Article
Thermodynamics
Chiazor Faustina Jisieike, Niyi Babatunde Ishola, Lekan M. Latinwo, Eriola Betiku
Summary: This study evaluated the modeling effectiveness of response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) in esterifying crude rubber seed oil (CRSO) with high free fatty acid (FFA). The ANFIS-PSO hybrid provided the best optimal conditions, resulting in the lowest FFA of 0.56%.
Article
Computer Science, Artificial Intelligence
Parminder Singh, Avinash Kaur, Ranbir Singh Batth, Sukhpreet Kaur, Gabriele Gianini
Summary: This paper proposes a BSO-ANFIS model for heart disease and multi-disease diagnosis, achieving high accuracy and precision by optimizing parameters and analyzing feature extraction. The results demonstrate the superiority of this algorithm over competitor models.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Mahdi Danesh, Sedigheh Danesh
Summary: This study introduces a new method for fuzzy parameter estimation using linear programming and a combination of PSO and ACOR algorithms. The proposed method shows higher accuracy in dealing with issues related to inhomogeneity, randomness, and imprecision, without significantly increasing computational complexity compared to traditional methods.
Article
Green & Sustainable Science & Technology
Oludamilare Bode Adewuyi, Komla A. Folly, David T. O. Oyedokun, Emmanuel Idowu Ogunwole
Summary: In this paper, a new prediction method for the voltage stability margin of power systems was proposed using machine learning techniques, based on the critical boundary index approach. The prediction models were developed using the adaptive neuro-fuzzy inference system and enhanced with particle swarm optimization. The performances of the models were evaluated on standard IEEE 30-bus and Nigerian 28-bus systems, with the PSO-ANFIS model showing superior performance in reducing prediction errors, albeit with higher simulation time.
Article
Automation & Control Systems
Aamer Bilal Asghar, Khazina Naveed, Gang Xiong, Yong Wang
Summary: In this paper, a hybrid intelligent learning based adaptive neuro-fuzzy algorithm is proposed for pitch angle scheduling of wind turbines. The algorithm trains the parameters of fuzzy membership functions using artificial neural networks, resulting in a more efficient control method.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Chemistry, Physical
Mehmet Hakan Demir, Berkay Eren
Summary: An optimization-based neuro-fuzzy inference controller is proposed to improve the voltage tracking performance of microbial fuel cells (MFC). The results show that controllers based on Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) have efficient performances in quickly responding to reference voltage patterns and robustly handling external load changes.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Thermodynamics
Boudy Bilal, Kondo Hloindo Adjallah, Alexandre Sava, Kaan Yetilmezsoy, Emel Kiyan
Summary: This study introduces an original adaptive neuro-fuzzy inference system modeling approach to predict wind turbine output power. The proposed model demonstrates higher accuracy in estimating power output compared to existing approaches, showcasing superior forecasting performance and accuracy. Multiple experiments validate the effectiveness and reliability of the proposed methodology.
Review
Transportation
Matthew Vechione, Ruey Long Cheu
Summary: This study investigates the adaptation of Fuzzy Inference System (FIS) model for mandatory lane changing decisions and compares its performance with Adaptive FIS (AFIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) models on a test data set. Results suggest that an ANFIS model is recommended for mandatory lane changes due to its higher overall correct decision rate.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Mechanics
Mahdi Shariati, Seyed Mehdi Davoodnabi, Ali Toghroli, Zhengyi Kong, Ali Shariati
Summary: Steel-Concrete Composite floor systems are crucial in construction, but fire-induced issues can damage connectors and alter system behavior. This paper introduces a soft computing approach to predict connector behavior at elevated temperatures, demonstrating superior performance with the ANFIS-PSO-GA model.
COMPOSITE STRUCTURES
(2021)
Article
Green & Sustainable Science & Technology
Hossein Rajabi Kuyakhi, Ramin Tahmasebi Boldaji
Summary: This study successfully developed a predictive model for the removal of Cr(VI) on NiO nanoparticles using the ANFIS-PSO algorithm, showing superior performance. The analysis of four initial parameters on the removal of Cr(VI) was conducted to assess the model's effectiveness.
