Discrete-Continuous Genetic Algorithm for Designing a Mixed Refrigerant Cryogenic Process

Ebrahimi, Akram; Tamnanloo, Javad; Mousavi, Seyed Hesam; Miandoab, Ehsan Soroodan; Hosseini, Elaheh; Ghasemi, Homa; Mozaffari, Saeed

doi:10.1021/acs.iecr.1c01191

Cited by 47 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ebrahimi et al optimized the energy-efficiency of a PRICO cycle considering up to 17 components as candidates in the composition of the mixed refrigerant. Their work uses a tailored genetic algorithm that initially explores the solution space considering only a predefined list of values of each variable.…”

Section: Previous Researchmentioning

confidence: 99%

Development of an Energy-Efficient Single Mixed Refrigerant Cycle for Small-Scale LNG Production

Trasviña

Smith

Jobson

2021

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Energy-intensive single mixed refrigerant (SMR) cycles are employed to produce liquefied natural gas (LNG) at small scale. The energy required for refrigerant compression (shaft work) dominates the overall operating costs of the LNG plant. While current methods to minimize shaft work demand (e.g., exergy analysis) focus on the PRICO cycle, this paper shows that structurally modifying the configuration of SMR cycles can yield significant savings in shaft work demand with low added complexity. The novel SMR cycle developed in this work is based on the CryoMan SMR cycle; its benefits, in terms of energy savings, are demonstrated through a case study for natural gas liquefaction at small scale. The shaft work demand of the PRICO, CryoMan, and novel SMR cycles are minimized using a genetic algorithm and nonlinear optimization. The structural modifications applied tailor the refrigerant composition and exploit complex trade-offs between the operating variables to enhance the energy efficiency of the SMR cycles. The results from the case study demonstrate that the novel SMR cycle achieves 10% savings in shaft work demand in comparison with the PRICO SMR cycle.

show abstract

Section: Previous Researchmentioning

confidence: 99%

Development of an Energy-Efficient Single Mixed Refrigerant Cycle for Small-Scale LNG Production

Trasviña

Smith

Jobson

2021

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…For efficient stabilization of the asphaltene in the crude oil, it is important to study the relationship between the pH of the medium, the composition of the nanoparticles in the nanofluid, the salinity, and the organic solvents using machine learning algorithms such as Gaussian Process Regression (GPR). The GPR is a nonparametric machine learning algorithm that employs Bayesian method to regression (Gao et al 2018;Ebrahimi et al 2021). The GPR has the capacity to operate effectively with limited datasets and provide uncertainty assessments on the predictions of the targeted output (Chen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Investigating the effect of TiO2-based nanofluids in the stability of crude oil flow: parametric analysis and Gaussian process regression modeling

Shnain

Mageed

Majdi³

et al. 2022

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Study has shown that the precipitation of asphaltenes could be the most detrimental mechanism that significantly influences well productivity during crude oil processing. The flow of the crude oil could seriously be affected if the effect of the asphaltenes is not inhibited. This study aims to investigate the effect of TiO2-based nanofluid in stabilizing crude oil flow. The effect of the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid, the salinity of the nanofluid, and pH on the amount of organic solvent (n-heptane) added to stabilize the crude oil flow was investigated using Gaussian Process Progression (GPR) with five kernel functions [exponential square kernel (model 1), rotational quadratic (model 2), Matern 5/2 (model 3), exponential (model 4), and non-isotopic rotational quadratic (model 5)]. The GPR using the various kernel function had good modeling of the relationship between the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid, the salinity of the nanofluid, the pH, and the amount of organic solvent (n-heptane) added to stabilize the crude oil flow. This is evidence from the R2 of 0.820, 0.999, 0.999, 0.999, 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively. Each of the models had low prediction errors as indicated by the MSE, RMSE, and MAE. Based on the sensitivity analysis, the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid had the most significant influence on the amount of n-heptane added to stabilize the crude oil.

show abstract

“…They used the homotopy analysis method to calculate the governing equation and Mathematica software (Wolfram Mathematica, New Jersey, NJ, United State) to provide a graphical illustration of the velocity gradient, temperature, and concentration gradient of the Casson model. Although research on the Casson model is ongoing, we include some related studies based on heat transfer [25][26][27][28][29], fractional models of fluids with magnetic field [30][31][32][33][34][35][36][37][38][39][40], and some others [41][42][43][44][45][46][47][48][49][50][51][52][53][54] herein. Motivated by the above discussion, we analyzed an analytic solution of incompressible and magnetic Casson fluid subjected to temperature and concentration dependence within a porous-surfaced plate.…”

Section: Introductionmentioning

confidence: 99%

Exact Symmetric Solutions of MHD Casson Fluid Using Chemically Reactive Flow with Generalized Boundary Conditions

et al. 2021

View full text Add to dashboard Cite

Dynamic analysis of magnetic fluids with the combined effect of heat sink and chemical reactions based on their physical properties demonstrates strong shock resistance capabilities, low-frequency response, low energy consumption, and high sensitivity. Therefore, the applied magnetic field always takes diamagnetic, ferromagnetic, and paramagnetic forms. The influence of radiation is considered in the temperature profile. This manuscript investigates an analytic solution of incompressible and magnetic Casson fluid in Darcy’s medium subjected to temperature and concentration dependence within a porous-surfaced plate with generalized boundary conditions. The substantial mathematical technique of the Laplace transform with inversion is invoked in the governing equations of the magnetic Casson fluid. The analytic results are transformed into a special function for the plate with a constant velocity, a plate with linear velocity, a plate with exponential velocity, and a plate with sinusoidal velocity. Graphical illustrations of the investigated analytic solutions at four different times are presented. Our results suggest that the velocity profile decreases by increasing the value of the magnetic field, which reflects the control of resistive force. The Nusselt number remains constant at a fixed Rd and is reduced by raising the Rd value.

show abstract

Discrete-Continuous Genetic Algorithm for Designing a Mixed Refrigerant Cryogenic Process

Cited by 47 publications

References 38 publications

Development of an Energy-Efficient Single Mixed Refrigerant Cycle for Small-Scale LNG Production

Development of an Energy-Efficient Single Mixed Refrigerant Cycle for Small-Scale LNG Production

Investigating the effect of TiO2-based nanofluids in the stability of crude oil flow: parametric analysis and Gaussian process regression modeling

Exact Symmetric Solutions of MHD Casson Fluid Using Chemically Reactive Flow with Generalized Boundary Conditions

Contact Info

Product

Resources

About