Discussion: A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity [Arab J Geosci (2021) 14(7), 534]

O’Kelly, Brendan C.; Soltani, Amin

doi:10.1007/s12517-021-08566-2

Cited by 4 publications

(2 citation statements)

References 40 publications

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Section: Introductionmentioning

confidence: 99%

“…Artificial Neural Networks: Artificial Neural Networks (ANN) have been used in various characteristic models (Al-Mukhtar et al, 2020 ; Teymen and Mengüç, 2020 ; O'Kelly and Soltani, 2021 ). These models show good accuracy compared to regression models where all the features are present (O'Kelly and Soltani, 2021 ). However, these models were implemented on systems with all the features accurately measured and have not been implemented on a system with lacking feature sensors.…”

Section: Introductionmentioning

confidence: 99%

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Integration of machine learning with complex industrial mining systems for reduced energy consumption

Harmse

Laar

Pelser

et al. 2022

Front. Artif. Intell.

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The deep-level mining industry is experiencing narrowing profit margins due to increasing operating costs and decreasing production. The industry is known for its lack of dynamic control across complex integrated systems running deep underground, making IoT technologies difficult to implement. An important integrated system in a typical underground mine is the refrigeration-ventilation system. In practice, the two systems are still controlled independently, often due to a lack of continuous measurements. However, their integrated effects ultimately affect energy usage and production. This study develops and compares various machine learning prediction techniques to predict the integrated behavior of a key component operating on the boundary of the refrigeration-ventilation system, while also addressing the lack of continuous measurements. The component lacks sensors and the developed industrial machine learning models negate the effect thereof using integrated control. The predictive models are compared based on accuracy, prediction time, as well as the amount of data required to obtain the required level of accuracy. The “Support Vector Machines” method achieved the lowest average error (1.97%), but the “Artificial Neural Network” method is more robust (with a maximum percentage error of 12.90%). A potential energy saving of 215 kW or 2.9% of the ventilation and refrigeration system, equivalent to R1.33-million per annum ($82 9001) is achievable using the “Support Vector Machines” method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of machine learning with complex industrial mining systems for reduced energy consumption

Harmse

Laar

Pelser

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

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Discussion: Development of a Single-Point Method to Determine Soil Plastic Limit Using Fall-Cone Data [Geotech Geol Eng 41:4473–4485, 2023]

O’Kelly,

Soltani

2023

Geotech Geol Eng

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This discussion article presents a critical appraisal of three empirical correlations developed via multiple regression analysis and presented in the Kayabali et al. [Geotech Geol Eng 41:4473–4485, 2023. https://doi.org/10.1007/s10706-023-02527-0] (the Authors’) investigation for the determination of the soil consistency limits. Specifically, based solely on British Standard (BS) fall-cone (FC) test data, the Authors purport that the correlations given by Equations 2 and 3 of their paper can be used to predict the ASTM rolling-plate plastic limit (i.e., PLRP), while their Equation 4 can be used to predict the BS FC liquid limit (i.e., LLFC). The Authors demonstrated that these correlations gave good predictions of the measured LLFC and PLRP water contents (i.e., wL(FC) and wP(RP), respectively) for 87 fine-grained soils they sourced from different parts of Central Turkey. Employing newly compiled large and diverse consistency limits databases assembled from the published research literature, this discussion article confirms that the Authors’ Equations 2 and 3 generally produce poor wP(RP) predictions for the fine-grained soils comprising these databases, invariably overestimating (often seriously) their measured plastic limit values. Hence, the Discussers recommend that the Authors’ Equations 2 and 3 (being generally unreliable beyond the investigated Turkish soils) should not be used in geotechnical engineering practice. While the Authors’ single-point LLFC method given by their Equation 4 broadly appears as a good wL(FC) predictor for the newly compiled database soils, it is noted that there already exist well-established and standardised single-point LLFC methods.

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Reply to “Discussion: A Comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity [Arab J Geosci (2021) 14(7): 534] Brendan O’Kelly and Amin Soltani”

et al. 2021

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Discussion: A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity [Arab J Geosci (2021) 14(7), 534]

Cited by 4 publications

References 40 publications

Integration of machine learning with complex industrial mining systems for reduced energy consumption

Integration of machine learning with complex industrial mining systems for reduced energy consumption

Discussion: Development of a Single-Point Method to Determine Soil Plastic Limit Using Fall-Cone Data [Geotech Geol Eng 41:4473–4485, 2023]

Reply to “Discussion: A Comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity [Arab J Geosci (2021) 14(7): 534] Brendan O’Kelly and Amin Soltani”

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