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Optimizing hydrogen yield in sorption-enhanced steam methane reforming: A novel framework integrating chemical reaction model, ensemble learning method, and whale optimization algorithm

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JOURNAL OF THE ENERGY INSTITUTE 2024年 114卷

作者： Wang, Lei Li, Hongwei Du, Changhe Hong, Wenpeng Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China Xian Inst Electromech Informat Technol Xian 710065 Peoples R China

High hydrogen production is essential in sorption-enhanced steam methane reforming (SE-SMR). This study presents an innovative optimization framework to optimize the H2 yield of SE-SMR through a coupled reaction model, prediction surrogate model, and whale optimization algorithm. Firstly, a computational fluid dynamics chemical reaction model of SE-SMR is constructed. A database is established to consider the H2 yield of SE-SMR with temperature, pressure, velocity, and steam to carbon ratio. Then, a surrogate model is developed on the basis of eXtreme Gradient Boosting (XGBoost) to predict the H2 yield under various operating parameters. Finally, the surrogate model is exploited to calculate the fitness function of the whale optimization algorithm to construct an optimization framework for maximizing the H2 yield. The results show that the XGBoost ensemble surrogate model can predict the H2 yield of SE-SMR within 4 s with high accuracy. The optimization framework can optimize the maximum H2 yield and the corresponding operating parameters within 1 min. The optimization results are validated by applying the model and the percentage error of only 0.79 %. Moreover, under optimal operating conditions, methane is almost completely converted to hydrogen, and the dry hydrogen mole fraction could reach 95.96 %. The presented optimization framework can guide the prediction of H2 yield and the optimization of operating parameters in SE-SMR.

关键词： Sorption-enhanced steam methane reforming Machine learning whale optimization algorithm optimization framework Hydrogen yield

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Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm

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AGRICULTURAL WATER MANAGEMENT 2020年 237卷 106145-106145页

作者： Mohammadi, Babak Mehdizadeh, Saeid Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Urmia Univ Water Engn Dept Orumiyeh Iran

In achieving water resource management goals such as irrigation scheduling, an accurate estimate of reference evapotranspiration (ET0) is critical. Support vector regression (SVR) was applied to the modeling of daily ET0 at three meteorological stations in Iran subject to different climates: Isfahan (arid), Urmia (semi-arid), and Yazd (hyper-arid). Different pre-processing approaches [relief (RL), random forests (RF), principal component analysis (PCA), and Pearson's correlation (COR)] served to determine the SVR's optimal input combinations. While these approaches introduced different inputs to the SVR models, those drawn upon by the RF approach (i.e., RF-SVR) generated better results than other approaches. Models performance was evaluated using the root mean square error (RMSE), normalized RMSE (NRMSE), mean absolute error (MAE), coefficient of determination (R-2), and the Nash-Sutcliffe efficiency (E). A novel hybrid model, coupling SVR with a whale optimization algorithm (WOA), was also developed and applied to daily ET0 modeling. The hybrid models outperformed the SVR-only models, with the hybrid RF-SVR-WOA model having the best performance.

关键词： Reference evapotranspiration Support vector regression whale optimization algorithm Hybrid model Pre-processing

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A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 200卷 117012-117012页

作者： Ghobaei-Arani, Mostafa Shahidinejad, Ali Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

The rapid development of Internet of Things (IoT)-based applications and the era of 5G networks has led to an exponential increase in the amount of data required for processing the IoT services. The fog computing paradigm has emerged as a distributed computing solution for serving these applications using available fog nodes near the IoT devices. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) requirements that can be deployed on the fog nodes with different resource capabilities in the fog ecosystem, finding an efficient service placement plan is one of the challenging issues to be considered. In this paper, we propose an efficient IoT service placement solution based on the autonomic methodology for deploying IoT applications on the fog infrastructure. Our proposed solution monitors the QoS requirements of IoT services and capabilities of available fog nodes to determine an efficient service placement plan using the whale optimization algorithm (WOA) meta-heuristic technique. Besides, our evolutionary-based mechanism utilized the throughput and the energy consumption as objective functions for finding desirable IoT service placement plan while meeting the QoS requirements of each IoT service. Also, we develop an autonomous service placement framework according to a three-tier architecture of the fog ecosystem to show the interaction between the main components of the IoT device and fog layers for deploying IoT applications. The simulation results demonstrate that the proposed solution increases the resource usage and service acceptance ratio and reduces the service delay and the energy consumption compared with the other metaheuristic-based mechanisms.

