Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Agarwal, Subham; Dandge, Shruti Sudhakar; Chakraborty, Shankar

doi:10.22190/fume191118007a

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…According to the fuzzy concept (e.g., [ 12 – 18 ]), the uncertain data are represented by fuzzy sets and their membership functions that must be known in advance as a part of the problem itself. According to the rough concept [ 7 , 19 – 21 ], the uncertain data are represented by rough sets and/or rough functions that use the lower and upper approximations for the rough objective and constraints that must be known in advance as a part of the problem itself. Finally, according to the interval concept, the uncertain coefficients are represented as closed intervals that are supposed to be known in advance as a part of the problem itself.…”

Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.

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Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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“…The smaller the entropy value, the larger the information utility value. To better represent the proportion of each factor in the overall oil well data of the block, the difference coefficient of the j-th index is calculated using formula (9). The greater the difference in the index value, the greater the impact on the program evaluation.…”

Section: Plos Onementioning

confidence: 99%

“…Data mining technology can extract unknown hidden correlations that have potential application value from a large amount of noisy practical data, and convert this data into useful information [5,6]. At present, AI technology has developed into various fields [7,8], data mining technology has gradually matured, and the application frequency of rough set theory, neural network, and cluster analysis is extremely high [9][10][11]. At present, many scholars have applied data mining technology to the oilfield industry, mainly in many aspects such as water injection optimization, production forecasting, and enhanced oil recovery in the oil industry [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Main control factors affecting mechanical oil recovery efficiency in complex blocks identified using the improved k-means algorithm

Wang

Jiang

et al. 2021

PLoS ONE

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The system efficiency of pumping units in the middle and late stages of oil recovery is characterized by several factors, complex data and poor regulation. Further, the main control factors that affect system efficiency in different blocks vary greatly; therefore, it is necessary to obtain the block characteristics to effectively improve system efficiency. The k-means algorithm is simple and efficient, but it assumes that all factors have the same amount of influence on the output value. This cannot reflect the obvious difference in the influence of several factors in the block on the efficiency. Moreover, the algorithm is sensitive to the selection of the initial cluster centre point, so each calculation result that reflects the efficiency characteristics of the block system cannot be unified. To solve the aforementioned problems affecting the k-means algorithm, the correlation coefficient of all the factors was first calculated, followed by extracting the system efficiency of the positive and negative indicators of standardization. Next, the moisture value was calculated to obtain the weight of each factor used as a coefficient to calculate the Euclidean distance. Finally, the initial centre point selection of the k-means algorithm problem was solved by combining the dbscan and weighted k-means algorithm. Taking an oil production block in the Daqing Oilfield as the research object, the k-means and improved algorithm are used to analyse the main control factors influencing mechanical production efficiency. The clustering results of the two algorithms have the characteristics of overlapping blocks, but the improved algorithm’s clustering findings are as follows: this block features motor utilization, pump efficiency and daily fluid production, which are positively correlated with system efficiency. Further, low-efficiency wells are characterized by the fact that the pump diameter, power consumption, water content, daily fluid production, oil pressure and casing pressure are significantly lower than the block average; high-efficiency wells are characterized by pump depths lower than the block average. For this block, it is possible to reduce the depth of the lower pump and increase the water-injection effect to increase the output under conditions of meeting the submergence degree, which can effectively improve the system efficiency.

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“…Božanic [65] discussed a hybrid LBWA-IR-MAIRCA multi-criteria decision-making model for determination of constructive elements of weapons. Agarwal et al's [66] study involved a parametric analysis of a grinding process using the rough sets theory.…”

Section: Introductionmentioning

confidence: 99%

Linear Diophantine Fuzzy Rough Sets: A New Rough Set Approach with Decision Making

et al. 2022

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In this article, a new hybrid model named linear Diophantine fuzzy rough set (LDFRS) is proposed to magnify the notion of rough set (RS) and linear Diophantine fuzzy set (LDFS). Concerning the proposed model of LDFRS, it is more efficient to discuss the fuzziness and roughness in terms of linear Diophantine fuzzy approximation spaces (LDFA spaces); it plays a vital role in information analysis, data analysis, and computational intelligence. The concept of (<p,p′>,<q,q′>)-indiscernibility of a linear Diophantine fuzzy relation (LDF relation) is used for the construction of an LDFRS. Certain properties of LDFA spaces are explored and related results are developed. Moreover, a decision-making technique is developed for modeling uncertainties in decision-making (DM) problems and a practical application of fuzziness and roughness of the proposed model is established for medical diagnosis.

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Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Cited by 9 publications

References 29 publications

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Main control factors affecting mechanical oil recovery efficiency in complex blocks identified using the improved k-means algorithm

Linear Diophantine Fuzzy Rough Sets: A New Rough Set Approach with Decision Making

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