scholarly journals Simulated annealing technique to design minimum cost exchanger

2011 ◽  
Vol 17 (4) ◽  
pp. 409-427 ◽  
Author(s):  
Nadeem Khalfe ◽  
Kumar Lahiri ◽  
Kumar Wadhwa
Keyword(s):  
Simulated Annealing ◽  
Heat Exchangers ◽  
Cost Minimization ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Minimum Cost ◽  
Geometric Parameters ◽  
Tube Length ◽  
Total Annual Cost ◽  
Economic Point

Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters to satisfy a given heat duty and constraints. Although well proven, this kind of approach is time consuming and may not lead to cost effective design as no cost criteria are explicitly accounted for. The present study explores the use of nontraditional optimization technique: called simulated annealing (SA), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut etc and minimization of total annual cost is considered as design target. The presented simulated annealing technique is simple in concept, few in parameters and easy for implementations. Furthermore, the SA algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The SA approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported GA approach.

Particle swarm optimization technique for the optimal design of shell and tube heat exchangers

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Sandip Kumar Lahiri ◽  
Nadeem Muhammed Khalfe ◽  
Shiv Kumar Wadhwa
Keyword(s):  
Heat Exchanger ◽  
Heat Exchangers ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Geometric Parameters ◽  
Total Annual Cost ◽  
Swarm Optimization ◽  
Economic Point ◽  
Shell And Tube ◽  
Operational Constraints

Abstract Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until given heat duty and set of geometric and operational constraints are satisfied.Although well proven, this kind of approach is time consuming and may not lead to cost effective design. The present study explores the use of non-traditional optimization technique: calledParticle swarm optimization (PSO), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tubelength, bafflespacing, number of tube passes, tubelayout, type of head, baffle cutetc and minimization of total annual cost is considered as design target. The presented PSO technique is conceptually simple, has only a few parameters and is easy to implement.Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm . The PSO method leads to a design of a heat exchanger with a reduced cost of heat exchanger as compare to cost obtained by previously reported GA approach.

Optimal Thermal Design of Fin-and-Tube Heat Exchangers by Integration of a SUMT and a SCGM

2016 ◽  
Author(s):  
Xinyi Li ◽  
Ting Ma ◽  
Qiuwang Wang
Keyword(s):  
Heat Exchangers ◽  
Optimization Technique ◽  
Unconstrained Minimization ◽  
Geometric Parameters ◽  
Thermal Design ◽  
Trial And Error ◽  
Heat Transfer Area ◽  
Compact Heat Exchangers ◽  
Minimization Technique ◽  
Combined Structure

It is a recognized hard task for the traditional thermal design of compact heat exchangers to obtain the optimal geometric parameters efficiently and effectively, owing to its complex trial-and-error process. In response to this issue, a simplified conjugate-gradient method (SCGM) combined with a sequential unconstrained minimization technique (SUMT) as a favorable optimization technique is incorporated with the traditional thermal design in this study, and then the key geometric parameters of fin-and-tube heat exchangers (FTHEs) are investigated and optimized successfully. In this method, the minimum total weight of FTHEs as the final objective is discussed, involving two geometric parameters, diameter of tube and height of shape as search variables. Aiming to minimize the objective function, SCGM is introduced to the SUMT to update the search variables continually with the fixed search steps and the search directions. Meanwhile, with the known geometric parameters from the SUMT, the log-mean temperature difference method (LMTD) is applied to determine the heat transfer area under the combined structure sizes for a given heat duty. Additionally, optimization results for three different heat duty is discussed in this work. The results show that it is effective to obtain the optimal sets of geometric parameters of FTHEs by the present method, and there are some guidance values for the thermal designs of compact heat exchangers.

