predictions.…”

Section: Development Of Ann Modelsmentioning

confidence: 99%

Intelligent Control of an Industrial Debutanizer Column

et al. 2022

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A debutanizer column located at a refinery in Malaysia produces LPG as its top product and light naphtha as its bottom product. Control of the product compositions of the column is very important to maintain the desired purities. The current control design of the debutanizer column is based on the classical proportional integral derivative approach, which has been found to be less effective in controlling the variations in the column as the process is characterized by strong nonlinear and uncertain dynamics. Here, intelligent control based on an adaptive neurofuzzy inference system (ANFIS) is investigated and compared against an artificial neural network, for the special case of limited training data of the industrial debutanizer column. The ANFIS-based controller outperforms the other control configurations for both set point tracking and disturbance rejection cases and has better generalization performance even when trained with limited data samples.

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SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration

Cited by 27 publications

References 48 publications

Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

Intelligent Control of an Industrial Debutanizer Column

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