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Ultrasound supported synthesis of waste mangifera indica linn biodiesel: an optimization using whale algorithm

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 1695-1709页

作者： Arumugam, S. Peddamangari Venkatesulu Reddy, Chengareddy Arulvalavan, Tamilarasan Gopalasamy, Sriram Sri Chandrasekharendra Saraswathi Viswa Mahavidyal Dept Mech Engn Kanchipuram 631561 Tamil Nadu India

The synthesis of biodiesel from vegetable oil is not the right choice as its feedstock and production cost is expensive than diesel. To address this, the study reports the implementation of whale algorithm (WA) for the optimization of biodiesel synthesis from cost-effective waste feedstock, i.e. waste mango seed kernel oil (WMS) using ultrasound-supported methanolysis process. The influence of methanolysis process variables, i.e. methanol:WMS molar ratio (12:1-20:1), sonication time (10-30 min), heterogeneous catalyst concentration (1-3 wt.%), and ultrasound amplitude (25-75%) on the yield percentage of WMS methyl ester were examined and optimized. The WA model is used to augment the yield percentage upto 90.88% at optimum methanol:WMS molar ratio of 16.32:1, the sonication time of 19.75 min, the quantity of heterogeneous catalyst of 2.28 wt.% and an ultrasound amplitude of 54.31%. 1H-NMR spectral approach was adopted to ensure the formation of WMS methyl ester. The combined use of heterogeneous catalyst along with ultrasonication significantly improved the conversion percentage of WMS methyl ester to 90.88% over the conventional transesterification process yield of 83%. The outcome of this study confirmed that the optimization by WA showed marginal improvement in yield percentage of WMS methyl ester as compared to those obtained using response surface methodology (RSM), a commonly applied optimization method.

关键词： Biodiesel waste feedstock waste mango seed kernel oil whale algorithm ultrasound energy

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Microgrid Fault Diagnosis Based on whale algorithm Optimizing Extreme Learning Machine

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第3期19卷 1827-1836页

作者： Wu, Zhongqiang Lu, Xueqin Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

A microgrid fault diagnosis method based on whale algorithm optimizing extreme learning machine (ELM) is proposed. Firstly, the three-phase fault voltage is analyzed by wavelet packet decomposition, and the feature vector composed of wavelet packet energy entropy is calculated as data samples. Then, a whale algorithm is used to optimize the extreme learning machine to establish a diagnostic model to identify and diagnose the fault type of microgrid. The whale algorithm has the characteristics of simple parameter setting, fast learning speed, and strong global optimization ability. The whale algorithm is used to optimize the input weights and hidden layer neuron thresholds of the extreme learning machine, which solves the problem that the random initialization of the input weights and hidden layer neuron thresholds easily affects the network performance, which can further improve the learning speed and generalization ability of the network, and benefit to global optimization. Simulation results show that compared with BP neural network, RBF neural network and ELM, the fault diagnosis model based on whale algorithm optimization extreme learning machine has faster learning speed, stronger generalization ability and higher recognition accuracy.

关键词： Microgrid Wavelet packet decomposition Extreme learning machine whale algorithm Fault diagnosis

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Oil well productivity capacity prediction based on support vector machine optimized by improved whale algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第12期14卷 3251-3260页

作者： Ma, Kuiqian Wu, Chunxin Huang, Yige Mu, Pengfei Shi, Peng CNOOC China Ltd Tianjin Bohai Branch Tianjin 300452 Peoples R China

Oil well productivity capacity is an important parameter in oilfield development, which is of great significance for efficient development. Traditional oil well productivity capacity prediction methods have a series of problems, such as limited application scope, large prediction errors, difficulty in characterizing changes under the influence of multiple factors. Aiming at these problems, a well productivity prediction method based on machine learning algorithm was proposed. Taking Bohai X oilfield as the research object, 12 factors affecting oil well productivity capacity were selected from three aspects: geology, engineering, and production. The degree of each factor influence on oil well productivity capacity was analyzed by using the mean decrease impurity (MDI) method, the feature parameters were sequentially excluded, and redundant features that do not affect the prediction accuracy of the model were removed. And then support vector machine (SVM) optimized by improved whale optimization algorithm (IWOA) was used to establish prediction model for oil well productivity capacity. The results show that the main control factors of oil well productivity capacity are: permeability, porosity, effective thickness, pressure draw-down, perforation thickness, fracturing sand addition amount, resistivity, oil saturation, sand addition strength and shale content. The model based on SVM optimized by the improved whale algorithm have an average error of 9.3%, while the model based on SVM optimized by grid search and whale algorithm have bigger errors, which are 21.7% and 15.7% respectively. Residual sum of squares (R2) values for SVM optimized by grid search optimization, whale algorithm and improved whale algorithm are 0.372, 0.939 and 0.941 respectively. The model based on SVM optimized by the improved whale algorithm has higher accuracy in predicting oil well productivity capacity. Compared with existing literature, the MDI method was used to optimize the factors affectin

