Application of fuzzy logic in drying: A review

Hosseinpour, Soleiman; Martynenko, Alex

doi:10.1080/07373937.2020.1846192

Cited by 20 publications

(11 citation statements)

References 74 publications

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“…The FL methods can link human reasoning and concept formation through linguistic rules to obtain functions and control nonlinear systems. The FL can efficiently handle the complexity and uncertainty of the process with limited data [ 28 ]. The fuzzy logic can be applied with small data; hence they cannot occupy a huge memory space [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

et al. 2021

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Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model’s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.

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Section: Methodsmentioning

confidence: 99%

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

et al. 2021

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“…Almost all existing applications of fuzzy logic in drying technology to solve problems of nonlinear modeling, simulation, clustering, pattern recognition, control, classification, and optimization are comprehensively reviewed by Hosseinpour and Martynenko. [12] The benefits and shortcomings of fuzzy logic applications are also systematically reviewed. A roadmap for future studies based on the significant underexplored potential and current state of fuzzy logic in drying technology is proposed.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Critical reviews for facilitating innovations and advances in drying science and technology

Shirkole¹,

Thorat²,

Mujumdar³

2021

Drying Technology

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“…The computational efficiency is improved and overshoot is suppressed. The research [15] Moreover, there are some intelligent controllers such as neural-network-based control [16], [17], fuzzy logic control [18], [19]. These methods can control fast the difficult object.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

Vuong

Nguyen²

2023

IJECE

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<span lang="EN-US">This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling.</span>

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Application of fuzzy logic in drying: A review

Cited by 20 publications

References 74 publications

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

Critical reviews for facilitating innovations and advances in drying science and technology

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

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