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Thermal-hydraulic characteristics and optimization of coiled-tube resistance furnace based on response surface method and multi-objective genetic algorithms

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CASE STUDIES IN THERMAL ENGINEERING 2025年 72卷

作者： Zhang, Ruochen Wu, Yinglong Xiao, Juan Wang, Simin Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China

The electrification transformation of the petrochemical industry is imminent, driven by the imperative to reduce carbon emissions. This paper presents a coiled-tube resistance furnace and investigates the flow and heat transfer performance of heavy residual oil within the tube through numerical simulation methods. The furnace's coiled-tube structure was optimized using a multi-objective genetic algorithm (MOGA) combined with a genetic aggregation response surface model, with inlet velocity, coil diameter, pitch, and tube diameter as design variables. The numerical model was experimentally validated and demonstrated the reliability of the simulation approach. Sensitivity analysis identified inlet velocity and coil diameter as the most influential parameters. Consequently, the optimal structural parameters were determined as a coil diameter of 200 mm, a pitch of 60 mm, and a tube diameter of 30 mm. Compared to the original structure, the optimized design led to a 63.7 % increase in the average heat transfer coefficient and a 44.6 % reduction in the average pressure drop. Additionally, a new correlation formula was developed by incorporating the Dean number, coil curvature, and dimensionless pitch as correction factors. The formula shows excellent fitting accuracy and robustness, providing a theoretical basis for the design of coiled tube resistance furnaces.

关键词： Coiled-tube Resistance furnace Heat transfer enhancement multi-objective genetic algorithm Fitting correlations

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Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by multi-objective genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

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SENSORS 2024年第2期24卷 608页

作者： Del Priore, Emiliano Lampani, Luca Sapienza Univ Roma Dipartimento Ingn Meccan & Aerosp I-00184 Rome Italy

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

关键词： structural health monitoring shape sensing sensor placement inverse finite element method smoothing element analysis multi-objective genetic algorithm

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A genetic algorithm based approach to solve multi-resource multi-objective knapsack problem for vegetable wholesalers in fuzzy environment

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OPERATIONAL RESEARCH 2020年第3期20卷 1321-1352页

作者： Changdar, Chiranjit Pal, Rajat Kumar Mahapatra, Ghanshaym Singha Khan, Abhinandan Univ Calcutta Dept Comp Sci & Engn Kolkata 700106 W Bengal India Natl Inst Technol Dept Math Karaikal 609605 Puducherry India Raja NL Khan Womens Coll Midnapore 721102 W Bengal India

Vegetable wholesaling problem has a vital role in the business system. In this problem, a vegetable wholesaler is supposed to supply raw, fresh vegetables to supermarkets in an efficient way by minimizing time but maximizing profit. In this paper, we have presented a multi-resource multi-objective knapsack problem (MRKP) for vegetable wholesalers. This model is beneficial for a vegetable wholesaler who collects different types of vegetables (objects) from different villages (resources) to a market for selling the vegetables (objects). MRKP is an extension of the classical concept of 0-1 multi-dimensional knapsack problem (KP). In this model, precisely a wholesaler has a limited capacity van/trolley by which he/she collects a set of vegetables from different villages/vegetable fields. In this model, we have assumed that all the vegetables are available for each resource. Each type of vegetable is associated with a weight, a corresponding profit, and a collection time (for a particular resource). The profit, weight, and collection time of objects is different for different resources. Here a time slice is considered for each object to collect it from different villages to a particular destination/market. Also, the profit and time are the two objectives. MRKP aims to find the amount of an object and the corresponding resource name from which it is collected. We have solved the proposed problem in the fuzzy environment. In this paper, we have explained two defuzzification techniques, namely fuzzy expectation and total lambda-integral value method to solve the proposed problem. We have explained a modified multi-objective genetic algorithm (NSGA-II by Deb et al. in IEEE Trans Evol Comput 6(2):192-197, 2002) that is to maximise the profit and minimise the time to collect the objects. We have considered a multi-objective benchmark test function to show the effectiveness of the proposed genetic algorithm. Modification is made by introducing refinement operation. An extensive

关键词： Fuzzy number multi-resource knapsack problem multi-objective genetic algorithm Refinement Vegetable wholesaling

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Machine learning-based predictive modeling and optimization: Artificial neural network-genetic algorithm vs. response surface methodology for black soldier fly (Hermetia illucens) farm waste fermentation

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BIOCHEMICAL ENGINEERING JOURNAL 2025年 218卷

作者： Okoro, Oseweuba Valentine Hippolyte, D. E. Caevel Nie, Lei Karimi, Keikhosro Denayer, Joeri F. M. Shavandi, Armin Univ Libre Bruxelles ULB Ecole Polytech Bruxelles BioMatter Unit Ave FD Roosevelt 50CP 165-61 B-1050 Brussels Belgium Xinyang Normal Univ Coll Life Sci Xinyang 464000 Peoples R China Vrije Univ Brussel Dept Chem Engn B-1050 Brussels Belgium

