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Boosting CO2 Uptake from Waste Concrete Powder Using Artificial Intelligence and the marine predators algorithm

PROCESSES 2023年第9期11卷 2655页

作者： Rezk, Hegazy Alahmer, Ali Ghoniem, Rania M. As'ad, Samer Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj 11942 Saudi Arabia Tuskegee Univ Dept Mech Engn Tuskegee AL 36088 USA Tafila Tech Univ Fac Engn Dept Mech Engn Tafila 66110 Jordan Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia Middle East Univ Fac Engn Renewable Energy Engn Dept Amman 11831 Jordan

Waste concrete powder (WCP) is emerging as a potential method of adoption for CO2 sequestration due to its ability to chemically react with carbon dioxide and trap it within its structure. This study explores the application of artificial intelligence (AI) and the marine predators algorithm (MPA) to maximize the absorption of CO2 from waste concrete powder generated by recycling plants for building and demolition debris. Initially, a model is developed to assess CO2 uptake according to carbonation time (CT) and water-to-solid ratio (WSR), utilizing the adaptive neuro-fuzzy inference system (ANFIS) modeling approach. Subsequently, the MPA is employed to estimate the optimal values for CT and WSR, thereby maximizing CO2 uptake. A significant improvement in modeling accuracy is evident when the ANOVA method is replaced with ANFIS, leading to a substantial increase of approximately 19% in the coefficient of determination (R-squared) from 0.84, obtained through ANOVA, to an impressive 0.9999 obtained through the implementation of ANFIS;furthermore, the utilization of ANFIS yields a substantial reduction in the root mean square error (RMSE) from 1.96, as indicated by ANOVA, to an impressively low value of 0.0102 with ANFIS. The integration of ANFIS and MPA demonstrates impressive results, with a nearly 30% increase in the percentage value of CO2 uptake. The highest CO2 uptake of 3.86% was achieved when the carbonation time was 54.3 h, and the water-to-solid ratio was 0.27. This study highlights the potential of AI and the MPA as effective tools for optimizing CO2 absorption from waste concrete powder, contributing to sustainable waste management practices in the construction industry.

关键词： waste concrete powder CO2 uptake marine predators algorithm ANFIS modeling mineral carbonation

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QQLMPA: A quasi-opposition learning and Q-learning based marine predators algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Zhao, Shangrui Wu, Yulu Tan, Shuang Wu, Jinran Cui, Zhesen Wang, You-Gan Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Australian Catholic Univ Inst Learning Sci & Teacher Educ Brisbane 4000 Australia Changzhi Univ Dept Comp Sci Changzhi 046011 Shanxi Peoples R China

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Levy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer.

关键词： Q-learning algorithm marine predators algorithm Meta-heuristics Quasi-opposition based learning

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Excogitating marine predators algorithm based on random opposition-based learning for feature selection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第4期34卷

作者： Balakrishnan, Kulanthaivel Dhanalakshmi, Ramasamy Khaire, Utkarsh Mahadeo Indian Inst Informat Technol Tiruchirappalli Dept Comp Sci & Engn Tiruchirappalli India Indian Inst Informat Technol Dharwad Dept Data Sci & Intelligent Syst Dharwad 580009 Karnataka India

Obtaining precise information from a high-dimensional dataset is one of the most difficult tasks as datasets contain more features and fewer samples. The high-dimensionality of the dataset reduces predictive capability and increases the computational complexity of the analytical model. The widespread employment of meta-heuristic methods to handle the challenge of high-dimensional datasets has been exceptional in recent years. The marine predators algorithm (MPA) is a recently developed meta-heuristic algorithm based on the "survival-of-the-fittest" notion. This research critique overcomes the drawbacks of the existing MPA and proposes a feature selection model using random opposition-based learning (ROBL). The searching for the optimum solution in a single direction of the traditional MPA reduces its performance. The incorporation of ROBL in the MPA enhances its ability to reconnoiter bigger search space. The proposed algorithm generates a new population based on the initial and random opposite population. The performance of ROBL-MPA is inspected on six high-dimensional microarray datasets. The results of the proposed ROBL-MPA are compared to traditional MPA and opposition based MPA (OBL-MPA). The proposed ROBL-MPA outperforms traditional MPA based on several benchmark performance analysis tests.

