2021
DOI: 10.1038/s41598-021-82104-8
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Finite-difference based response surface methodology to optimize tailgate support systems in longwall coal mining

Abstract: Designing a suitable support system is of great importance in longwall mining to ensure the safe and stable working conditions over the entire life of the mine. In high-speed mechanized longwall mining, the most vulnerable zones to failure are roof strata in the vicinity of the tailgate roadway and T-junctions. Severe roof displacements are occurred in the tailgate roadway due to the high-stress concentrations around the exposed roof span. In this respect, Response Surface Methodology (RSM) was utilized to opt… Show more

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Cited by 8 publications
(3 citation statements)
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“…This mathematical model can describe the response surface linearly, the interaction of two factors (2FI), squared and cubic. RSM will predict the most suitable mathematical model to describe the relationship between responses and factors without further complicated and timeconsuming numerical simulations [29]. The empirical relationship between the observed response and factor effects is expressed by a second-order polynomial equation ( 6), (7), and (8).…”
Section: Discussionmentioning
confidence: 99%
“…This mathematical model can describe the response surface linearly, the interaction of two factors (2FI), squared and cubic. RSM will predict the most suitable mathematical model to describe the relationship between responses and factors without further complicated and timeconsuming numerical simulations [29]. The empirical relationship between the observed response and factor effects is expressed by a second-order polynomial equation ( 6), (7), and (8).…”
Section: Discussionmentioning
confidence: 99%
“…The four significant PBD variables, peptone (g/l), temperature (°C), agitation (rpm) and inoculum size (%), were evaluated as factors, and carotenoid yield was recorded as the response in the central composite inscribed design (CCI) using Design Expert (Table 5 ) 89 . The relationship between the independent factors and dependent responses was calculated by using a quadratic polynomial equation as follows 90 : where Y is the response (carotenoids yield in µg/ml), X 0 is the intercept or regression coefficient; β i , β ii , and β ij are the linear, quadratic and interaction coefficients respectively; X i and X j are the coded values of the variables; E is the experimental/residual error and K is the number of variables.…”
Section: Methodsmentioning
confidence: 99%
“…On average, the model uniaxial compressive strength value was 0.284 of the laboratory strength value. By estimating the mechanical parameters of the coal and rock masses used in the numerical model 26 , 27 , it was determined that the elastic modulus, cohesion, and tensile strength values corresponded to 0.2 of the laboratory testing result values and that the Poisson's ratio was 1.2 of the laboratory testing result value.…”
Section: Engineering Backgroundmentioning
confidence: 99%