关键词： Fog computing IoT applications Service placement whale optimization algorithm

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Automatic generation controller based on whale optimization algorithm in PV-thermal power systems

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第2期38卷 915-926页

作者： Can, Ozay Eroglu, Hasan Ozturk, Ali Recep Tayyip Erdogan Univ Tech Sci Vocat Sch Dept Elect & Automat TR-53100 Rize Turkiye Recep Tayyip Erdogan Univ Fac Engn & Architecture Dept Elect Elect Engn TR-53100 Rize Turkiye Duzce Univ Fac Engn Dept Elect Elect Engn TR-81620 Duzce Turkiye

To ensure a balance between the power generated and the consumed power in power systems, a control process namely automatic generation control (AGC) must be carried out. This process becomes more challenging due to increasing use of renewable energy sources (RES) such as wind turbines (WT) and photovoltaic (PV) panels. Therefore, AGC needs to be performed more sensitively. In this study, it has been aimed to determine the parameters of the PID controller by using the whale optimization algorithm (WOA) for the AGC in a hybrid power system consisting of photovoltaic (PV) system and thermal generator. The performance of WOA tuned PID controller is tested under load change in area-1 and area-2. Additionally, comparisons have been made with the performances of other optimization techniques such as firefly algorithm (FA), genetic algorithm (GA) and population extremal algorithm (PEO). The results obtained indicated that the WOA tuned PID controller proposed in the study gave better results than the other methods in terms of overshoot values and settling time of system frequency.

关键词： Automatic generation control whale optimization algorithm PID controller PV system Frequency deviation

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A novel data clustering approach based on whale optimization algorithm

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EXPERT SYSTEMS 2021年第3期38卷 e12657-e12657页

作者： Singh, Tribhuvan Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar Odisha India

Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra-cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches, the problem of local entrapment is very common due to weak exploration mechanism. In this paper, a novel approach based on a whale optimization algorithm (WOA) is suggested for data clustering. The performance of the suggested approach is validated using 14 benchmark datasets of the UCI machine learning repository. The experimental results and various statistical tests have justified the efficacy of the suggested approach.

关键词： data clustering data mining k‐ means optimization problems whale optimization algorithm

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Using whale optimization algorithm and Haze Level Information in a Model-Based Image Dehazing algorithm

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SENSORS 2023年第2期23卷 815-815页

作者： Hsieh, Cheng-Hsiung Chen, Ze-Yu Chang, Yi-Hung Chaoyang Univ Technol Dept Comp Sci & Informat Engn 168Jifong E Rd Taichung 413 Taiwan Macronix Int Co 19Lihsin RdSci Pk Hsinchu 300 Taiwan

Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN

关键词： whale optimization algorithm model-based image dehazing algorithm dark channel prior haze level information hazy image discriminator hazy image clustering

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Hybrid whale optimization algorithm enhanced with Levy flight and differential evolution for job shop scheduling problems

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APPLIED SOFT COMPUTING 2020年 87卷 105954-000页

作者： Liu, Min Yao, Xifan Li, Yongxiang South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China Guizhou Univ Engn Sci Sch Mech Engn Bijie 551700 Guizhou Peoples R China

The job shop scheduling problem (JSSP) has been a hot issue in manufacturing. For the past few decades, scholars have been attracted to research JSSP and proposed many novel meta-heuristic algorithms to solve it. whale optimization algorithm (WOA) is such a novel meta-heuristic algorithm and has been proven to be efficient in solving real-world optimization problems in the literature. This paper proposes a hybrid WOA enhanced with Levy flight and differential evolution (WOA-LFDE) to solve JSSP. By changing the expression of Levy flight and DE search strategy, Levy flight enhances the abilities of global search and convergence of WOA in iteration, while DE algorithm improves the exploitation and local search capabilities of WOA and keeps the diversity of solutions to escape local optima. It is then applied to solve 88 JSSP benchmark instances and compared with other state-of-art algorithms. The experimental results and statistical analysis show that the proposed algorithm has superior performance over contesting algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Job shop scheduling problem whale optimization algorithm Levy flight Differential evolution

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Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm

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AGRICULTURAL WATER MANAGEMENT 2021年 249卷 106799-106799页

作者： Zheng, Jing Fan, Junliang Zhang, Fucang Wu, Lifeng Zou, Yufeng Zhuang, Qianlai Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Purdue Univ Dept Earth Atmospher & Planetary Sci W Lafayette IN 47907 USA Purdue Univ Dept Agron W Lafayette IN 47907 USA Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China