关键词： feature selection marine predators algorithm meta-heuristic optimization random opposition-based learning

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Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification

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ENERGY 2022年第PartB期239卷 122072-122072页

作者： Rezk, Hegazy Inayat, Abrar Abdelkareem, Mohammad A. Olabi, Abdul G. Nassef, Ahmed M. Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Riyadh Saudi Arabia Minia Univ Fac Engn Dept Elect Engn Al Minya Egypt Univ Sharjah Dept Sustainable & Renewable Energy Engn Sharjah 27272 U Arab Emirates Univ Sharjah Ctr Sustainable Energy & Power Syst Biomass & Bioenergy Res Grp Sharjah 27272 U Arab Emirates Univ Sharjah Ctr Adv Mat Res Sharjah 27272 U Arab Emirates Minia Univ Fac Engn Dept Chem Engn Al Minya Egypt Aston Univ Sch Engn & Appl Sci Mech Engn & Design Birmingham B4 7ET W Midlands England Tanta Univ Fac Engn Comp & Automat Control Engn Dept Tanta Egypt

The current study is related to optimizing methane production from steam gasification of palm kernel shell (PKS) utilizing coal bottom ash as a catalyst. Based on the experimental dataset, the fuzzy logic technique, represented by the Adaptive Network-based Fuzzy Inference System (ANFIS) structure, is used to create a robust model to simulate methane production via biomass gasification. Then, a marine Predator algorithm (MPA) is used to determine the optimal operating parameter of the gasification process. The temperature, particle size, CaO/PKS ratio, and coal bottom ash are used as the decision variables, whereas methane production is used as the objective function. The main findings confirmed that the yield of methane gasification reached 52.82 vol% when the associated temperature, particle size, CaO/PKS Ratio, and coal bottom ash are at 678 degrees C, 0.42 mm, 3.03, and 0.037 wt%, respectively. Accordingly, the proposed strategy provides a better result than the Analysis of Variance (ANOVA) methodology reported in the literature. Furthermore, the proposed strategy's output exceeds both the results obtained experimentally and ANOVA methods by 20.26% and 29.69%, respectively. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic marine predators algorithm Methane Biomass gasification

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An innovative prediction algorithm based on grey modeling theory and the marine predators algorithm for short-term carbon dioxide emissions in China

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期137卷

作者： Liu, Chong Wu, Wen-Ze Xie, Wanli Shi, Sheng Zhu, Hegui Northeastern Univ Coll Sci Shenyang 110819 Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China

To accurately predict China's carbon dioxide emissions, this paper constructs an innovative prediction algorithm based on the marine predators algorithm and a new discrete nonlinear grey Bernoulli model with fractional-order accumulation operation. In this prediction algorithm, the new model is used to complete the modeling task of nonlinear time series, and the marine predators algorithm is used to facilitate the solution process of model. It is found that the proposed model satisfies both unbiasedness and uniformity, underscoring its superiority over conventional grey prediction models. Numerical results indicate that the proposed algorithm outperforms all competing algorithms in fitting China's carbon dioxide emissions, validating the effectiveness and feasibility of this approach.

