The use of sensitivity analysis and genetic algorithms for the management of catalyst emissions from oil refineries

Whitcombe, Joshua Matthew; Cropp, Roger Allan; Braddock, Roger David; Agranovski, Igor E.

doi:10.1016/j.mcm.2006.01.003

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Figure 12 shows the regression diagrams in which the regression value obtained is 0.9703, where the number of hidden nodes is 9. Figure 13 shows the sensitivity analysis 74 derived from the artificial neural network model, using the connection weight method. Sensitivity analysis describes the influence of the input variables over output variables.…”

Section: Resultsmentioning

confidence: 99%

Design of patient specific basal dental implant using Finite Element method and Artificial Neural Network technique

Choudhury

Rana

Chakraborty

et al. 2022

Proc Inst Mech Eng H

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The bone conditions of mandibular bone vary from patient to patient, and as a result, a patient-specific dental implant needs to be designed. The basal dental implant is implanted in the cortical region of the bone since the top surface of the bone narrows down because of aging. Taguchi designs of experiments technique are used in which 25 optimum solid models of basal dental implants are modeled with variable geometrical parameters, viz. thread length, diameter, and pitch. In the solid models the implants are placed in the cortical part of the 3D models of cadaveric mandibles, that are prepared from CT data using image processing software. Patient-specific bone conditions are varied according to the strong, weak, and normal basal bone. A compressive force of 200 N is applied on the top surface of these implants and using finite element analysis software, the microstrain on the peri-implant bone ranges from 1000 to 4000 depending on the various bone conditions. According to the finite element data, it can be concluded that weak bone microstrain is comparatively high compared with normal and strong bone conditions. A surrogate artificial neural network model is prepared from the finite element analysis data. Surrogate model assisted genetic algorithm is used to find the optimum patient-specific basal dental implant for a better osseointegration-friendly mechanical environment.

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Section: Resultsmentioning

confidence: 99%