A Genetic Algorithm for Cost Estimating Turned Parts in an Interactive DFM Environment

2003 ◽  
Author(s):  
R. Sebastian Schrader ◽  
Michael L. Philpott ◽  
Gautam Subbarao ◽  
Dale E. Holmes
Keyword(s):  
Genetic Algorithms ◽  
Initial Conditions ◽  
Cost Minimization ◽  
Optimization Technique ◽  
Minimum Cost ◽  
Cutting Parameters ◽  
Replacement Policy ◽  
Total Production ◽  
Machining Parameters ◽  
Tool Replacement

This paper presents a method for the optimization of machining parameters, essential for the determination of an economical operating point for multi-pass turning operations. Optimal selection of cutting parameters such as the number of passes, depth of cuts, cutting speeds and feed rates are critical to process planning and cost optimization. This in turn creates a crossing point between product design and manufacturing. Generally, machining models are complex since they are highly non-linear. This research utilizes genetic algorithms as an optimization technique in order to maximize the accuracy of results, the computational achievement, and to minimize the influence of initial conditions and part geometry. A new approach that uses conventional turning process modeling along with genetic algorithms to rapidly search and optimize the feasible workspace is considered. The total production cost minimization is achieved by adding together the minimum cost of each roughing pass and the final finishing pass. In addition, the preventive tool replacement policy used in practice is incorporated. Finally, the results obtained for test cases are evaluated and compared.

Genetic Algorithm for Material Cost Minimization of External Strengthening System with Fiber Reinforced Polymer

2012 ◽  
Vol 468-471 ◽  
pp. 1817-1822
Author(s):  
Md. Moshiur Rahman ◽  
Mohd Zamin Jumaat ◽  
Md. Akter Hosen
Keyword(s):  
Genetic Algorithm ◽  
Cost Minimization ◽  
Optimization Procedure ◽  
Minimum Cost ◽  
Optimization Method ◽  
Epoxy Adhesive ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Limit States ◽  
Reinforced Polymer

An optimization procedural method for designing fiber reinforced polymer (FRP) plate for strengthening reinforced concrete beam is presented. The optimization procedure is formulated to find the design variables leading to the minimum cost of structural strengthening system using CFRP plate with constraints imposed based on TR55 code provisions. Genetic algorithm based approach is utilized to solve the optimization task. The cost of FRP plate and epoxy adhesive is included in the formulation of the objective function. The ultimate limit states and the serviceability limit states are included in formulation of constraints. A numerical example is given to show the validity of the proposed optimization method.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Primena Lagranžove optimizacije na problematiku proizvodnje energije iz obnovljivih izvora

2020 ◽  
Vol 22 (1-2) ◽  
pp. 145-152
Author(s):  
Vladan Ristić ◽  
◽  
Nikola Rajaković
Keyword(s):  
Power Plants ◽  
Production Systems ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Sole Source ◽  
Renewable Sources ◽  
Hydro Power ◽  
Ecological Awareness ◽  
Hydro Power Plants ◽  
Feed In Tariff

The apparent increase of ecological awareness during the previous decade has led to the need for adaptation of numerous areas of everyday human lives, so they fit the newly developed environmental tendencies. One of the areas in which this necessity was prominent was the power engineering, where, above all, the understanding of production systems was affected, with the turnabout regarding the distancing from the conventional sources and inclining towards the more acceptable renewable sources taking place. However, alongside the undeniable ecological sustainability of these sources, the financial sustainability of their utilization needs to be considered as well, which is why the subject of this paper is the minimization of costs of energy production in these capacities, if the fictious demand area needed to be supplied. It was assumed that this area is separated from transmission and distribution network, with the sole source of energy for the analyzed consumption being the wind, solar and hydro power plants, located in the close vicinity of the examined area. As the objective of the optimization, performed by the method of Lagrange multipliers, the appropriate apportioning of the generation powers in these plants for every hour during the year was selected, so the total yearly costs of supplying the demand were as low as possible. Also, considering the currently promoted methods intended for encouragement of the potential investors to proceed with the projects that encompass renewable sources, the different cases for which the share of renewables included in the feed-in tariff was varied. Finally, in order to provide the full insight into the optimization procedure, for the randomly selected hour of the year and share of feed-in tariff in the generation capacities, the process of calculation of production powers in the described plants and multipliers needed for application of the selected optimization technique was enclosed.