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2021)
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
Computer Science, Information Systems
Bharat Bhushan Sharma, Naveen Kumar Sharma, Anuj Banshwar, Hasmat Malik, Fausto Pedro Garcia Marquez
Summary: This paper presents a new method for designing matched digital filters with discrete valued coefficients using the fuzzy particle swarm optimization vector quantization (FPSOVQ) algorithm. The approach utilizes fuzzy inference method and expert particle swarm optimization to generate an optimal codebook for compression of data. The results demonstrate the advantage of the developed algorithm in terms of energy compaction ratio for sampled voice signals when compared to a db4 filter.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kishore Balasubramanian, N. P. Ananthamoorthy
Summary: This study presents a predictive model for detecting neurodegenerative diseases and cancer, focusing on enhancing the efficiency of the adaptive neuro-fuzzy inference system. Experimental results demonstrate the superior performance of the DE-GSO-ANFIS in predicting medical disorders.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Thermodynamics
Boudy Bilal, Kondo Hloindo Adjallah, Alexandre Sava, Kaan Yetilmezsoy, Mohammed Ouassaid
Summary: This study predicts the output power of wind turbines (WTs) by using wind speed and WT operational characteristics. The proposed model identification method, based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window, outperforms existing ANFIS-based models in accurately estimating the WT output power.
Article
Energy & Fuels
Abrar Ahmed Chhipa, Vinod Kumar, Raghuveer Raj Joshi, Prasun Chakrabarti, Michal Jasinski, Alessandro Burgio, Zbigniew Leonowicz, Elzbieta Jasinska, Rajkumar Soni, Tulika Chakrabarti
Summary: This study introduces a novel MPPT controller that maximizes wind energy extraction using a combination of neural network and fuzzy inference system, which can enhance the performance of wind energy conversion systems in different wind speed conditions.
Article
Thermodynamics
A. Khosravi, T. Laukkanen, V. Vuorinen, S. Syri
Summary: The city of Espoo, Finland is planning to develop Kera as a green suburb with high energy efficiency and low CO2 emissions. The study found that the heat pump scenario is an efficient and cost-effective way to retrieve waste heat from the data center and 5G smart poles.
Article
Computer Science, Interdisciplinary Applications
Mohammad Malekan, Ali Khosravi, Luc St-Pierre
Summary: Fatigue crack propagation is crucial in evaluating the design life of engineering components. This paper introduces a freely distributed plug-in for simulating fatigue crack growth with the commercial FE code Abaqus, covering five different fatigue crack growth models and validated through comparisons with analytical and experimental results. The simplicity and free distribution of the plug-in make it a useful simulation tool for industrial, research, and educational purposes.
ENGINEERING WITH COMPUTERS
(2022)
Review
Green & Sustainable Science & Technology
Juan J. Garcia-Pabon, Dario Mendez-Mendez, Juan M. Belman-Flores, Juan M. Barroso-Maldonado, Ali Khosravi
Summary: This paper provides a comprehensive review on the use of R1234yf refrigerant and its mixtures as working fluids in ORC systems, showing that these fluids are competitive in utilizing residual energy for electricity generation.
Article
Thermodynamics
A. Khosravi, A. Santasalo-Aarnio, S. Syri
Summary: The study found that solar thermal collectors can effectively reduce fuel consumption and decrease the levelized cost of energy. Increasing solar irradiance can enhance the energy efficiency gains of a hybrid system. In comparisons, the proposed hybrid system performed the best in terms of levelized cost of energy.
Article
Thermodynamics
Ruilin Yin, Li Sun, Ali Khosravi, Mohammad Malekan, Yixiang Shi
Summary: This paper develops a dynamic model of the solid oxide electrolysis system, investigates the effects of temperature and current density on the electrolytic voltage, and discusses the energy consumption under different operation modes. Also, steady-state energy and exergy flow diagrams are used to find the optimal conditions with maximum system efficiency. The dynamic simulation of the stack temperature demonstrates the response rapidity, strong couplings, and different variation trends of variables.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Review
Energy & Fuels
Seyed Ali Kashani, Alireza Soleimani, Ali Khosravi, Mojtaba Mirsalim
Summary: In the past decade, there has been a growing trend towards utilizing green energies, such as solar power, in combination with electric vehicles to address the issues of nonrenewable fuel sources and pollution. This research focuses on wireless power transfer systems for charging electric vehicle batteries using solar panels as a power source. The study explores different types of solar-powered electric vehicle charging stations and investigates various components and coil structures within the wireless power transmission framework. Additionally, the use of artificial intelligence in the wireless power transfer systems is discussed, emphasizing the importance of developing AI models.