关键词： whale algorithm Support vector machine Machine learning Productivity capacity prediction Feature selection

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Optimal control of high-speed train speed tracking based on improved whale algorithm 5

Optimal control of high-speed train speed tracking based on ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Feng, Qingsheng Ge, Xiangdong Cong, Guanglin Dalian Jiaotong Univ Sch Automat & Elect Engn Dalian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Aiming at the high-speed train operation tracking control problem, a sliding mode control algorithm based on the improved whale algorithm of RBF neural network is proposed. Firstly, we establish a single mass point model of the train and control it by integral sliding mode;meanwhile, we introduce the adaptive algorithm of RBF neural network to weaken the influence of external interference and prove the feasibility of the controller by Lyapunov stability criterion;finally, in order to strengthen the global searching and local optimization ability of the whale algorithm, we carry out the nonlinear processing of the convergence factor of the whale algorithm, and optimize the parameters of the controller through the optimization strategy of the improved whale algorithm. Optimization. The simulation results show that the speed error is within the range of [-0.7 x 10(-3), 3.8 x 10(-3)] and the displacement error is within the range of [-3.5 x 10(-4), 5.2 x 10(-4)], and the algorithm can realize the accurate tracking of train speed and displacement.

关键词： high-speed train RBF neural network whale algorithm sliding mode control speed tracking

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Optimization Study of Microgrid Energy Storage System Based on Particle Swarm algorithm and whale algorithm 4

Optimization Study of Microgrid Energy Storage System Based ...

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4th IEEE International Conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Yifu Yang, Yiling Northeast Electric Power University Jilin China Chongqing Jiaotong University Chongqing China

ISBN: (纸本)9798350368239

In recent years, the development and utilization of renewable energy has become an important way to solve the global energy crisis and environmental pollution problems. This paper investigates the configuration of microgrids in parks, aiming to improve the utilization efficiency of renewable energy and the economy of power supply. Firstly, this paper analyzes in detail the key factors of the economic efficiency of the coordinated and optimized configuration scheme of wind and solar energy storage under the two modes of independent and joint operation of different park microgrids. Secondly, on the basis of the analyzed results, the optimal energy storage allocation scheme formulation model is constructed using particle swarm algorithm and whale algorithm respectively. Finally, the model is brought into different configuration scheme solving, and the result visualization of the final results proves the superiority of the model. ©2024 IEEE.

关键词： Economic Analysis Energy Storage System Park Microgrids Particle Swarm algorithm whale algorithm

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Proposing Models for Estimating the Compressive Strength of HSC and UHSC FRP-Confined Circular Concrete by Using whale algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2022年第1期46卷 67-76页

作者： Ghasemi, Hossien Sohrabi, Mohammad Reza Moodi, Yaser Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

Confining is a very common method of reinforcing concrete columns, and numerous experiments have been performed on different-strength RC columns confined with FRP sheets. Since models proposed to determine the compressive strength of normal-strength (less than 50 MPa) concretes are many, but those of higher strength ones (greater than 50 MPa) are few, this study has collected a large set of laboratory data from different FRP-confined circular specimens with strengths greater than 50 MPa and has used the data to present modified models to determine their compressive strength. The FRP strain efficiency is defined as the ratio of the actual FRP hoop rupture strain in confined columns to that obtained from the coupon test. These models integrate the FRP strain efficiency and strength increase factors and consider the integration as a function of: (1) strain ratio, (2) confinement stiffness ratio, and (3) the combination of the latter two. These functions' constant coefficients were so calculated by the whale optimization algorithm as to minimize the difference between the models and experimental results. A comparison of the results showed that the proposed models in this study perform better than the models of previous studies for estimating compressive strength of FRP-confined circular specimens with compressive strengths greater than 50 MPa. Also, among the models presented in this study, the model that used the integrated factor as a combinatory function estimated the columns' compressive strength better so that the correlation coefficient (R-2) of this model was 89%.