Design Optimization of an L-Shaped Extrusion Die

2009 ◽  
Author(s):  
Oktay Yilmaz ◽  
Hasan Gunes ◽  
Kadir Kirkkopru
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Narrow Channel ◽  
Geometric Parameters ◽  
Die Design ◽  
Dimensional Parameter ◽  
Large Channel

It is an important problem in the polymer extrusion of complex profiles to balance the flow at the die exit. In this paper, we employ simulated annealing-kriging meta-algorithm to optimize the geometric parameters of a die channel to obtain a uniform exit velocity distribution. Design variables for our optimization problem involve the suitable geometric parameters for the die design, which are the thickness of the large channel and the length of the narrow channel. Die balance is based on the deviation of the velocity with respect to the average velocity at the die exit. So the cost function for the optimization problem involves the minimization of this deviation. For the design of numerical experiments, we use Latin Hypercube Sampling (LHS) to construct the kriging model. Then, based on the LHS points, the numerical solutions are performed using Polyflow, a commercial software based on the finite element method and is specifically designed to simulate the flow and heat transfer of non-newtonian, viscoelastic fluids. In our simulations, a HDPE (high density polyethylene) is used as extrusion material. Having obtained numerical simulations for N = 60 LHS points in two-dimensional parameter space (t and L), the optimization of these parameters is carried out by Simulated Annealing (SA) method in conjunction with kriging model. We show that kriging model employed in SA algorithm can be used to optimize the die geometry.

Optimization of a Treatment System of Wastewater Streams for Electrochemical Cr(VI) Reduction: Selective versus Centralized Treatment

2018 ◽  
Vol 16 (11) ◽  
Author(s):  
Adrián López-Yañez ◽  
Jorge Ramirez-Muñoz ◽  
Alejandro Alonso ◽  
Luis G. Cota ◽  
Jhonny Pérez Montiel
Keyword(s):  
Hexavalent Chromium ◽  
Minimum Cost ◽  
Electrical Energy ◽  
Electrical Current ◽  
Electrochemical Process ◽  
Effluent Treatment ◽  
Treated Wastewater ◽  
Total Annual Cost ◽  
Optimal Solutions ◽  
Selective Treatment

Abstract The problem of the optimization of selective treatment systems of wastewater streams contaminated with hexavalent chromium [Cr(VI)] is investigated. In order to comply with the Mexican environmental norm of Cr(VI) for treated wastewater streams at minimum cost, a nonlinear programming (NLP) model for the electrochemical reduction of hexavalent chromium to trivalent chromium was developed. The model incorporates a variable reaction rate, which is a function of the Cr(VI) concentration and the electrical current density of the electrochemical process. For this purpose, a basic superstructure of the effluent treatment is proposed. The superstructure is composed of three continuous electrochemical reactors without recirculation, and it may produce either a series and/or parallel design topology. The NLP model was used to minimize the objective function, defined as the total annual cost (TAC), which includes the capital cost of each electrochemical reactor, the electrical energy cost and the cost of the treatment of the wastewater streams. In order to investigate the solution set of the proposed NLP model, i. e., to improve the possibilities of obtaining optimum solutions based on economic criteria, a multi-start algorithm was implemented. Two example problems are used to show the versatility of the model and different local optimal solutions were obtained for each case study. The results show that a selective treatment of wastewater streams based on the search of local optimal solutions yields significant savings with respect to a centralized treatment design.

scholarly journals Optimal design of a DC MHD pump by simulated annealing method

2014 ◽  
Vol 11 (2) ◽  
pp. 339-350
Author(s):  
Khadidja Bouali ◽  
Fatima Kadid ◽  
Rachid Abdessemed
Keyword(s):  
Simulated Annealing ◽  
Optimal Design ◽  
Objective Function ◽  
Design Problem ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Simulated Annealing Method ◽  
Direct Interpretation ◽  
Annealing Method

In this paper a design methodology of a magnetohydrodynamic pump is proposed. The methodology is based on direct interpretation of the design problem as an optimization problem. The simulated annealing method is used for an optimal design of a DC MHD pump. The optimization procedure uses an objective function which can be the minimum of the mass. The constraints are both of geometrics and electromagnetic in type. The obtained results are reported.

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