Article
Energy & Fuels
Guanru Li, Qingsong Hua, Li Sun, Ali Khosravi, Juan Jose Garcia Pabon
Summary: This paper presents a mathematical model for the hybrid LCPV-MPTL system to address the inefficient thermal management issue. Simulations and analysis show that the proposed system can improve the overall performance of PV and control the cell temperature effectively.
Article
Energy & Fuels
Jaan Ronkko, Ali Khosravi, Sanna Syri
Summary: This study focuses on the development of a hybrid wind-wave energy system and assesses its performance using a techno-economic model. Different combinations of offshore wind turbines and wave energy converters are compared to find the most cost-efficient pairing. The study finds that a combination of 160 MW of wind power and 40 MW of wave power has the lowest production cost when the shared costs are 15%.
Article
Chemistry, Physical
Lei Xia, Ali Khosravi, Minfang Han, Li Sun
Summary: This study proposes an intelligent optimization framework using an artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) to optimize the complex structure of the three-dimensional Reticulated trapezoidal flow field (RTFF), aiming to improve the performance and durability of solid oxide fuel cells (SOFCs).
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Thermodynamics
Qingshan Li, Qingsong Hua, Chenfang Wang, Ali Khosravi, Li Sun
Summary: This article introduces an innovative off-grid photovoltaic proton exchange membrane electrolytic cells hydrogen production system, which integrates with an organic Rankine cycle system to solve the instability issue caused by solar energy. The system is suitable for retrofitting photovoltaic plants, with a payback period of 12 years, showing higher profitability than grid-supplied hydrogen production.
APPLIED THERMAL ENGINEERING
(2023)
Review
Energy & Fuels
Ali Khosravi, Fanni Saamaki
Summary: This paper evaluates the energy consumption of various cryptocurrencies and compares them with traditional payment methods. The study shows significant differences in energy use among cryptocurrencies, with some newer digital coins having energy footprints similar to conventional payment methods. Despite challenges in estimating CO2 emissions, many cryptocurrencies, especially beyond Bitcoin, register considerably lower emissions.
Article
Green & Sustainable Science & Technology
Haleh Delnava, Ali Khosravi, Mamdouh El Haj Assad
Summary: Solar energy is a promising energy source that significantly reduces greenhouse gas emissions compared to fossil fuels. In this study, a meta frontier framework is used to estimate US solar energy performance in 2019 using stochastic non-parametric envelopment of data (StoNED) under convex and non-convex frameworks. The results indicate the need for a multifaceted approach to ensure energy supply and explore alternative options such as adapting panels for specific conditions.
Article
Thermodynamics
Tero Koivunen, Ali Khosravi, Sanna Syri
Summary: To combat climate change, decarbonization measures are undertaken across various sectors, including industry and transportation. This study examines the impact of introducing hydrogen into a Finnish energy system in 2040, using scenario simulations. The results show that introducing hydrogen can significantly reduce CO2 emissions and fossil energy consumption, but additional measures are needed to ensure carbon neutrality, as biomass consumption remains high.
Article
Thermodynamics
Yashar Aryanfar, Mamdouh El Haj Assad, Ali Khosravi, Rahman S. M. Atiqure, Shubham Sharma, Jorge Luis Garcia Alcaraz, Reza Alayi
Summary: A renewable energy source, particularly solar energy, is an excellent option for power generation in rural areas. The combination of the organic Rankine cycle (ORC) and vapor compression cycle (VCC), powered by a parabolic trough solar collector, is investigated in this study for a combined power generation and cooling system. Thermodynamic and economic simulations are conducted for four different working fluids, and it is concluded that the thermal efficiency of the power plant can be increased using the combined ORC-VCC system. The study also discusses the impact of thermodynamic parameters on system performance and provides optimal design values.
INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES
(2022)
Article
Thermodynamics
Mamdouh El Haj Assad, Yashar Aryanfar, Amirreza Javaherian, Ali Khosravi, Karim Aghaei, Siamak Hosseinzadeh, Juan Pabon, S. M. S. Mahmoudi
Summary: The increasing demand for energy worldwide has led to the search for alternative energy sources such as geothermal, solar, and wind power which are considered clean and sustainable. Through energy and exergy analysis, it has been found that a combined power plant utilizing geothermal energy is more efficient and produces more power than a standalone geothermal power plant.
INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES
(2021)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
