An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations

Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Thanh, Hung Vo; AlRassas, Ayman Mutahar; Elaziz, Mohamed Abd

doi:10.1016/j.est.2022.106150

Cited by 22 publications

(5 citation statements)

References 51 publications

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“…Models such as LSSVM, RF, GRNN, and others showed good performance. However, researchers ,, included several optimization strategies into the models to further increase accuracy and lower computational costs. The Aquila optimizer (AO), salp swarm algorithm (SSA), genetic algorithm (GA), cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and others are among these optimization algorithms.…”

Section: Application Of Machine Learning In Geological Storagementioning

confidence: 99%

Recent Advances in Geological Storage: Trapping Mechanisms, Storage Sites, Projects, and Application of Machine Learning

Feng

et al. 2023

Energy Fuels

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A significant amount of carbon dioxide (CO2) is being released into the atmosphere as a result of the acceleration of industrialization and increased energy consumption, which are causing a rise in world temperatures. Despite the fact that nations all over the world have actively participated in CO2 storage projects, there is still not enough to moderate global temperatures. Therefore, there is an urgent need for the development of CO2 sequestration technologies. The main geological storage trapping mechanisms are discussed in this work along with an analysis of the major influencing variables. Additionally, the benefits and drawbacks of significant storage locations and current research hotspots are explored. Storage project development across the globe is outlined. The use of machine learning for CO2 sequestration is reviewed toward the end. This extensive review reveals that the main variables affecting CO2 capture capacity are hydrodynamic and geochemical. The sequestration capability of various storage sites is significantly influenced by the reservoir characteristics and implementation methodologies. The successful implementation of the storage project is determined by local policies and public support. The development of machine learning technologies makes storage projects safer and more dependable.

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Section: Application Of Machine Learning In Geological Storagementioning

confidence: 99%

Recent Advances in Geological Storage: Trapping Mechanisms, Storage Sites, Projects, and Application of Machine Learning

Feng

et al. 2023

Energy Fuels

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“…By incorporating the AO's local search capabilities, the SOAAO method improves the optimization process of the DNR model for wind power forecasting. In their study, authors 53 employed an optimized adaptive neuro‐fuzzy inference system (ANFIS) to predict two indices related to CO2 Trapping in deep saline aquifers. To enhance the performance of the ANFIS model and optimize its parameters, they utilized two recently developed optimization algorithms, the AO and the salp swarm algorithm (SSA) 54 .…”

Section: Related Workmentioning

confidence: 99%

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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“…For alternative approaches to CCS challenges, and associated numerical modeling solutions, see, e.g., recent works. [10,11] In order to dynamically maintain its position while being subject to in situ ambient hydrodynamic stresses from ocean subsurface currents, the SST vessel uses thrusters and a propeller, see Figure 2.…”

Section: Introductionmentioning

confidence: 99%

“…For alternative approaches to CCS challenges, and associated numerical modeling solutions, see, e.g., recent works. [ 10 , 11 ]…”

Section: Introductionmentioning

confidence: 99%

Carbon Storage Tanker Lifetime Assessment

Gaidai

et al. 2023

Global Challenges

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CO 2 capture and storage (CCS) is an important strategy to reduce global CO 2 emissions. This work presents both cutting-edge carbon storage tanker design, as well as novel reliability method making possible to extract useful information about the lifespan distribution of carbon capture systems from their recorded time history. The method outlined may be applied on more complex sustainable systems that are exposed to environmental stresses throughout the whole period of their planned service life. The latter is of paramount importance at the design stage for complex engineering systems. Novel design for CCS system is discussed and accurate numerical simulation results are used to apply suggested novel reliability methodology. Furthermore, traditional reliability approaches that deal with complex energy systems are not well suited for handling high dimensionality and cross-correlation between various system components of innovative dynamic CO 2 storage subsea shuttle tanker. This study has two distinctive key features: the state of art CCS design concept, and the novel general purpose reliability method, recently developed by authors, and particularly suitable for operational safety study of complex energy systems.

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An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations

Cited by 22 publications

References 51 publications

Recent Advances in Geological Storage: Trapping Mechanisms, Storage Sites, Projects, and Application of Machine Learning

Recent Advances in Geological Storage: Trapping Mechanisms, Storage Sites, Projects, and Application of Machine Learning

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Carbon Storage Tanker Lifetime Assessment

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