Recognizing the complexity of non-linear and interdependent biological processes, this study compared the predictive performance of artificial neural network (ANN) models with response surface methodology regression based (RB) models. The research focused on the biological transformation of black soldier fly (Hermetia illucens) farm waste into chitin, facilitated by Lactobacillus paracasei. Key parameters of time (1-7 days), temperature (30-40 degrees C), substrate concentration (7.5-20 wt%), and inoculum concentration (5-15 v/v%), were evaluated for their impact on demineralization and deproteinization subprocesses and subsequently optimized. It was determined that the ANN models outperformed RB models, with R2 values of 0.950 and 0.959 for DP% and DD%, compared to 0.677 and 0.720 for RB models. While both models, optimized using a multi-objective genetic algorithm (MOGA) and a desirability function respectively, produced comparable optimal results, differences emerged in process variable analysis. Main effects plots (RB) and one way partial dependence plots (ANN) revealed conflicting parameter influences, highlighting the limitations of regression models in complex systems. This study highlights the superiority of ANN-MOGA in addressing biological complexity and recommends its use especially if RB models show suboptimal predictive capabilities.

关键词： Artificial neural network multi-objective genetic algorithm Chitin Black solder fly Response surface methodology Waste valorization

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An innovative design for floating breakwater with multi-objective genetic optimal method

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OCEAN ENGINEERING 2024年 312卷

作者： Mao, Puxiu Chen, Cheng Chen, Xinquan Zhang, Qi Bao, Yan Yang, Qi Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Underwater Engn Inst Co Ltd Shanghai 200231 Peoples R China

The hydro-meteorological conditions in large port terminals are complex, severely hindering the safe berthing and operations of ships;in this case, floating breakwaters are suitable alternatives to traditional fixed facilities because of their advantages such as maneuverability, adaptability, minimal impact on marine environments, and convenient construction and transportation. In this study, a floating breakwater with wing-plate was indicated, and the influence of the structural dimensions on wave attenuation performance is explored based on potential flow theory. Additionally, a parameterized scaling optimization method based on a multi-objective genetic algorithm (MOGA) was introduced and applied to the newly designed floating breakwater. Then a numerical hydrodynamic model was constructed according to the optimization result, and computational fluid dynamics (CFD) simulations were conducted using STAR-CCM+. Finally, wave dissipation experiments were performed and validated the superiority wave attenuation performance of the innovative floating breakwater over the traditional square-box floating breakwater.

关键词： Floating breakwater Wave attenuation performance Parametric optimization multi-objective genetic algorithm Computational fluid dynamics

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Acoustic Emotion Recognition: Two Ways of Features Selection based on Self-Adaptive multi-objective genetic algorithm

Acoustic Emotion Recognition: Two Ways of Features Selection...

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International Conference on Informatics in Control, Automation and Robotics

作者： Christina Brester Maxim Sidorov Eugene Semenkin Institute of Computer Sciences and Telecommunication Siberian State Aerospace University Institute of Communications Engineering University of Ulm

ISBN: (纸本)9781479979202

In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.

关键词： Heuristic Feature Selection multi-objective genetic algorithm Self-Adaptation Probabilistic Neural Network Speech-based Emotion Recognition

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Improving fuzzy clustering model for probability density functions using the two-objective genetic algorithm

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multiMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 45291页

作者： Phamtoan, Dinh Vovan, Tai Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This paper proposes a fuzzy clustering model for probability density functions (PDFs) using the two-objective genetic algorithm. In this model, the L-1-distance is used to evaluate the similarity of PDFs, and new two indexes that relate to the similarity of PDFs and clusters are proposed as the objective functions of genetic algorithm. Moreover, the operators for crossover, mutation, and selection are also updated to improve the quality of fuzzy clustering according to the corrected rand, the partition entropy, and the partition coefficients. By combining these improvements, we have an effective automatic fuzzy clustering algorithm for PDFs that can determine the appropriate number of clusters, the elements in each cluster, and the probability belonging to clusters of each element. The proposed model is tested through experiments using the established Matlab procedure, and it is also applied effectively to image data. These experiments demonstrate the superiority of the proposed model compared to other models.