关键词： Carbon dioxide emissions prediction Fractional-order accumulation operation marine predators algorithm Grey system model

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Detection of depression and anxiety in the perinatal period using marine predators algorithm and kNN

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期161卷 107003-107003页

作者： Ogur, Nur Banu Kotan, Muhammed Balta, Deniz Yavuz, Burcu Carkli Ogur, Yavuz Selim Yuvaci, Hilal Uslu Yazici, Esra Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn Sakarya Turkiye Sakarya Univ Fac Comp & Informat Sci Dept Informat Syst Engn Sakarya Turkiye Sakarya Univ Fac Comp & Informat Sci Dept Software Engn Sakarya Turkiye Sakarya Univ Fac Med Dept Psychiat Sakarya Turkiye Sakarya Univ Fac Med Dept Obstet & Gynecol Sakarya Turkiye

Undiagnosed prenatal anxiety and depression have the potential to worsen and have an adverse effect on both the mother and the infant. Although the diagnosis is made by specialist doctors, it is unclear which parameters are more effective. Especially in medicine, it is crucial to diagnose disease with high accuracy. For this reason, in this study, a questionnaire study was first conducted on pregnant women, and real original data were collected. Then, the marine predators algorithm (MPA), one of the current metaheuristic algorithms inspired by nature, was combined with K-Nearest Neighbors (kNN) to determine high-priority features in the collected data. As a result, five of the 147 features selected by the proposed method were determined as high priority and approved by the doctors. In addition, the proposed method is compared with the Chi-square method, which is one of the filter-based feature selection methods. Thanks to the proposed feature selection method based on MPA and kNN, it has been observed that the classification gives more successful results in a shorter time with 98.11% success, and the model supports the diagnosis stage of the doctors.

关键词： Perinatal period Anxiety Depression Feature selection marine predators algorithm K-Nearest Neighbors

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Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm

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APPLIED SOFT COMPUTING 2021年第PartA期113卷 107905-107905页

作者： Xing, Zhikai He, Yigang Wuhan Univ Sch Elect Engn & Automation Wuhan 430072 Peoples R China

With the development of power systems, fault diagnosis in infrared images has become increasingly important for ensuring the stability of these systems. In this paper, we propose a multi-objective multilevel threshold image segmentation method based on the boost marine predators algorithm (BMPA) for infrared-image fault diagnosis. As fault points are small, it is difficult to detect the target in an infrared image using existing segmentation methods. To address this, we use 9DKapur as the fitness function and obtain a many-objective optimization problems (MaOPs). Then, we use adaptive weights and opposition-based learning to boost the optimization ability of the MPA and solve the MOP so that a Pareto front is obtained. The DTLZ and WFG test suits are used as benchmarks to evaluate the performance of the BMPA, and electric equipment in infrared images was used to assess the fault-diagnostic ability of the proposed method. The results demonstrate that the BMPA performs better than other optimization algorithms in terms of uniformity measurement, peak signal-to-noise ratio, feature similarity, hypervolume, spacing, and CPU time. On the actual test data, the recall rate and fault detection accuracy of the proposed method were 94.29% and 92.38%, respectively. Insulator faults under various circumstances can be correctly detected in infrared images. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Power equipment fault diagnosis Infrared image segmentation Many objectives optimization marine predators algorithm

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A novel grey multivariate convolution model based on the improved marine predators algorithm for predicting fossil CO2 emissions in China

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Cang, Haoze Zeng, Xiangyan Yan, Shuli Guilin Univ Elect Technol Sch Math & Comp Sci Guangxi Coll Guilin 541004 Peoples R China Guilin Univ Elect Technol Univ Key Lab Data Anal & Computat Guilin 541004 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China

The effective prediction of fossil carbon dioxide (CO2) emissions provides a foundation for environmental protection authorities to make relevant management and decisions. CO2 emissions are influenced by various factors and exhibit a nonlinear and complex trend. First, the weighted conformable fractional accumulation (WCFA) method is proposed. WCFA is a generalized form of the existing weighted accumulation and conformable fractional order accumulation. A nonlinear discrete grey Bernoulli multivariate convolution model is introduced to model the sequences processed by WCFA. Then, an improved marine predators algorithm (IMPA) is obtained by improving the marine predators algorithm by the tent map and cosine control parameter. The performance of IMPA is verified by algorithm comparison experiments and Friedman test. The feasibility and predictive effectiveness of the proposed model is validated through three case studies. In prediction of fossil CO2 emissions in China, the fitting and prediction errors of the proposed model reach 1.1773% and 0.5402%, respectively. Finally, the total fossil CO2 emissions in China from 2022 to 2026 are predicted and analyzed. The results indicate a gradual decrease in fossil CO2 emissions over the next five years. Some reasonable suggestions are made based on the prediction results and sources of fossil CO2 emissions.