关键词： High-strength concrete Confining Fiber-reinforced polymers Compressive strength whale algorithm Circular section

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Improved whale algorithm and its application in cobot excitation trajectory optimization

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022年第4期6卷 615-624页

作者： Zhao, Yuntao Chen, Jun Li, Weigang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China

The improved whale algorithm is proposed to design the excitation trajectory in the parameter identification of collaborative robots. Firstly, the whale algorithm is improved, the initial population is optimized by the method of uniform distribution, the adaptive weight is introduced to enhance the optimization ability of the algorithm, and the optimization speed of the algorithm is improved by using the convergence factor. Then establish the dynamic model of the robotic arm, design the excitation trajectory of the collaborative robot, and use the improved whale algorithm to optimize the excitation trajectory. Finally, the HM06 robot is used as the experimental object to run the excitation trajectory optimized by the improved whale algorithm. The experimental results show that the excitation trajectory optimized by the improved whale algorithm is smooth and has strong anti-noise ability, which is beneficial to improve the accuracy of robot parameter identification.

关键词： Dynamic model Parameter identification Excitation trajectory whale algorithm

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Where to Install the Manipulator: Optimal Installation Pose Planning Based on whale algorithm

Where to Install the Manipulator: Optimal Installation Pose ...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Qin, Xinyu Zhang, Heng Zhou, Tieliang Xiong, Zhenhua Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Huadian Heavy Ind CO LTD Shanghai Peoples R China

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

In industrial applications, a manipulator is usually required to perform multiple pre-defined tasks on a fixed installed pose. Due to the ununiform dexterity distribution, the manipulator may work in awkward configurations, and some tasks may even exceed the reachable workspace. Thus, the performance of the tasks can be improved by optimizing the installation pose (IP) of the manipulator. However, there are complex nonlinear relationships between the IP and the tasks, which makes the optimization problem challenging. In this paper, a new method is proposed to optimize the IP of the manipulator for multiple tasks. Firstly, the paths of the end effector are discretized into poses for different tasks. Then the optimization model is established, which is applicable to different indexes and constraints. Finally, the whale algorithm is improved to solve the optimization model. To verify the effectiveness of the proposed method, two case studies of the robotic inspection task and the unhooking the freight train task are given. Simulation results show that the proposed method can give good performance for complex tasks.

关键词： Manipulator Installation pose Path discretization whale algorithm

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Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第2期28卷 1596-1611页

作者： Ehteram, Mohammad Ahmed, Ali Najah Latif, Sarmad Dashti Huang, Yuk Feng Alizamir, Meysam Kisi, Ozgur Mert, Cihan El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Univ Tenaga Nasl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang 43000 Selangor Malaysia Islamic Azad Univ Dept Civil Engn Hamedan Branch Hamadan Hamadan Iran Ilia State Univ Dept Civil Engn Tbilisi Georgia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Int Black Sea Univ Fac Comp Technol & Engn Tbilisi Georgia Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ UAEU Natl Water Ctr Al Ain 15551 U Arab Emirates

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of theartificial neural network(ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highestR(2)when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

关键词： Soft computing model whale algorithm ANN Suspended sediment load Optimisation Iran

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Software fault prediction using whale algorithm with genetics algorithm

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第5期51卷 1121-1146页

作者： Alsghaier, Hiba Akour, Mohammed Yarmouk Univ Dept Informat Syst Irbid Jordan

Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12-NASA MDP and 12-Java open-source projects), where NASA MDP is considered as a large-scale dataset, and Java open source projects are considered as a small-scale dataset. Results indicate that integrating Genetics algorithm with SVM and whale algorithm improves the performance of the software fault prediction process when it is applied to large-scale and small-scale datasets and overcome the limitations that appeared in the previous studies.

关键词： fault prediction genetics algorithm machine learning optimization algorithms whale algorithm

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