关键词： Fuzzy clustering genetic algorithm Image cluster multi-objective genetic algorithm

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multi-parameter optimization of multiple vapor-cooled shields integrated with para-to-ortho catalytic conversion based on self-pressurization model of liquid hydrogen tank and MOGA

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APPLIED THERMAL ENGINEERING 2025年 263卷

作者： Li, Ke Wen, Jian Xin, Biping Zhu, Shun Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

The temperature difference between the inside and the outside of liquid hydrogen storage tank is very large, so VMLI (vacuum multi-layer insulation) coupled with VCS (vapor cooled-shield) is generally introduced to shield external heat leakage. Further, VCS is filled with catalyst for hydrogen para-to-ortho catalytic conversion (POC), which can release cold energy to shield the external heat leakage. Based on the self-pressurization model of tank constructed in the research, it is found that the mass flowrate in VCS m (center dot) vcs , the opening moment of VCS t open , the duration time of VCS t d , and VCS DP(dimensionless position) have obvious effect on the total mass of gas hydrogen consumed by VCS and the dormancy of tank, which are respectively described by two dimensionless factors 7c and 7d. It is hoped that the tank dormancy can be extended with as little hydrogen consumed by VCS as possible, by choosing appropriate (m(center dot)vcs, t open , t d , VCS DP). Especially for TVCS, six parameters need to be determined, including (m(center dot)vcs, t open , t d ) and DP of the three VCSs. Therefore MOGA (multi-objective genetic algorithm) is innovatively introduced to help search for the optimized (m(center dot)vcs, t open , t d , VCS DP) in high-dimensional input parameter space, which can make 7c and 7d Pareto-optimal, and the corresponding (7c, 7d) form the Pareto front. The Pareto front can be fitted as a straight line through the origin. The slope parameter of the line determines the theoretical limit of 7d when 7c is fixed. It means that, when the total mass of gas hydrogen consumed by VCS is fixed, the dormancy has a theoretical limit. When the TVCS-POC (triple vapor-cooled shield integrated with hydrogen para-to-ortho catalytic conversion) is adopted, (7c, 7d) calculated according to the optimized (m(center dot)vcs, t open , t d , VCS DP)(the three optimized points in high-dimensional input parameter space) are compared with that calculated according

关键词： Liquid hydrogen tank Self-pressurization Triple vapor-cooled shield Dormancy multi-objective genetic algorithm Pareto-optimal

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Designing a fuzzy sliding mode controller for a three-phase inverter via genetic algorithm

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第5期11卷 2452-2465页

作者： Taran, Babak Mohajeri, Hamed Tarbiat Modares Univ Dept Mech Engn Jalal AleAhmad Highway POB 14115-111 Tehran Iran Univ Zanjan Dept Elect Zanjan Iran

In a three-phase inverter, if the electricity consumers' loads are not equal, balance will be disturbed and switching will be challenged. To overcome this problem, herein, a fuzzy sliding mode control is proposed to control the output voltages of a three-phase inverter. Control methods for commanding IGBTs have advantages over methods such as PWM and SVM as the effect of errors and uncertainties is neutralized by measuring the output error. Sliding mode control has long been used to control systems with uncertainty in modeling. This controller is robust because they provide sliding conditions by generating discontinuous control signals around the sliding surface. Despite these advantages, sliding mode control has its drawbacks. A popular method to solve these problems is fuzzy control, which promotes controller performance. In addition to paying attention to reducing the output error in inverters, changes in the state of switches should also be considered. In practice, the power loss increases and the inverter efficiency decreases, with increasing the disconnection and reconnection of IGBTs. In the present study, a multi-objective genetic algorithm is used so as to select the optimal control coefficients, in such a way that a compromise is made between the error and the number of times the keys are connected and disconnected.

关键词： Three-phase inverter Sliding mode control Fuzzy control multi-objective genetic algorithm

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multi-objective optimization of sandwich structures for reinforcing composite fuselages

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JOURNAL OF SANDWICH STRUCTURES & MATERIALS 2024年第5期26卷 567-585页

作者： Albanesi, Alejandro Eduardo Volpe, Nahuel Jose Langhi, Federico Fachinotti, Victor Daniel CONICET Santa Fe CIMEC Ctr Invest Metodos Computac Predio CONICET Santa Fe Santa Fe Argentina CONICET Santa Fe CIMEC Ctr Invest Metodos Computac Col Ruta 168 s-n RA-3000 Santa Fe Argentina

This study introduces a simulation-based optimization approach that combines a multi-objective genetic algorithm and the finite element method to design sandwich structures to reinforce composite fuselages. The sandwich structure has been parametrized with a set of mixed integer-continuous variables representing the polymer core thickness, the number and weight of laminated fiberglass layers, and size and position of the structure. Automatic scripting builds the geometry of the structure, then mounts it in the fuselage, and finally creates a conformal mesh. During the optimization, each reinforced fuselage is subjected to several load cases specified by the CS-22 EASA design standard, determining the worst load condition. Two objectives were considered: increasing the reinforced fuselage's minimum fiber stress safety factor for improved safety and reducing the number of layers of the sandwich structure to minimize manufacturing costs. Structural constraints were the mass of the sandwich structure and buckling and flexo-torsional deformation of the reinforced fuselage. Results show an efficient material arrangement due to the reduced number of layers in the sandwich structure and a stress safety factor surpassing that set by the standard. Finally, the reinforcement of a glider fuselage to incorporate a retractable electric propulsion system is presented as a real-world industrial application.

关键词： Sandwich structures multi-objective genetic algorithm optimization finite element method composite fuselage reinforced fuselage

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