关键词： GMC(1 N) model Weighted conformable fractional accumulation generation marine predators algorithm

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Chaotic marine predators algorithm for global optimization of real-world engineering problems

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KNOWLEDGE-BASED SYSTEMS 2023年第1期261卷

作者： Kumar, Sumit Yildiz, Betul Sultan Mehta, Pranav Panagant, Natee Sait, Sadiq M. Mirjalili, Seyedali Yildiz, Ali Riza Univ Tasmania Australian Maritime Coll Coll Sci & Engn Launceston 7248 Australia Bursa Uludag Univ Dept Mech Engn Gorukle Bursa Turkey Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 India Khon Kaen Univ Fac Engn Sustainable Infrastruct Res & Dev Ctr Dept Mech Engn Khon Kaen 40002 Thailand King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran Saudi Arabia Torrens Univ Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

A novel metaheuristic called Chaotic marine predators algorithm (CMPA) is proposed and investigated for the optimization of engineering problems. CMPA integrates the exploration merits of the recently proposed marine predators algorithm (MPA) with the chaotic maps exploitation capabilities. Several chaotic maps were applied in the proposed CMPA to govern MPA parameters that eventually led to controlled exploration and exploitation of search. This study makes an initial attempt to explore and employ CMPA in decoding complex and challenging design and manufacturing problems. For performance evaluation of the proposed algorithm, CEC 2020 numerical problems having different dimensions and five widely adopted constrained design problems were solved. For all problems, both qualitative and qualitative results are examined and discussed. Moreover, two case studies of multi pass turning were examined by the proposed CMPA algorithm to optimize the cutting operation with a minimum cost of production per unit objective. Furthermore, the suggested CMPA algorithm has been investigated for solving a real-world structural topology optimization problem. Statistical analysis is performed, and the results of CMPA are compared with twelve distinguished algorithms. Outcomes of the proposed variant algorithm on the benchmarks demonstrate its significantly improved performance relative to other optimizers including a variant of MPA and two state-of-the-art IEEE CEC competitions winners algorithms. Findings from the manufacturing process exhibit CMPA proficiency in solving arduous real-world design problems.(c) 2022 Published by Elsevier B.V.

关键词： marine predators algorithm Chaotic maps Global optimization Engineering design problems Metaheuristic algorithms

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Training Feedforward Neural Networks Using an Enhanced marine predators algorithm

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PROCESSES 2023年第3期11卷 924-924页

作者： Zhang, Jinzhong Xu, Yubao West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any finite training sample set. In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based mutation operator (EMPA) was presented to train FNNs, and the objective was to attain the minimum classification, prediction, and approximation errors by modifying the connection weight and deviation value. The ranking-based mutation operator not only determines the best search agent and elevates the exploitation ability, but it also delays premature convergence and accelerates the optimization process. The EMPA integrates exploration and exploitation to mitigate search stagnation, and it has sufficient stability and flexibility to acquire the finest solution. To assess the significance and stability of the EMPA, a series of experiments on seventeen distinct datasets from the machine learning repository of the University of California Irvine (UCI) were utilized. The experimental results demonstrated that the EMPA has a quicker convergence speed, greater calculation accuracy, higher classification rate, strong stability and robustness, which is productive and reliable for training FNNs.

关键词： feedforward neural networks marine predators algorithm ranking-based mutation operator experimental results

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