D-Optimal design and multi-objective optimization for green extraction conditions developed with ultrasonic probe for oleuropein

Author(s):  
Nilüfer Vural ◽  
Özge Algan Cavuldak ◽  
M. Abdülkadir Akay
2018 ◽  
Vol 102 ◽  
pp. 134-140 ◽  
Author(s):  
Okjeong Lee ◽  
Sangdan Kim ◽  
Jungmin Lee ◽  
Yoonkyung Park

2017 ◽  
Vol 59 (4) ◽  
pp. 1750019-1-1750019-23 ◽  
Author(s):  
Lamanto T. Somervell ◽  
Santosh G. Thampi ◽  
A. P. Shashikala

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 156
Author(s):  
Rongchao Jiang ◽  
Shukun Ci ◽  
Dawei Liu ◽  
Xiaodong Cheng ◽  
Zhenkuan Pan

The lightweight design of vehicle components is regarded as a complex optimization problem, which usually needs to achieve two or more optimization objectives. It can be firstly solved by a multi-objective optimization algorithm for generating Pareto solutions, before then seeking the optimal design. However, it is difficult to determine the optimal design for lack of engineering knowledge about ideal and nadir values. Therefore, this paper proposes a multi-objective optimization procedure combined with the NSGA-II algorithm with entropy weighted TOPSIS for the lightweight design of the dump truck carriage. The finite element model of the dump truck carriage was firstly developed for modal analysis under unconstrained free state and strength analysis under the full load and lifting conditions. On this basis, the multi-objective lightweight optimization of the dump truck carriage was carried out based on the Kriging surrogate model and the NSGA-II algorithm. Then, the entropy weight TOPSIS method was employed to select the optimal design of the dump truck from Pareto solutions. The results show that the optimized dump truck carriage achieves a remarkable mass reduction of 81 kg, as much as 3.7%, while its first-order natural frequency and strength performance are slightly improved compared with the original model. Accordingly, the proposed procedure provides an effective way for vehicle lightweight design.


2014 ◽  
Vol 69 (10) ◽  
pp. 2052-2058 ◽  
Author(s):  
Wenliang Chen ◽  
Chonghua Yao ◽  
Xiwu Lu

Optimal design of activated sludge process (ASP) using multi-objective optimization was studied, and a benchmark process in Benchmark Simulation Model 1 (BSM1) was taken as a target process. The objectives of the study were to achieve four indexes of percentage of effluent violation (PEV), overall cost index (OCI), total volume and total suspended solids, making up four cases for comparative analysis. Models were solved by the non-dominated sorting genetic algorithm in MATLAB. Results show that: ineffective solutions can be rejected by adding constraints, and newly added objectives can affect the relationship between the existing objectives; taking Pareto solutions as process parameters, the performance indexes of PEV and OCI can be improved more than with the default process parameters of BSM1, especially for N removal and resistance against dynamic NH4+-N in influent. The results indicate that multi-objective optimization is a useful method for optimal design ASP.


Sign in / Sign up

Export Citation Format

Share Document