A sensitivity analysis and multi-objective optimization to enhance ethylene production by oxidative dehydrogenation of ethane in a membrane-assisted reactor

2018 ◽  
Vol 26 (9) ◽  
pp. 1879-1895 ◽  
Author(s):  
Ali Darvishi ◽  
Ali Bakhtyari ◽  
Mohammad Reza Rahimpour
Keyword(s):  
Sensitivity Analysis ◽  
Oxidative Dehydrogenation ◽  
Ethylene Production ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Oxidative Dehydrogenation Of Ethane

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone

2017 ◽  
Vol 231 (11) ◽  
pp. 1012-1024 ◽  
Author(s):  
Vahid Tahmasbi ◽  
Majid Ghoreishi ◽  
Mojtaba Zolfaghari
Keyword(s):  
Sensitivity Analysis ◽  
Feed Rate ◽  
Process Temperature ◽  
Drilling Process ◽  
Drilling Speed ◽  
Multi Objective Optimization ◽  
Bone Drilling ◽  
Multi Objective ◽  
Tool Diameter ◽  
First Time

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.

Thermohydraulic sensitivity analysis and multi-objective optimization of Fe3O4/H2O nanofluid flow inside U-bend heat exchangers with longitudinal strip inserts

2020 ◽  
Vol 164 ◽  
pp. 114518 ◽  
Author(s):  
Amir Ebrahimi-Moghadam ◽  
Soheil Kowsari ◽  
Faezeh Farhadi ◽  
Mahdi Deymi-Dashtebayaz
Keyword(s):  
Sensitivity Analysis ◽  
Heat Exchangers ◽  
Multi Objective Optimization ◽  
Nanofluid Flow ◽  
Multi Objective ◽  
Longitudinal Strip

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

2009 ◽  
Author(s):  
M. Li ◽  
N. Williams ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Output Entropy ◽  
Global Uncertainty ◽  
Significant Attention

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “investment”. Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

2009 ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Interval Uncertainty ◽  
Minimal Amount ◽  
Engineering System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

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