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Smart predictive viscosity mixing of CO2-N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2024年第2期12卷

作者： Ewees, Ahmed A. Thanh, Hung Vo Al-qaness, Mohammed A. A. Abd Elaziz, Mohamed Samak, Ahmed H. Univ Bisha Coll Comp & Informat Syst Dept Informat Sci & Cybersecur POB 551 Bisha Saudi Arabia Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zhejiang Optoelect Res Inst Jinhua 321004 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Menoufia Univ Fac Sci Shibin Al Kawm Egypt Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman Jordan

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization algorithm with marine Predator algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

关键词： CO2 N2 CCUS marine predators algorithm Seagull optimization algorithm

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Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第2期35卷 025504-025504页

作者： Yan, Pengcheng Wang, JingBao Wang, Wenchang Li, Guodong Zhao, Yuting Wen, Ziming Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol Collaborat Innovat Ctr Mine Intelligent Equipment Huainan 232001 Peoples R China

Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established;and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitti

关键词： laser-induced fluorescence fault detection marine predators algorithm random forest

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An Amalgamated Novel IDS Model for Misbehaviour Detection using VeReMiNet

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COMPUTER STANDARDS & INTERFACES 2024年 88卷

作者： Saudagar, Saleha Ranawat, Rekha SAGE Univ Indore Dept Comp Sci & Engn Devas Indore Highway Indore India

Vehicle ad hoc networks (VANETs) are gaining popularity because they can potentially increase traffic efficiency with road safety in cooperative intelligent transport systems (C-ITS). Effective vehicle deployment requires the identification of nodes providing false data. Researchers have focused much of their attention on an important issue called misbehaviour detection for disseminating false data about vehicle information (position, time, speed and vehicle id) in VANETs. It can threaten the security and privacy of the network and cause some problems, including accidents, collisions, and traffic congestion. There are more works have been proposed based on machine learning methods. However, all of the machine learning-based detection models used currently are signature-based, necessitating previous knowledge of the attacks to identify them and also requiring numerous features, which increases the computational time needed to detect attacks and to select the high-performing features;this works a marine predator algorithm based on teaching-learning. The article provides a unique deep learning strategy to recognize the position falsification attacks in VANETs to find hidden patterns from the data for fighting against known and emerging threats. We evaluate our method using a publicly accessible VeReMi dataset, demonstrating that it achieves high detection accuracy of 99.55% for various attacks. According to our findings, our method can be useful for enhancing VANET security and assuring vehicular communications' reliable and secure functioning.

关键词： Intelligent transport systems VANETs malicious nodes meta-heuristics optimization marine predators algorithm teaching-learning-based optimization

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Wind integrated optimal power flow considering power losses, voltage deviation, and emission using equilibrium optimization algorithm

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ENERGY ECOLOGY AND ENVIRONMENT 2022年第4期7卷 369-392页

作者： Amroune, Mohammed Univ Ferhat Abbas Setif 1 Dept Elect Engn Setif 19000 Algeria

In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional thermal power generators in the system. The objectives are to minimize generation costs, including those incurred in thermal and stochastic wind power generation, active power loss, voltage deviation, and emission. To evaluate the performance of the EO algorithm in the OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems with stochastic wind power generators will be used. A comparative study will be performed to show the efficiency of the EO algorithm compared with other recently developed metaheuristic algorithms such as the marine predators algorithm (MPA), artificial ecosystem-based optimization (AEO), and slime mould algorithm (SMA), as well as with other well-known algorithms. Based on the obtained results, the EO algorithm offered the best results.

关键词： Optimal power flow Wind power Equilibrium optimization marine predators algorithm Artificial ecosystem-based optimization Slime mould algorithm

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Enhancing photovoltaic hosting capacity in distribution networks by optimal allocation and operation of static var compensators 57

Enhancing photovoltaic hosting capacity in distribution netw...

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57th International Universities Power Engineering Conference (UPEC) - Big Data and Smart Grids

作者： Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Univ Twente Dept EEMCS Enschede Netherlands Marmara Univ Dept Elect & Elect Engn Istanbul Turkey Istanbul Tech Univ Dept Elect Engn Istanbul Turkey

ISBN: (纸本)9781665455053

This paper presents a novel two-stage optimization approach for the optimal sizing of static var compensators and their operational management to maximize the photovoltaic integration capacity of distribution systems. In the first stage, the optimal locations for fixed size photovoltaic (PV) systems are determined to minimize the sum of total voltage violations. In the second phase, the size of the PV units are resized and the optimal size, number, location, and operating strategy of the SVC units are determined to maximize PV hosting capacity. In both phases, the marine predators algorithm is used for the solution optimization equations. The performance of the proposed approach and solution method is validated on modified 33-node and 141node radial distribution networks. The results are discussed from the point of view of the maximum hosting capacity and compared with the Grey Wolf Optimization and Whale Optimization algorithms in terms of computational performance.

