Determining the heat transfer coefficient during the continuous casting process using stochastic particle swarm optimization

Ruan, Zhaohui; Gao, Xiu-Yan; Yuan, Yuan; Tan, He‐Ping

doi:10.1016/j.csite.2021.101439

Cited by 14 publications

(4 citation statements)

References 26 publications

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“…The cognitive and social parameters are set to c 1 = c 2 = 2. More details of the stochastic PSO omitted here can be found in [48], as the solution method is not the focus of this paper. The free-stream wind speed of each optimisation period is assumed to be constant.…”

Section: Optimisation Resultsmentioning

confidence: 99%

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Deng

Huo

et al. 2023

Energies

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In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the perspective of time-varying wakes. To this end, we first deduce a simplified dynamic wake model according to the momentum conservation theory and backward difference method. The accuracy of the proposed model is verified by simulation comparisons. Then, the time lag of wake propagation and its impact on wind farm production maximisation through wake meandering is analysed. On this basis, a yaw optimisation method for increasing wind farm energy capture is presented. This optimisation method uses the proposed dynamic wake model for wind farm prediction. The results indicate that the optimisation period is critical to the effect of the optimisation method on wind farm energy capture.

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Section: Optimisation Resultsmentioning

confidence: 99%

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Deng

Huo

et al. 2023

Energies

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“…Sun et al 25 use the sequential quadratic programming algorithm as an optimization technique to determine the surface heat flux of the billet. Huang et al 26 estimate the time-varying heat flux on the chip boundary, based on three-dimensional unsteady PDE by using the inverse method; Ruan et al 27 develop a stochastic particle swarm optimization method to determine the heat transfer coefficient in the continuous casting process. Torrijos et al 28 study the inverse heat problem for transient heat conduction equation of the heat flux on the unknown surface of the molten salt loop.…”

Section: Optimization Problem Of Boundary Conditions Of Pdesmentioning

confidence: 99%

Parameterization optimal control of an unsteady partial differential equations with convection term by an improved three-term spectrum conjugate gradient algorithm

Yu,

Wang,

Pang

et al. 2023

Measurement and Control

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The unsteady partial differential equations (UPDE) with convection term gives a clear descriptions for the solidification process of a slab in dynamic production of continuous casting. To give a suitable setting value of secondary cooling water flow rate for the dynamic control system, this study investigates an optimal control problem (OCP) of UPDE with convection term. Firstly, control vector discretization of OCP and the solution of UPDE are given. Secondly, due to the rapidity for gradient, this paper analyzes the expression of the gradient calculation method based on Hamiltonian function costate system by approximate treatment, matrix calculation and composite trapezoidal integral method. Thirdly, an improved three-term spectrum conjugate gradient algorithm (ITSCGA) is proposed to solve the OCP of UPDE, and the global convergence of the ITSCGA is demonstrated. Lastly, the performance of ITSCGA is demonstrated by experimental simulations. The results demonstrate that the ITSCGA provides a smaller temperature fluctuations, and improves the quality of a slab.

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“…Quantum-based PSO has also been applied to task scheduling in device-edge-cloud cooperative computing [26]. Many other variants of the PSO algorithm are available, such as bare-bone PSO [27], stochastic PSO [28], self-adaptive PSO [29], and multi-population PSO [30].…”

Section: Particle Swarm Optimization Variants and Literature Reviewmentioning

confidence: 99%

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Ali,

Abbas,

Mahmood

et al. 2023

Mathematics

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Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level.

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Determining the heat transfer coefficient during the continuous casting process using stochastic particle swarm optimization

Cited by 14 publications

References 26 publications

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Parameterization optimal control of an unsteady partial differential equations with convection term by an improved three-term spectrum conjugate gradient algorithm

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

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