Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Miriyala, Srinivas Soumitri; Mittal, Prateek; Majumdar, Saptarshi; Mitra, Kishalay

doi:10.1016/j.ces.2015.09.030

Cited by 83 publications

(34 citation statements)

References 31 publications

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“…This method is believed that to be more cost-effective 47 and superior to represent nonlinear relationships. 48,49…”

Section: Resultsmentioning

confidence: 99%

“…This method is believed that to be more cost-effective 47 and superior to represent nonlinear relationships. 48,49 Figures 5-7 illustrate the three-dimensional (3D) graph showing the influence of the process parameters (v c , f z , a p , and H) on the response parameters (R a , P c , and T c ) when the Kriging model were applied. These 3D graphs are the visual tool that are used to observe the relationship between inputs and outputs as well as to analyze the trend of outputs.…”

Section: Resultsmentioning

confidence: 99%

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Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

Dang

Huang

2020

Measurement and Control

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This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.

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“…This method is believed that to be more cost-effective 47 and superior to represent nonlinear relationships. 48,49…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

Dang

Huang

2020

Measurement and Control

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“…Furthermore, over recent decades, novel modeling techniques have been developed which can substantially aid the optimization of process systems. For instance, surrogate models such as Kriging [39][40][41][42][43][44], radial basis functions [45][46][47][48][49][50], artificial neural networks [51][52][53][54][55][56], splines [57,58], among others were shown to accurately represent complex physical systems while aiding optimal search algorithms. No literature exists which explores the application of such techniques to advance the study of CHP dispatch.…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.

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“…Though when compared with SRM models or conventional polynomials these regression model has found to be better than that. Kriging model developed in geostatic in South Africa is also being used to define the relationship between different process parameters and machining responses in EDM, and it has been found to be better than both SRM and ANN when highly nonlinear characteristic and efficiency in cost [15] of experimentation is considered though Kringing model may require more points to capture non-linear behaviour [16,17]. When optimizing the process parameters, it helps an EDM operator to get the most optimum machining outputs for a required purpose.…”

Section: Literature Surveymentioning

confidence: 99%

Optimization of Machine Process Parameters in EDM for EN 31 Using Evolutionary Optimization Techniques

Faisal

Kumar

2018

Technologies

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Electrical discharge machining (EDM) is a non-conventional machining process that is used for machining of hard-to-machine materials, components in which length to diameter ratio is very high or products with a very complicated shape. The process is commonly used in automobile, chemical, aerospace, biomedical, and tool and die industries. It is very important to select optimum values of input process parameters to maximize the machining performance. In this paper, an attempt has been made to carry out multi-objective optimization of the material removal rate (MRR) and roughness parameter (Ra) for the EDM process of EN31 on a CNC EDM machine using copper electrode through evolutionary optimization techniques like particle swarm optimization (PSO) technique and biogeography based optimization (BBO) technique. The input parameter considered for the optimization are Pulse Current (A), Pulse on time (µs), Pulse off time (µs), and Gap Voltage (V). PSO and BBO techniques were used to obtain maximum MRR and minimize the Ra. It was found that MRR and SR increased linearly when discharge current was in mid-range however non-linear increment of MRR and Ra was found when current was too small or too large. Scanning Electron Microscope (SEM) images also indicated a decreased Ra. In addition, obtained optimized values were validated for testing the significance of the PSO and BBO technique and a very small error value of MRR and Ra was found. BBO outperformed PSO in every aspect like computational time, less percentage error, and better optimized values.

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Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Cited by 83 publications

References 31 publications

Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Optimization of Machine Process Parameters in EDM for EN 31 Using Evolutionary Optimization Techniques

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