Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization

Santos, Lucas F.; Costa, Caliane Bastos Borba; Ravagnani, Mauro A.S.S.

doi:10.1016/j.apenergy.2022.118537

Cited by 19 publications

(21 citation statements)

References 52 publications

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“…This approach could find applications in refrigeration cycles, 87 natural gas liquefaction, 91 supply chain inventory control, 92 carbon capture, 93 process synthesis, 94 pharmaceutical processes, 95 semibatch bioprocesses, 27 and biorefineries, 96 to mention a few in chemical engineering and beyond. On the other hand, the applications are not limited to technology benchmarking, as discussed below.…”

Section: Analytical Application Of the Expressionsmentioning

confidence: 99%

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

et al. 2022

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Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO 2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO 2 capture process against the business as usual technology.

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Section: Analytical Application Of the Expressionsmentioning

confidence: 99%

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

et al. 2022

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“…The process constraint is that a minimum temperature approach of 3 °C must be assured throughout the MSHEs. To overcome the issue of this constraint not being smooth for kriging modelling (Santos, 2021b), Santos et al (2021a) proposed to discretize the MSHEs 1 and 2 in . The optimal process design can be formulated as the following multiobjective optimization problem…”

Section: Natural Gas Liquefaction Processmentioning

confidence: 99%

“…( 1) is the work consumption of the pressure manipulator unit in the set of compressors and pumps , and is the global heat transfer coefficient multiplied by the area of the heat exchanger ex in the set of all heat exchangers HE. and are the temperature of hot and cold composite curves in the κ section of the MSHEs, and Ωκ is the set of the points from composite curves calculations that belongs to section κ (Santos et al, 2021a).…”

Section: Natural Gas Liquefaction Processmentioning

confidence: 99%

“…Previous works on multi-objective optimization of natural gas liquefaction processes relied on meta-heuristics, mainly GAs. However, recent papers have shown that surrogate-based approaches can be more efficient than meta-heuristics to these simulation optimization problems (Santos et al, 2021a), which can lead to further improvement to the LNG processes. The objective of the present paper is to propose a multi-objective optimization framework to the SMR natural gas liquefaction process design based on kriging surrogate models that replace the process-simulator-dependent, black-box objectives and constraints functions and introduce explicit algebraic formulation to the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of natural gas liquefaction process simulation via kriging surrogate model

Santos

Costa

Caballero

et al. 2022

Computer Aided Chemical Engineering

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“…Tapia et al built a surrogate model by applying Gaussian processes regression (GPR) to predict the melt pool depth of the laser powder bed fusion process [14]. Numerous methods have been applied in other studies to construct surrogate models; these methods include support vector regression (SVR) [15][16][17][18], the response surface methodology [19][20][21], kriging [22][23][24][25], and the adaptive neuro fuzzy inference system (ANFIS) [26].…”

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confidence: 99%

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

Shiu

Liu

2022

Int J Adv Manuf Technol

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In this study, the clamping stress and force involved in the centering of optical glass lens were evaluated and quantified. On the basis of the key design parameters of the examined clamps, the finite element method was applied to measure clamping stress under various parameter combinations. Support vector regression, Gaussian process regression, and adaptive neuro fuzzy inference system algorithm of surrogate models were established using the results obtained through finite element simulation. These surrogate models, which can predict clamping stress on the basis of key parameters, can reduce the time required to perform finite element analysis while providing references for optimizing clamp configuration.Keywords：Centering process, Surrogate model, Finite element analysis, Machine learning IntroductionAn optical axis is defined as the line that connects the centers of the curvature of the curved surfaces on both sides of a lens. If the optical axis deviates from the lens' geometric center axis, the imaging position of the lens will be affected and cause aberration. Centering is a key procedure in optical glass lens manufacturing because it is required to minimize the aforementioned phenomenon [1-3]. During centering, a lens is secured by a pair of bell-shaped clamps that come into contact with both of the polished surfaces of the lens. The forces between the clamps and the lens are radially balanced. Under this condition, the optical axis coincides with the geometric center line. The lens is then grounded by a grinding wheel, such that its shape becomes perfectly symmetrical with respect to the optical axis. To prevent lens clamps from scratching the polished surfaces of lens, they must be made from soft materials.Consequently, lens clamps are prone to deforming under clamping stress, which causes the central axis of a lens to become offset. With unstable clamping, a lens can be easily pushed and decentered by a grinding wheel feed. Therefore, effective methods for evaluating and analyzing clamping stress are required for the centering process.The finite element method (FEM) is widely used to solve engineering problems, including the evaluation of stress distribution [4] and simulation of mechanical behavior [5,6]. FEM-derived results can also be used to conduct pre-machining assessments [7,8] and support parameter optimization [9,10].However, to obtain accurate simulation results, precise engineering modeling and meshing are required.Simulating and analyzing a complex finite element model are usually tasks that require substantial time and computational resources [11,12]. Surrogate models are often applied as substitutes of complex computational models in engineering design; they are established by using simple computational models

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Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization

Cited by 19 publications

References 52 publications

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Multi-objective optimization of natural gas liquefaction process simulation via kriging surrogate model

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

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Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization

Cited by 19 publications

References 52 publications

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies

Multi-objective optimization of natural gas liquefaction process simulation via kriging surrogate model

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

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Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO₂ Capture Technologies