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Article
Computer Science, Artificial Intelligence
Zhan Li et al.
Summary: The problem of fuel reloading optimization requires searching for the optimal core configuration in a huge solution space. The use of meta-heuristic algorithms and artificial neural networks has been effective in updating optimization solutions and evaluating core configurations respectively, but there is little research on the application of convolution neural networks. Aiming to solve the problems in fuel reloading optimization for block-type HTGRs, triangle-filter convolution neural networks are developed and applied with genetic algorithm and particle swarm optimization. The results show that the TFCNNs can accurately predict optimization parameters and have a faster calculation time compared to DRAGON V4 program and a higher ratio of feasible solutions compared to BP-ANNs.
APPLIED SOFT COMPUTING
(2023)
Article
Thermodynamics
Long Liu et al.
Summary: In recent years, there has been significant interest in reducing global carbon emissions, leading to stricter policies and regulations. For high power-density marine diesel engines, optimizing fuel consumption is difficult due to the large injection mass and little intake swirl. This article proposes a concept of rapid and controllable combustion to address this issue, which involves optimizing the combustion process based on the Sabathe-Miller cycle and determining controllable parameters through the evolution of in-cylinder spray.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Energy & Fuels
Adeyemi Emman Aladejare et al.
Summary: This paper describes the development of metaheuristic based artificial neural network (ANN-PSO) and multilinear regression models for predicting the higher heating values (HHV) of solid fuels. The ANN-PSO models outperformed the multilinear regression models in terms of predictive performance. They showed excellent ability in predicting the HHV of solid fuels for practical applications.
INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
(2022)
Article
Engineering, Chemical
Fatih Gulec et al.
Summary: A novel method called Kalman filter was investigated to predict the pyrolysis mechanisms of four different biomasses and compared with regression analysis. The models with reversible reactions in addition to parallel pyrolysis steps showed a better fit with the experimental results. The pyrolysis step from biomass to bio-oil exhibited the highest reaction rates compared to other defined pyrolysis steps in the models. Kalman filter is considered a promising method for estimating detailed pyrolysis mechanisms and model parameters with minimum experimental data.
CHEMICAL ENGINEERING & TECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Vasiliki P. Aravani et al.
Summary: Greece and China, as agricultural countries, face challenges in managing agricultural and livestock wastes. The amount of waste generated in China is significantly larger than that in Greece. Estimates suggest that these wastes have the potential to supply a significant portion of the energy needs for both countries.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Engineering, Environmental
Lizette De la Pena et al.
Summary: The transition from fossil fuels to renewable energy systems is crucial for developing countries to achieve carbon neutrality and address energy security issues. In the case of Mexico, gradual and rapid transition scenarios have been developed, with the latter showing potential for reducing CO2 emissions and moving closer to carbon neutrality by 2050. The study emphasizes the importance of prioritizing renewable energy sources, energy efficiency improvements, and coal phase-out to align with global efforts in combating climate change.
RESOURCES CONSERVATION AND RECYCLING
(2022)
Article
Biochemical Research Methods
Haoyu Zhang et al.
Summary: This study presents a novel model based on sequence distance matrix and support vector machine (SVM) for predicting DNA 6mA modification. The model achieved high accuracy rates and correlation coefficients on rice and mouse data, showing significant advantages over traditional machine learning methods.
CURRENT BIOINFORMATICS
(2022)
Article
Environmental Sciences
Peng Wang et al.
Summary: This study established a framework for assessing the potential of rooftop photovoltaic (PV) energy-saving in old residential buildings in Nanjing City, China. The results showed the electricity generation potential, carbon reduction potential, and economic potential of rooftop PV. The findings can provide valuable insights for the assessment and planning of rooftop PV in other cities in China.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Energy & Fuels
Fatih Gulec et al.
Summary: This study applies artificial neural network (ANN) models to predict the higher heating value (HHV) of biomass feedstocks and comprehensively analyzes the factors that affect the prediction, including activation functions, algorithms, hidden layers, dataset, and randomization. The results show that using ANN models trained by the combination of ultimate-proximate analyses (UAPA) datasets, with sigmoidal activation functions (tansig and logsig), and with Levenberg-Marquardt (lm) or Bayesian Regularization (br) algorithms as training activation functions, can provide accurate HHV prediction.
Review
Energy & Fuels
Shivangi Jha et al.
Summary: This article provides insights into the fundamental and applied concepts of biofuels and their production and application methods, including thermochemical conversion methods, the properties of biomass resources, and different types of biofuels. The article also analyzes the strengths, weaknesses, opportunities, and threats of thermochemical conversion technologies.
Article
Green & Sustainable Science & Technology
Long Liu et al.
Summary: Ammonia is a promising carbon-free alternative fuel for reducing carbon emissions in marine engines. The study found that ammonia-diesel stratified injection technology can significantly reduce CO2 and NOx emissions, improve energy ratio, and meet future low-carbon emission regulations without the need for after-treatment. Increasing injection pressure and reducing pilot fuel proportion is the optimal solution for achieving high IMEP, low NOx, and carbon emissions.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Chemistry, Multidisciplinary
Wei Dang et al.
Summary: Machine sense of smell plays an important role in various scenarios, but it relies on data and algorithms for support. This study proposes a semi-supervised extreme learning machine algorithm, SELMWK, which combines weighted kernel with SKELM to handle a small amount of labeled data and a large amount of unlabeled data, achieving good classification performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Zhongwen Cao et al.
