Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer

Mudhsh, Mohammed; El‐Said, Emad M.S.; Aseeri, Ahmad O.; Almodfer, Rolla; Elaziz, Mohamed Abd; Elshamy, Samir M.; Elsheikh, Ammar H.

doi:10.1016/j.csite.2023.103294

Cited by 18 publications

(2 citation statements)

References 41 publications

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“…The FHO procedure can find optimal solutions with relatively small iterations and populations, allowing fast solutions. It happens because FHO utilizes natural algorithms inspired by the behavior of groups of birds of prey [23]. Birds of prey tend to work collaboratively and efficiently in searching for prey, so FHOs can mimic this strategy to achieve convergence of optimal solutions quickly [22].…”

Section: Results Of Fho Algorithmmentioning

confidence: 99%

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Wati,

Amallynda

2023

JTI

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The current energy crisis is a pressing global challenge, with the industrial sector accounting for half of global energy consumption. Scheduling is considered one of the potential methods to reduce energy consumption. This article introduces the Fire Hawk Optimizer (FHO) algorithm to solve the no-idle flow shop scheduling problem to minimize overall energy consumption. FHO organizes the job sequence in no-idle flow shop scheduling for reduce energy consumption. This research investigates the use of different machine speed levels, namely slow, fast, and normal, based on case data of manufacturing industries in Indonesia. The results of this study compare the performance of the FHO algorithm with the Adaptive Integrated Greedy (AIG) heuristic method and compare it with the Grey Wolf Optimizer (GWO) algorithm. The experimental results showed that total energy consumption tends to be high when processed at high speed. Conversely, low-speed results in lower energy consumption but requires longer processing time. The comparison results show that the Fire Hawk Optimizer is more efficient in reducing total energy consumption than the AIG heuristic method. Meanwhile, the FHO algorithm performs comparably to the GWO algorithm and completes enumeration. These findings confirm that the proposed procedure can be an alternative to the scheduling optimization process.

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Section: Results Of Fho Algorithmmentioning

confidence: 99%

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Wati,

Amallynda

2023

JTI

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“…The results indicated that the combination of ANN and the PSO method slightly outperformed the combination of ANFIS and the PSO method. Furthermore, researchers have applied machine learning models in various other heat transfer domains, such as nano-fluid microchannel heat sinks [22], spherical dimples [23] inside tubes, double-pipe heat exchangers [24], and helical plate [25] heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

Multi-Output Regression Algorithm-Based Non-Dominated Sorting Genetic Algorithm II Optimization for L-Shaped Twisted Tape Insertions in Circular Heat Exchange Tubes

Li,

Qian,

Liu

2024

Energies

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In this study, an optimization method using various multi-output regression models as model proxies within the NSGA-II framework was applied to determine the geometric parameters (P, W, D) of L-shaped twisted tape inserts for achieving the optimal overall heat transfer performance in a circular heat exchange tube. Herein, 4 multi-output regression models, namely, MOLR, MOSVR, MOGPR, and BPNN, were selected as proxy models and trained on a dataset containing 74 groups of data. The training results indicated that the MOGPR model, balancing high accuracy and low error conditions, exhibited moderate training times among the four algorithms. BPNN showed a comparatively lower comprehensive training effect, obtaining training accuracy close to that of the MOGPR algorithm but with approximately twice the training time. The worst fitting performance was gained with the MOSVR algorithm. Due to its fitting performance, the MOSVR algorithm was excluded from the subsequent NSGA-II model proxy. Through multi-objective optimization with NSGA-II, the optimal structural dimensions for three sets of L-shaped twisted tape inserts were obtained to achieve the best overall heat transfer efficiency within the tube.

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A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

Kiran,

Raju,

Kumar

2024

Adv Control Appl

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Switched reluctance motors (SRMs) have gained popularity in various industrial applications due to their advantages, including structural simplicity, high reliability, low cost, and operational stability over a wide speed range without relying on rare‐earth permanent magnet materials. However, these motors exhibit drawbacks such as weak torque density, low efficiency, and significant torque ripple, particularly in high‐speed operation. An efficient converter‐based control approach is proposed to manage speed and torque variations in SRM motors, addressing these issues. A multilevel converter (MC) is introduced as a fundamental component. The novel multilevel converter (NMC) accommodates SRMs with varying numbers of phases and exhibits quick demagnetization and excitation behaviors, enabling independent operation of each phase even during conduction overlaps. Subsequently, an effective controller is developed for the SRM motor, combining proportional integral derivative (PID) control with enhanced fire hawks optimization (EFHO). The EFHO optimizes the PID gain values to enhance controller performance. The converter operation minimizes torque ripple and facilitates speed management. The EFHO technique is a fusion of fire hawks optimization (FHO) and the sine cosine algorithm (SCA). This amalgamation improves the updating process of FHO by incorporating SCA. The proposed methodology is implemented in MATLAB and evaluated through various metrics, including SRM motor current, voltage, speed, and torque, under electric vehicle (EV) load conditions. Performance comparisons are conducted with traditional optimization algorithms such as the whale optimization algorithm (WOA) and ant colony optimization (ACO). The results validate the effectiveness of the proposed methodology in achieving improved SRM motor control and performance.

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Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer

Cited by 18 publications

References 41 publications

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Multi-Output Regression Algorithm-Based Non-Dominated Sorting Genetic Algorithm II Optimization for L-Shaped Twisted Tape Insertions in Circular Heat Exchange Tubes

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

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