关键词： Distributed generation Distribution Networks marine predators algorithm Static VAR compensator

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Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

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ENERGY REPORTS 2021年 7卷 3760-3774页

作者： Ajayi, Oluwafemi Heymann, Reolyn Univ Johannesburg Dept Elect & Elect Engn ZA-2092 Johannesburg South Africa Univ Johannesburg Ctr Collaborat Digital Networks Johannesburg South Africa

This paper presents a novel marine predators algorithm for both training an Artificial Neural Network model used for predicting the energy demand and for solving a dynamic combined economic and emission dispatch of a data centre. The MPA is proposed for first finding the optimal weights and biases of the neural network based on a Mean Squared Error and Mean Absolute Error minimization objective function. Real life dataset obtained from an anonymous data centre operator in Cape Town, South Africa was used for the model implementation. The dataset was made up of a total of 564 samples and was split into training and testing set using an 80:20 ratio. The input variables contained in the dataset are the data centre's ambient temperature, ambient relative humidity, chiller output temperature and Computer Room Air Conditioning supply temperature while the energy demand is the target variable. The optimal weights of the neural network model were analysed using a weights based approach to determine the level of influence each input parameter of the model has on the data centre's energy demand. Then based on the predicted energy demand of the data centre, a dynamic economic and emission dispatch problem is solved for the building while considering thermal and solar photovoltaic generations. A spinning reserve is also incorporated in the energy dispatch model to cater for any shortfall that may exist between the predicted and actual energy demand of the data centre due to possible inaccuracies in the energy demand prediction model. Results for the energy demand prediction task showed that the proposed method outperformed the state-of-the-art by producing the least Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and highest prediction accuracy for the training and testing sets. Further analyses also highlighted that the data centre's ambient temperature has the highest influence of about 37.63% on its energy demand pattern. For

关键词： Artificial Neural Networks Data Centre Economic and Emission Dispatch Energy Demand Forecasting marine predators algorithm

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Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

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ISA TRANSACTIONS 2021年 114卷 470-484页

作者： Wang, Zhenya Yao, Ligang Chen, Gang Ding, Jiaxin Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China

The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods. This paper focuses on the sensitive features extraction and pattern recognition for rolling bearing fault diagnosis and proposes a novel intelligent fault-diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM). Firstly, a novel non-linear technology named GCMWPE was presented, allowing the extraction of bearing features from multiple scales and enabling the construction of a high-dimensional feature set. The GCMWPE uses the generalized composite coarse-grained structure to overcome the shortcomings of the original structure in multiscale weighted permutation entropy and obtain more stable entropy values. Subsequently, the S-Iso algorithm was introduced to obtain the main features and reduce the GCMWPE set dimensionality. Finally, a combination of GCMWPE and S-Iso set was input to the MPA-SVM for diagnosis and identification. The marine predators algorithm (MPA) was used to obtain the optimal SVM parameters. The effectiveness of the proposed fault diagnosis method was confirmed through two bearing fault diagnosis experiments. The results have shown that the proposed method can be used to correctly diagnose bearing states with high diagnostic accuracy. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Rolling bearing Multiscale weighted permutation entropy Supervised Isomap marine predators algorithm Support vector machine

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A Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

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Computer Systems Science & Engineering 2021年第4期37卷 63-71页

作者： A.N.M.Alahmadi Hegazy Rezk Department of Electrical Engineering Umm Al-Qura UniversityMakkah21955Saudi Arabia College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz UniversityWadi Ad-Dawasir11991Saudi Arabia Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt

A robust single-sensor global maximum power point tracking(MPPT)strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this *** proposed strategy is designed for battery charging applications and direct current(DC)*** normal operation,the curve of photovoltaic(PV)output power versus PV voltage contains only a single peak *** point can be simply captured using any traditional tracking method like perturb and ***,this situation is completely different during the shadowing effect where several peaks appear on the power voltage *** of these peaks are local with only a single *** condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak *** are trapped in the first peak point even when the point is ***,global tracking approaches based on modern optimization are highly required.A recent marine predators algorithm(MPA)has been used to solve the problem of tracking the global MPP under shadowing *** shadowing scenarios are used to test and evaluate the performance of MPA based *** obtained results are compared with particle swarm optimization(PSO)and ant lion optimizer(ALO).The results of the comparison con-firmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.

关键词： Optimization marine predators algorithm photovoltaic system battery charging applications partial shading condition MPPT

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On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第23期33卷 15969-15985页

作者： Ben Seghier, Mohamed El Amine Corriea, Jose A. F. O. Jafari-Asl, Jafar Malekjafarian, Abdollah Plevris, Vagelis Nguyen-Thoi Trung Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Univ Porto Fac Engn CONSTRUCT Porto Portugal Univ Sistan & Baluchestan Fac Engn Dept Civil Engn Zahedan Iran Univ Coll Dublin Sch Civil Engn Struct Dynam & Assessment Lab Dublin 4 Ireland Qatar Univ Dept Civil & Architectural Engn POB 2713 Doha Qatar

Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.

关键词： Suspension bridges Main cables Annual corrosion rate Artificial intelligence Multilayer perceptron marine predators algorithm

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New binary marine predators optimization algorithms for 0-1 knapsack problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106949-106949页

作者： Abdel-Basset, Mohamed Mohamed, Reda Chakrabortty, Ripon K. Ryan, Michael Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Sharqiyah 44519 Egypt UNSW Canberra Sch Engn & IT Capabil Syst Ctr Canberra ACT Australia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Recently, a novel meta-heuristic algorithm known as the marine predators algorithm (MPA) has been proposed for solving continuous optimization problems. Despite the significant superiority of MPA in solving continuous problems, the algorithm is not applicable to binary problems. This work proposes a binary version of MPA for solving the 0-1 knapsack (KP01) problem. To develop the binary variant of MPA (BMPA), a wide range of V-Shaped and S-shaped transfer functions is investigated for mapping continuous values to binary. The performance of a binary algorithm is first shown to heavily rely on the selection of an appropriate transfer function on a specific dataset. The performance of the proposed BMPA algorithm is then tested on a set of KP01 problems and compared to a number of existing algorithms. The results demonstate the merits of the BMPAs proposed in this work.

关键词： 0-1 knapsack problem marine predators algorithm algorithm Artificial Intelligence Repair infeasible solutions Binary optimization Benchmark Combinatorial optimization

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