Article
Materials Science, Composites
Gokce Ozden, Mustafa Ozgur Oteyaka, Francisco Mata Cabrera
Summary: Polyetheretherketone (PEEK) and its composites are widely used in various industries. This study employed Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) to predict cutting forces during the machining of PEEK with different reinforcements. The experimental results showed that both ANN and ANFIS models provided accurate predictions of cutting forces.
JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS
(2023)
Article
Energy & Fuels
Aamer Bilal Asghar, Saad Farooq, Muhammad Shahzad Khurram, Mujtaba Hussain Jaffery, Krzysztof Ejsmont
Summary: Circulating Fluidized Bed gasifiers are commonly used to convert solid fuel into liquid fuel. This study employs Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System to estimate the solid circulation rate in the gasifier, and experimental results demonstrate the superiority of the Adaptive Neuro-Fuzzy Inference System.
Article
Engineering, Chemical
Musamba Banza, Hilary Rutto
Summary: The current study examines the effectiveness of removing nickel (II) from aqueous solutions using an adsorption method. ANN and ANFIS models were used to predict the adsorption potential of blend hydrogels for nickel (II) removal. The results show that the ANN and ANFIS models are promising approaches for predicting metal ions adsorption. The adsorption process is spontaneous and well explained by the Langmuir model.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wei Chang, Wenzhong Zheng
Summary: In this paper, an adaptive neural-fuzzy inference system (ANFIS) model was developed to evaluate the compressive strength of concrete confined with stirrups. By establishing a reliable experimental database and analyzing the effects of various parameters on the compressive strength of concrete, the results showed that the proposed ANFIS model accurately predicted the compressive strength of concrete confined with spiral stirrups.
Article
Environmental Sciences
Vahid Nourani, Hossein Karimzadeh, Aida Hosseini Baghanam
Summary: This study developed an efficient model for predicting CO pollutant concentrations using artificial neural network (ANN) and adaptive neural-fuzzy inference system (ANFIS), demonstrating the importance of air quality monitoring and developing effective models for sustainable development goals.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Construction & Building Technology
Ahmed M. Yosri, A. I. B. Farouk, S. I. Haruna, Ahmed Farouk Deifalla, Walaa Mahmoud Shaaban
Summary: This study performs a sensitivity analysis on the shear strength prediction of stud connectors embedded in concrete and finds that the number of studs is the most sensitive parameter. Regardless of concrete compressive strength, the combination of stud diameter, number of studs, and stud spacing can accurately predict shear strength with an accuracy of +/- 8.67 kN. The results demonstrate that sensitivity analysis is an essential tool for accurate prediction using machine learning models.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2023)
Article
Agricultural Engineering
Pu-Yun Kow, Mei-Kuang Lu, Meng-Hsin Lee, Wei-Bin Lu, Fi-John Chang
Summary: A hybrid machine learning approach (ANFIS-NM) was proposed to optimize the cultivation conditions of Antrodia cinnamomea (A. cinnamomea) based on a 32 fractional factorial design. The approach successfully identified three key factors and significantly boosted mycelia yield. It reduces time consumption and increases mycelia yield, showing great potential for biomass production.