Soil mulching can effectively modify the crop growth environment and increase crop productivity in rainfed agriculture. Accurate estimation of crop evapotranspiration (ET), especially its transpiration (T) component, is crucial for understanding the crop water use and predicting crop yield in agricultural ecosystems. Nevertheless, direct measurement of T in the field is often difficult, expensive, destructive and time-consuming. Daily rainfed maize T under four mulching methods (NM: non-mulching, SM: straw mulching, RPBF: plastic-mulched ridge with bare furrow, and RPSF: plastic-mulched ridge with straw-mulched furrow) was obtained from sap flow measurements over four maize growing seasons (2015-2018) in Northwest China. A modified Jarvis-Stewart model (MJS) and a support vector machine model optimized by the whale optimization algorithm (SVM-WOA) were further proposed to estimate daily maize T based on solar radiation (R-s), vapor pressure deficit (VPD), soil water content (SWC) and leaf area index (LAI), which were compared to the simple multiple linear regression model (MLR). The three models were calibrated using data obtained in 2015 and 2017, and validated using data from 2016 and 2018. The measured seasonal T under SM, RPBF and RPSF was increased by 6.9-19.1%, 12.1-31.3% and 15.3-36.7% compared to that under NM, respectively. The SVM-WOA model (R-2 = 0.83-0.89, RMSE = 0.55-0.73 mm d(-1), MAE = 0.42-0.53 mm d(-1)) was superior to the MJS model (R-2 = 0.61-0.79, RMSE = 0.75-1.12 mm d(-1), MAE = 0.58-0.88 mm d(-1)) during validation, both of which greatly outperformed the MLR model (R-2 = 0.57-0.60, RMSE = 1.28-1.41 mm d(-1), MAE = 0.99-1.09 mm d(-1)) under various mulching methods. The prediction accuracy of the SVM-WOA and MJS models was improved by 47-57% and 19-41% in terms of RMSE compared with that of the MLR model, respectively. Although the physically-based MJS model satisfactorily described the dynamics of rainfed maize T under various mulching methods,

关键词： Transpiration Mulching Modified Jarvis-Stewart model Support vector machine whale optimization algorithm

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A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using whale optimization algorithm and Adaptive Neuro-Fuzzy Inference System

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ENERGIES 2022年第7期15卷 2578页

作者： Jalaee, Mahdis Sadat GhasemiNejad, Amin Jalaee, Sayyed Abdolmajid Zarin, Naeeme Amani Derakhshani, Reza Shahid Bahonar Univ Kerman Fac Management & Econ Dept Econ Kerman *** Iran Shahid Bahonar Univ Kerman Dept Geol Kerman *** Iran Univ Utrecht Dept Earth Sci NL-3584 CB Utrecht Netherlands

Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world's primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method's performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R-2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R-2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

关键词： whale optimization algorithm adaptive neuro-fuzzy inference system climate change energy consumption

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A multistrategy hybrid adaptive whale optimization algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第5期9卷 1952-1973页

作者： Li, Xinning Wu, Hu Yang, Qin Tan, Shuai Xue, Peng Yang, Xianhai Shandong Univ Technol Sch Mecharlcal Engn Zibo 255 Peoples R China Gachon Univ Grad Sch Seongnam Si 13120 South Korea Natl Engn Res Ctr Prod Equipment Dongying 257091 Peoples R China

To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) was proposed. In this algorithm, the logistic-tent hybrid chaos algorithm was used to initialize the population, which could make the population distribution more random and uniform. The opposition-based learning strategy was adopted to expand the population of individuals and complete the population selection. To balance the exploitation phase and exploration phase, a dynamic parameter was constructed based on the sigmoid excitation function, and an active adaptive weight was added to adjust the global and local search, which accelerated the convergence speed also. The perturbation mechanism of the Student T-distribution was introduced with random perturbation to expand the search range and improve the global search ability of the algorithm. In total, 23 benchmark functions were selected to conduct convergence performance and optimization performance experiments of the proposed algorithm. The average value and standard deviation were determined as evaluation indexes. The MHWOA was compared with other improved WOA variants and advanced algorithms. The results showed that the proposed MHWOA had better iterative convergence and optimization performance than different algorithms on the optimization of unimodal functions, multimodal functions, and fixed dimension functions. Meanwhile, the MHWOA was applied to the optimal designs of pressure vessels and springs. The experimental results displayed that the MHWOA obtained better solutions than other meta-heuristic algorithms. This study has practical solid application value, which can be applied to solving various engineering problems.

关键词： whale optimization algorithm multistrategy hybrid adaptive optimization meta-heuristic algorithm

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