scholarly journals Optimal Design of Laboratory and Pilot-Plant Experiments Using Multiobjective Optimization

2017 ◽  
Vol 89 (5) ◽  
pp. 645-654 ◽  
Author(s):  
Esther Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Norbert Asprion ◽  
Michael Bortz
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Pilot Plant

scholarly journals Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

2021 ◽  
Author(s):  
Esther Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Norbert Asprion ◽  
Michael Bortz
Keyword(s):  
Experimental Design ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Of Experiments ◽  
Pilot Plant ◽  
Pareto Frontier ◽  
Trade Offs ◽  
Interesting Part ◽  
Conflicting Objectives ◽  
Statistical Approaches

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

scholarly journals Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

2018 ◽  
Author(s):  
Ester Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Michael Bortz ◽  
Norbert Asprion
Keyword(s):  
Experimental Design ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Of Experiments ◽  
Pilot Plant ◽  
Pareto Frontier ◽  
Trade Offs ◽  
Interesting Part ◽  
Conflicting Objectives ◽  
Statistical Approaches

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

Entropy-based Optimization for Heat Transfer Enhancement in Tubes with Helical Fins

2021 ◽  
Author(s):  
Bharath Pidaparthi ◽  
Peiwen Li ◽  
Samy Missoum
Keyword(s):  
Heat Transfer ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Heat Transfer Enhancement ◽  
Point Of View ◽  
Design Solution ◽  
Total Entropy ◽  
Transfer Enhancement ◽  
Pressure Drops ◽  
Conflicting Objectives

Abstract In this work, a tube with internal helical fins is analyzed and optimized from an entropy generation point of view. Helical fins, in addition to providing heat transfer enhancements, have the potential to level the temperature of the tube under non-uniform circumferential heating. The geometric parameters of helical fins are optimized under two different entropy-based formulations. Specifically, this work focuses on comparing the optimal design solution obtained through the minimization of total entropy and through the multiobjective optimization of the thermal and viscous entropy contributions when considered as two separate objectives. The latter quantities being associated with heat transfer and pressure drops, it is shown that, from a design optimization point of view, it is important to separate both entropies which are conflicting objectives.

Optimal Design of Monolithic ESBT Device carried out by Multiobjective Optimization

2007 ◽  
pp. 339-346
Author(s):  
Salvatore Spinella ◽  
Vincenzo Enea ◽  
Daniele Kroell ◽  
Michele Messina ◽  
Cesare Ronsisvalle
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization

MULTIOBJECTIVE OPTIMIZATION OF PROCESS PLANT USING GENETIC ALGORITHM

2006 ◽  
Vol 06 (03) ◽  
pp. 315-327 ◽  
Author(s):  
L. GOVINDARAJAN ◽  
T. KARUNANITHI
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Large Scale ◽  
Raw Material ◽  
Fine Tuning ◽  
Design Parameters ◽  
Nsga Ii ◽  
Process Plant ◽  
Mathematical Techniques

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams–Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

scholarly journals A Modified Multiobjective Self-Adaptive Differential Evolution Algorithm and Its Application on Optimization Design of the Nuclear Power System

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yunjia Yang ◽  
Shinian Peng ◽  
Li Zhu ◽  
Dan Zhang ◽  
Zhifang Qiu ◽  
...  
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Power System ◽  
Differential Evolution ◽  
Nuclear Power ◽  
Optimization Design ◽  
Differential Evolution Algorithm ◽  
Nuclear Power System ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

scholarly journals Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
P. Sabarinath ◽  
M. R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Production Costs ◽  
Machining Parameters ◽  
Conflicting Objectives

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

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