BIORESOURCE TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Mohammed Al-Yaari, Theyazn H. H. Aldhyani, Sayeed Rushd
Summary: In this study, a new artificial neural network (ANN) model was developed using different architectures of an adaptive network-based fuzzy inference system (ANFIS) to predict the adsorption efficiency of arsenate (As(III)) from polluted water. The results showed that the ANFIS model had high prediction accuracy and identified the dominant factors affecting the adsorption process efficiency.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Vali Rasooli Sharabiani, Mohammad Kaveh, Ebrahim Taghinezhad, Rouzbeh Abbaszadeh, Esmail Khalife, Mariusz Szymanek, Agata Dziwulska-Hunek
Summary: In this study, the IR-HA drying kinetics of parboiled hull was modeled and predicted using three different models: ANFIS, ANN, and SVR. The results showed that higher inlet air temperature and IR power led to shorter drying time. Among the three models, SVR performed the best in terms of prediction performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
Danesh Mirzaei, Ali Behbahaninia, Ashkan Abdalisousan, Seyed Mohammadreza Miri Lavasani
Summary: This study focuses on estimating repair time and time-dependent availability of gas turbine power plant components and subsystems based on human experience. Fuzzy logic and adaptive neuro-fuzzy inference system were used for simulation and prediction. The Monte Carlo simulation method was utilized to estimate the time-dependent availability for 20 years. The results revealed the critical components in the power plant units and indicated lower availability for the fuel system and lubrication system. The highest availability of 95% was estimated in the 16th year, while the lowest availability of 87% was predicted in the 19th year.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2023)
Article
Engineering, Marine
Duc-Anh Pham, Seung-Hun Han
Summary: Efficient ship guidance and fuel savings can be achieved through the integration of neural networks and fuzzy logic control in an intelligent control system. This study presents a ship autopilot system using the Adaptive Neural Fuzzy Inference System (ANFIS) that outperforms the traditional PID controller in terms of stability and trajectory accuracy.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Thermodynamics
Daniel Jia Sheng Chong, Yi Jing Chan, Senthil Kumar Arumugasamy, Sara Kazemi Yazdi, Jun Wei Lim
Summary: This study utilizes machine learning algorithms such as RSM, ANFIS, and ANN to model biogas production and methane yield in a local anaerobic covered lagoon. The models show high accuracy with R² up to 0.98. ANFIS has the highest prediction accuracy with the lowest MAE and RMSE values. Optimal conditions obtained through multi-objective optimization show increased biogas production and methane yield. pH is identified as the most influential factor on methane yield through sensitivity analysis.
Article
Mathematics, Applied
Aditya Khamparia, Rajat Jain, Poonam Rani, Deepak Gupta, Ashish Khanna, Oscar Castill
Summary: The study aims to design a system for diagnosing COVID-19 using ANFIS, and comparative analysis reveals that ANFIS model outperforms fuzzy systems in accuracy.
APPLIED AND COMPUTATIONAL MATHEMATICS
(2021)
Article
Environmental Sciences
Seyyed Ahmad Naghibi, Ehsan Salehi, Mohammad Khajavian, Vahid Vatanpour, Mika Sillanpaa
Summary: In this study, machine learning methods were applied to assess batch adsorption of MG dye on CPZ membrane adsorbents. ANFIS was found to be more effective than the ANN approach for predicting adsorption performance, with a RMSE of 0.01822 and R-square of 0.9958. Sensitivity analysis revealed that residence time plays a crucial role in removal efficiency. This study demonstrates the effectiveness of ANFIS in optimizing membrane adsorption processes.
Article
Green & Sustainable Science & Technology
Mehmet Akif Koc, Ramazan Sener
Summary: The study developed an intelligent software based on adaptive neural-fuzzy inference systems to predict the emission and performance values of a reactivity-controlled compression ignition engine fueled with natural gas and diesel under different operating conditions. The software successfully predicted the engine performance and emission parameters, showing high accuracy in comparison with artificial neural networks.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Energy & Fuels
Mohammad Hossein Ahmadi, Hamidreza Jashnani, Kwok-Wing Chau, Ravinder Kumar, Marc A. Rosen
Summary: In this study, an Artificial Neural Network (ANN) approach called Group Method of Data Handling (GMDH) was used to model carbon dioxide emissions based on shares of energy sources and GDP in five countries: Iran, Kuwait, Qatar, Saudi Arabia, and UAE. The results showed that the ANN model accurately predicted CO2 emissions, with an average absolute relative error of 2.3% and an R-squared value of 0.9998.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2023)
Article
Green & Sustainable Science & Technology
Mohammad Ehteram, Ali Najah Ahmed, Chow Ming Fai, Sarmad Dashti Latif, Kwok-wing Chau, Kai Lun Chong, Ahmed El-Shafie
Summary: This research aims to optimize water release for optimal hydropower generation in the future. The results show that temperature will increase while precipitation will decrease. The adoption of adaptive rule curves can improve hydropower generation and reduce vulnerability in the future period.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Geography
Senlin Zhu, Qingfeng Ji, Mariusz Ptak, Mariusz Sojka, Abdalsamad Keramatfar, Kwok Wing Chau, Shahab S. S. Band
Summary: This study combines GRU and LSTM deep learning models with attention mechanisms for the first time to forecast daily water level in lakes. The results show that GRU performs the best among the deep learning models in different lakes, and the performance of deep learning models improves as the prediction horizon increases.