Summary: This paper proposes an adaptive neural control strategy for switched pure-feedback non-linear systems based on neural networks and prescribed performance control method. The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, the restrictive differentiability assumption on non-affine functions is removed to obtain more general results.
ASSEMBLY AUTOMATION
(2022)
Article
Multidisciplinary Sciences
Saleh Hosseini et al.
Summary: The study accurately determines methanol loss in a three-phase separator using intelligent connectionist approaches like least-squares support vector machines (LS-SVM). The LS-SVM model shows excellent consistency with real-field datasets and the economic impact on gas processing plants is also evaluated.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Energy & Fuels
Satyajit Pattanayak et al.
Summary: India has abundant bamboo biomass resources, especially in the north-east states. Three artificial neural network models have been developed to accurately predict the higher heating values of bamboo biomass, with the model based on combined proximate-ultimate analysis showing the best accuracy. The ANN models outperform correlation-based models in predicting the actual HHVs with an average error of 2.49%.
BIOMASS CONVERSION AND BIOREFINERY
(2021)
Article
Energy & Fuels
Zhiyuan Si et al.
Summary: This paper proposes a novel method based on satellite images to accurately forecast photovoltaic power by predicting cloud movement and dynamically selecting cloud regions. By utilizing the XGBoost algorithm and considering multiple factors, the accuracy of the forecast can be improved, as demonstrated through testing the effectiveness of the method compared to other benchmarks.
Article
Energy & Fuels
Maria Margarida Mateus et al.
Summary: This study developed a model based on a dataset of 54 samples to correlate carbon, hydrogen, and oxygen content with HHV for biomass conversion into liquid fuels. The model was compared with others in literature and validated with nine samples, showing excellent prediction accuracy. The model demonstrated good precision and performance with a low absolute bias error of 0.01% and absolute average error of 1.48%.
Article
Green & Sustainable Science & Technology
Peng Li et al.
Summary: This article introduces a confidence interval based distributionally robust real-time economic dispatch (CI-DRED) approach, which addresses the risk associated with accommodating wind power. By developing a novel ambiguity set based on imprecise probability theory and transforming the original nonlinear dispatch problem into a determined mixed integer linear programming problem, the proposed method effectively balances operational costs and risks. Numerical results on both the IEEE 118-bus system and a real 445-bus system demonstrate the efficiency and effectiveness of the approach.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Engineering, Multidisciplinary
Mohsen Karimi et al.
Summary: Biochar is a promising spectrum for thermal science applications in environmental protection. This study used artificial intelligence techniques to determine the heat capacity of biochar, with LS-SVM method identified as the best paradigm and showing excellent accuracy in statistical analysis. The developed LS-SVM approach outperforms the existing empirical correlation in literature by more than 700 times.
Article
Green & Sustainable Science & Technology
Salah Yahya et al.
Summary: This study compares the accuracy of different empirical and intelligent paradigms for estimating biodiesel-diesel blends, and determines that the LSSVM with a polynomial kernel is the most accurate approach. The designed model estimated the kinematic viscosity of 636 biodiesel-diesel blends with high accuracy.
Article
Green & Sustainable Science & Technology
Abolfazl Sajadi Noushabadi et al.
Summary: The utilization of biomass fuels as a potential renewable energy is favored for its advantages in sustainable economy and environment. Various accurate correlation methods like multivariate nonlinear regression and genetic algorithm-radial basis function are effective in estimating the higher heating value (HHV) of biomass fuels. The study provides reliable results that can be used by researchers to design and optimize biomass combustion systems.
Article
Engineering, Industrial
Xiaofeng Xu et al.
Summary: The study reveals the crucial significance of oil-gas recovery in refined oil distribution, which can reduce environmental pollution and economic losses while improving efficiency. Therefore, conducting oil-gas recovery in closed mode is necessary, and the related research and algorithm design both have a positive impact on GVRP.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2021)
Article
Thermodynamics
Krishna Veer Singh et al.
Summary: This paper focuses on optimal energy sharing in a hybrid electric vehicle (HEV) between the internal combustion engine and battery-powered electric motor. Fuzzy logic and Elman neural network-based adaptive energy management strategies (EMS) were designed and implemented, leading to higher fuel economy, faster response, and improved vehicle speed control. The system behavior was validated using CHIL testing platform.
Article
Energy & Fuels
Cheng Qian et al.
Article
Green & Sustainable Science & Technology
Yuanzhou Zheng et al.
Article
Thermodynamics
Soleiman Hosseinpour et al.
Article
Energy & Fuels
Ayhan Demirbas
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2017)
Review
Green & Sustainable Science & Technology
Athanasios Dimitriadis et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2017)
Article
Engineering, Chemical
Babak Aghel et al.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2017)
Article
Agricultural Engineering
Harun Uzun et al.
BIORESOURCE TECHNOLOGY
(2017)
Article
Energy & Fuels
Ebru Akkaya
Review
Energy & Fuels
Stanislav V. Vassilev et al.
Article
Energy & Fuels
S. B. Ghugare et al.
BIOENERGY RESEARCH
(2014)
Article
Agricultural Engineering
C. Telmo et al.
BIOMASS & BIOENERGY
(2011)
Article
Thermodynamics
Wanignon Ferdinand Fassinou et al.
Article
Energy & Fuels
A. K. Majumder et al.
