Feasibility evaluation of large-scale underground hydrogen storage in bedded salt rocks of China: A case study in Jiangsu province

Liu, Wei; Zhang, Zhixin; Chen, Jie; Jiang, Deyi; Wu, Fei; Fan, Jinyang; Li, Yinping

doi:10.1016/j.energy.2020.117348

Cited by 187 publications

(56 citation statements)

References 64 publications

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“…Recently, HHO finds its applications in several types of problems [54] including micro-channel heat sink design [55], optimization of related components of harmonics polluted distribution systems [56], satellite image de-noising [57], manufacturing optimization problems [58], structural design optimization of vehicle components [59], drug design and discovery [60], and soil compression coefficient prediction [61]. Still, many real-life cases not modeled as an optimization case, whereas there are some aspects that we can solve those cases using new optimizers such as HHO [62]- [65]. Bao et al [66] described a hybrid algorithm, with the HHO and differential evolutionary (DE) algorithms involved, and used it in the color image multi-threshold segmentation field.…”

Section: Proposed Gbhhomentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

et al. 2020

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This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students. The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk's optimizer (HHO). In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets. Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students. In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems. The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems. At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention. The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics. Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.

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Section: Proposed Gbhhomentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

et al. 2020

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“…1 K -nearest neighbor (KNN) method is implemented from scratch. All parameters were determined by trial and error approach [95], [96]. The search range of penalty coefficient and kernel bandwidth in SVM and KELM are set to [ −10, 10].…”

Section: B Experimental Setupmentioning

confidence: 99%

Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

Zhu

Zhao

et al. 2020

IEEE Access

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This study aims to propose an efficient evaluation model for Sino foreign cooperative education projects, which can offer a reasonable reference for universities to deepen reform and innovation of education and further enhance the level of international education. The core engine of the model is the kernel extreme learning machine (KELM) model integrated with orthogonal learning (OL) strategy optimization. The introduction of the OL mechanism is to further strengthen the optimization capabilities of the basic SCA, which is devoted to promoting the KELM model to select the optimal parameter combination and feature subset and further enhance the KELM evaluation capability of Sino foreign cooperative education projects. To examine the performance of the proposed method, OLSCA is evaluated on 23 benchmark problems, comparison with eight other well-known methods. The experimental results have shown that the proposed OLSCA is prominently superior to existing methods on most functional problems. Meantime, OLSCA-KELM is compared against other machine learning approaches in dealing with the evaluation of education projects of Sino foreign cooperation. The simulation results illustrate that the presented OLSCA-KELM obtains better performance of classification and higher stability on all four indicators. Therefore, it is evident that the presented OLSCA-KELM can be an effective solution for the evaluation of Sino foreign cooperative education projects. INDEX TERMS Sine cosine algorithm, swarm intelligence, sino foreign cooperative education project, Kernel extreme learning machine, parameter optimization. HUILING CHEN received the Ph.D. degree from the Department of Computer Science and Technology, Jilin University, China. He is currently an Associate Professor with the College of Computer Science and Artificial Intelligence, Wenzhou University, China. He has published more than 100 articles in international journals and conference proceedings, including Information Sciences, Pattern Recognition, Future Generation Computer System, Expert Systems with Applications, Knowledge-Based Systems, Neurocomputing, PAKDD, and among others. His current research interests include machine learning and data mining, as well as their applications to medical diagnosis and bankruptcy prediction. He is also a Reviewer of many journals such as Applied Soft Computing, Artificial Intelligence in Medicine, Knowledge-Based Systems, and Future Generation Computer System. He has been serving as an Associate Editor for IEEE ACCESS.

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“…The interlayers seriously affect the sealing characteristics of surrounding rocks of the SPR salt caverns, which are also the key to determine whether salt caverns can store crude oil safely and reliably [25]. Once the salt cavern leaks, it will lead to oil leakage, which may bring the risk of serious disasters such as cavity scrapping and ground subsidence, even causing major fires and activating regional faults [26][27][28]. Therefore, the porosity and permeability characteristics of the rock surrounding the caverns are important for evaluating its tightness [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Microscopic Pore Structure of Surrounding Rock for Underground Strategic Petroleum Reserve (SPR) Caverns in Bedded Rock Salt

et al. 2020

Self Cite

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Using salt caverns for an underground strategic petroleum reserve (SPR) is considered as an ideal approach due to the excellent characteristics of low porosity, low permeability, self-healing of damage, and strong plastic deformation ability of rock salt. Salt deposits in China are mostly layered rock salt structures, with the characteristics of many interlayers, bringing great challenges for the construction of SPR facilities. Studying the microscopic pore characteristics of the rock surrounding SPR salt caverns in different environments (with brine and crude oil erosion) is necessary because the essence of mechanical and permeability characteristics is the macroscopic embodiment of the microscopic pore structure. In this paper, XRD tests and SEM tests are carried out to determine the physical properties of storage media and surrounding rock. Gas adsorption tests and mercury intrusion tests are carried out to analyze the microscopic pore structure, specific surface area variation and total aperture distribution characteristics of SPR salt cavern host rock. Results show that: (1) Large numbers of cores in interlayer and caprock may provide favorable channels for the leakage of high-pressure crude oil and brine. (2) The blockage of pores by macromolecular organic matter (colloid and asphaltene) in crude oil will not significantly change the structural characteristics of the rock skeleton, which is beneficial to the long-term operation of the SPR salt cavern. (3) The water–rock interaction will bring obvious changes in the micro-pore structure of rock and increase the leakage risk of the storage medium. The results can provide theoretical bases and methods for the tightness analysis of China’s first underground SPR salt cavern.

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Feasibility evaluation of large-scale underground hydrogen storage in bedded salt rocks of China: A case study in Jiangsu province

Cited by 187 publications

References 64 publications

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

Microscopic Pore Structure of Surrounding Rock for Underground Strategic Petroleum Reserve (SPR) Caverns in Bedded Rock Salt

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