GEOGRAPHICAL JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Wen-chuan Wang, Lei Xu, Kwok-wing Chau, Chang-jun Liu, Qiang Ma, Dong-mei Xu
Summary: This paper proposes a lightweight and efficient variant of differential evolution algorithm, Ce-LDE, for solving constrained single-objective optimization problems. The algorithm achieves high competitiveness and practicality through the introduction of a combined constraint handling method and redefinition of control parameters, as demonstrated by experimental results and comparative studies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Mahdi Ghasemi, Mehrshad Samadi, Elham Soleimanian, Kwok-Wing Chau
Summary: In this study, six data-driven models were used to predict landfill leachate permeability. The GMDH and ANN models showed the highest accuracy, with GMDH providing a simpler and more understandable mathematical expression for predicting permeability compared to ANN.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Dong-mei Xu, Xiang Wang, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang
Summary: In this study, a coupled forecasting model combining ICEEMDAN, WD, and SVM optimized by SOA is proposed to predict monthly runoff. The model decomposes the original runoff series using ICEEMDAN and WD to obtain IMF and Res components, which are then input into the SOA-SVM model for prediction. The ICEEMDAN-WD-SOA-SVM model achieves the smallest RMSE and MAPE and the largest NSEC and R compared to other benchmarking models, demonstrating its superior prediction accuracy.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Engineering, Civil
Wen-chuan Wang, Qi Cheng, Kwok-wing Chau, Hao Hu, Hong-fei Zang, Dong-mei Xu
Summary: Reliable runoff prediction is essential for reservoir scheduling, water resources management, and efficient water utilization. To improve the accuracy of monthly runoff prediction, a hybrid model (TVF-EMD-SSA-ELM) combining TVF-based EMD, SSA, and ELM is proposed. The model successfully decomposes the runoff series, optimizes the ELM model with SSA, and generates accurate predictions. Evaluation results show that the TVF-EMD-SSA-ELM model outperforms other models in terms of prediction accuracy. This model provides a new method for monthly runoff prediction and can be applied in similar study areas.
JOURNAL OF HYDROLOGY
(2023)
Article
Biochemistry & Molecular Biology
Vikas Mehta, Naresh Kumar, Ali Algahtani, Vineet Tirth, Tawfiq Al-Mughanam, Kwok-Wing Chau
Summary: Recently, research has shown the increasing importance of natural fiber in various industries such as medicine, aerospace, and agriculture. Natural fiber is preferred due to its eco-friendly nature and excellent mechanical properties. This study aims to increase the use of environmentally friendly materials, specifically in brake pads. By comparing natural fiber composites with Kevlar-based composites, it was found that composites with 5 wt.% sugarcane fiber outperformed the entire natural fiber composite in terms of coefficient of friction, fade, and wear. However, mechanical properties were similar. The study also found that the Kevlar-based brake pad specimens provided superior outcomes compared to the sugarcane fiber composite for fade, wear performance, and coefficient of friction. Scanning electron microscopy was used to examine the worn composite surfaces and understand the wear mechanisms and tribological behavior of the composites.
Review
Engineering, Civil
Wen-chuan Wang, Wei-can Tian, Dong-mei Xu, Kwok-wing Chau, Qiang Ma, Chang-jun Liu
Summary: River flood routing is a crucial aspect of water resources management, with the Muskingum model being the dominant method. This paper reviews the development and parameter estimation research status of the Muskingum model. The combination of mathematical techniques and evolutionary algorithms has shown promising results in recent years. The paper also provides an overview of accuracy evaluation criteria and research case data sets commonly used in the literature, and discusses challenges and future trends in Muskingum model research.
WATER RESOURCES MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Dong-mei Xu, Xiao-xue Hu, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang
Summary: This research provides a hybrid forecasting model to increase the precision of monthly runoff predictions. It applies complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to decompose the raw monthly runoff time series. The input-output relationships for all intrinsic mode functions (IMFs) are determined using Harris Hawks Optimization (HHO) algorithm to optimize least squares support vector machine (LSSVM) model. Evaluation indicators demonstrate the effectiveness of the proposed hybrid model in improving prediction accuracy.
EARTH SCIENCE INFORMATICS
(2023)
Article
Environmental Sciences
Wenchuan Wang, Weican Tian, Kwokwing Chau, Hongfei Zang, Mingwei Ma, Zhongkai Feng, Dongmei Xu
Summary: This paper proposes an improved bald eagle search algorithm (CABES) combined with epsilon-constraint method (epsilon-CABES) to tackle the complex reservoir flood control operation problem. Through simulations and comparisons with other algorithms, the superior performance of the CABES algorithm is verified. The results of the tests on single and multi-reservoir systems show that the epsilon-CABES method outperforms other methods in flood control scheduling.
Article
Agronomy
Ashkan Nabavi-Pelesaraei, Hassan Ghasemi-Mobtaker, Marzie Salehi, Shahin Rafiee, Kwok-Wing Chau, Rahim Ebrahimi
Summary: This article evaluates intelligent models for exergoenvironmental damage and emissions social cost in mushroom production through three machine learning methods. The results reveal that diesel fuel and compost are the main hotspots in terms of human health, ecosystems, and emissions. The economic analysis shows a total social cost of approximately $1035. The results of machine learning models indicate that the artificial neural network is the best topology for forecasting, while the adaptive neuro-fuzzy inference system has weaker prediction results compared to the artificial neural network. The support vector regression is selected as the best machine learning model.
Article
Computer Science, Interdisciplinary Applications
Wen-chuan Wang, Bo Wang, Kwok-wing Chau, Dong-mei Xu
Summary: In this study, a monthly runoff interval prediction method based on WOA-VMD-LSTM and non-parametric kernel density estimation is proposed to address the issue of conveying prediction uncertainty. The approach involves dividing the monthly runoff series into stable subsequences using VMD optimized by WOA, predicting each subsequence using LSTM, and obtaining final point predictions by superposition. Non-parametric kernel density estimation is then used to forecast the runoff interval and is compared with other models. Results show that this model has higher prediction accuracy and provides a useful reference for decision-makers in water resources management.
EARTH SCIENCE INFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Wenchuan Wang, Weican Tian, Kwok-wing Chau, Yiming Xue, Lei Xu, Hongfei Zang
Summary: The improved Bald Eagle algorithm (CABES) enhances the performance of the Bald Eagle Search algorithm (BES) by integrating Cauchy mutation and adaptive optimization. CABES adjusts the step size in the selection stage to select a better search range, and updates the search position formula with an adaptive weight factor to further improve the local optimization capability of BES. Experimental results demonstrate that CABES exhibits good exploration and exploitation abilities, making it effective and efficient in practical engineering problems.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Environmental Sciences
Zohreh Sheikh Khozani, Mohammad Ehteram, Wan Hanna Melini Wan Mohtar, Mohammed Achite, Kwok-wing Chau
Summary: This study introduces a new deep learning model that combines a convolutional neural network with a novel version of radial basis function neural network for predicting effluent quality parameters of a wastewater treatment plant. The model uses the salp swarm algorithm to optimize parameters and has shown robust performance in simulating complex phenomena.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
