Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Xiangjuan Yao et al.
Summary: This article proposes a method to solve large-scale many-objective optimization problems (LSMaOPs) based on dimension reduction and a solving knowledge-guided evolutionary algorithm (KGEA). The method effectively reduces the dimension of the original problem by clustering and aggregating the objective functions, and then solves the reduced problem using the solving KGEA. Experimental results demonstrate the effectiveness of the proposed algorithm in tackling LSMaOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Energy & Fuels
Batyr Orazbayev et al.
Summary: The study aims to optimize the operating modes of a catalytic reforming unit using fuzzy information through the development of a system of models. The research methods include system analysis, mathematical modeling, multicriteria optimization, expert assessments, and fuzzy set theories. The results show the effectiveness of the proposed fuzzy approach for improving the efficiency of the facility.
Article
Energy & Fuels
Haipeng Wang et al.
Summary: The new quantitative calibration algorithm PLSRR-ELM utilizes the advantages of partial least squares (PLS) and non-linear extreme learning machine (ELM) to effectively handle relationship information, leading to improved calibration performance for determining the research octane number (RON) of blended gasoline.
Article
Thermodynamics
Yanqing Cui et al.
Summary: A prediction model for the ignition delay of three-component surrogates was established using a BP neural network, showing that a BP neural network with two hidden layers performs better in predicting ignition delay. Through optimization with genetic algorithm and particle swarm optimization algorithm, a high correlation coefficient was achieved with low mean square error. The analysis of mean impact value revealed the major and minor effects of ambient temperature and molar fraction of iso-octane on ignition delay.
COMBUSTION AND FLAME
(2022)
Article
Chemistry, Analytical
Yuxiang Hou et al.
Summary: This paper proposes an improved GWO algorithm to address the issues of instability and convergence accuracy in mobile robot path planning when GWO is used as a meta-heuristic algorithm. By enhancing the initialization method, convergence factor, and weighting strategy, the improved GWO algorithm demonstrates higher accuracy and faster convergence speed in experiments.
Article
Computer Science, Information Systems
Prabhjot Singh et al.
Summary: In this research, a combination of range-based and range-free localization strategies, along with fuzzy logic, is used to determine the location of unknown sensors in a three-dimensional scenario using a single anchor node. By applying an Adaptive SSA technique and other meta-heuristic algorithms, it is demonstrated that the range-based localization techniques are more efficient than the range-free techniques.
Article
Thermodynamics
Fengyu Zhang et al.
Summary: In this study, a machine learning model was developed to handle highly non-linear and coupling relationships, and 35 important variables were selected for modeling out of 353 variables. The dragonfly algorithm was used to optimize the back propagation neural network and logistics regression process, resulting in a combined model that balanced local and global searching. The evaluation and validation of the model showed high accuracy and strong generalization ability, making it essential for gasoline refining.
Article
Energy & Fuels
Qiuxia Chen et al.
Summary: This paper studies the design of a multi-energy supply system based on the adaptive improved genetic algorithm for the intelligent control system of agricultural greenhouses. By intelligent control of agricultural greenhouses, production efficiency can be improved and deviations in temperature, humidity, and carbon dioxide can be reduced. The improved genetic algorithm is an effective optimization method.
Article
Computer Science, Information Systems
Bingwei Gao et al.
Summary: This paper proposes an improved evolutionary sparrow search algorithm to address the issues of poor global search ability, weak local development ability, and easy fall into the local optimal solution. By introducing the chaotic map, random search ability, and mutation evolution operation, the algorithm shows superior performance in optimization ability, robustness, convergence ability, and optimization trajectory. The algorithm is also applied to parameter identification and control strategies, demonstrating its advantages in practical engineering applications.
Article
Computer Science, Artificial Intelligence
Shaoze Cui et al.
Summary: The study proposes a method for predicting RON loss in gasoline refining process, including feature selection and stacking heterogeneous ensemble model. Experimental results show that the method is more accurate than other machine learning methods and can promote the development of the gasoline refining industry.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Ahmedbahaaaldin Ibrahem Ahmed Osman et al.
Summary: This study aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations, serving as a great benchmark for future groundwater levels prediction.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Chemistry, Analytical
Guiyun Liu et al.
Summary: This paper presents a modified sparrow search algorithm named CASSA to solve the UAV route planning problem. By introducing a chaotic strategy, adaptive inertia weight, and Cauchy-Gaussian mutation strategy, the algorithm shows improved efficiency and diversity. Simulation results demonstrate that CASSA outperforms other algorithms in generating routes in the same environment.
Article
Green & Sustainable Science & Technology
Xiaomin Xu et al.
Summary: The prediction of power grid engineering cost is crucial for fine management, and accurate prediction of substation engineering cost is essential to ensure the smooth operation of engineering funds. The use of SSA-BP model has shown to effectively improve prediction accuracy by optimizing BP neural network parameters.
Article
Engineering, Civil
Ji Guo et al.
Summary: This paper proposes a method for optimizing gasoline octane loss using data analysis technology and neural network prediction model to achieve optimal operating conditions, reduce octane loss, ensure emission compliance, provide reference data for optimizing cracking gasoline processes, and save economic costs for chemical enterprises.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Computer Science, Artificial Intelligence
Chenglong Zhang et al.
Summary: A stochastic configuration network model, CSSA-SCN, based on chaotic sparrow search algorithm is introduced in this paper to enhance the performance of SCN by optimizing network parameters. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Chemical
Xiao Liu et al.
Summary: A new systematic method was proposed to determine an optimal operation scheme for minimizing RON loss and operational risks. Through data collection, dimensionality reduction, and multiple nonlinear regression, a multi-objective nonlinear optimization model was established with the objective of maximizing the reduction in RON loss and minimizing operational risks.
Article
Computer Science, Interdisciplinary Applications
Chuang Chen et al.
Summary: An intelligent selection and optimization method of feature variables is proposed to suppress the research octane number (RON) loss in the gasoline refining process. By calculating the importance of main variables and establishing a nonlinear mapping relationship, the optimal values of feature variables are obtained through continuous iterative solution.
COMPUTERS & CHEMICAL ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Mohammad-Reza Mohammadi et al.
Summary: This study implemented five robust machine learning models to accurately estimate the hydrogen solubility in hydrocarbons, with the XGBoost model showing the best performance and potential application in the chemical and petroleum industries.
SCIENTIFIC REPORTS
(2021)
Article
Mathematical & Computational Biology
Chengtian Ouyang et al.
Summary: This paper proposes an improved learning sparrow search algorithm, which enhances the algorithm's performance and robustness in optimization problems by introducing lens reverse learning strategy and differential-based local search strategy.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Ningchen Fu et al.
Summary: The octane number is a key indicator in crude oil processing and its reduction in gasoline refining process is closely related to economic benefits. The proposed RON prediction model combining random forest algorithm, BP neural network and genetic algorithm is effective and meets actual industrial application requirements.
NEW JOURNAL OF CHEMISTRY
(2021)
Article
Computer Science, Information Systems
Baosheng Li et al.
Summary: This research proposes a new analytical framework to predict octane number, which can improve product quality through dimension reduction, model establishment, and parameter optimization. Key attributes affecting product quality are identified, enabling engineers to adjust operational variables to obtain high-quality products.
Article
Engineering, Electrical & Electronic
Lulu Wen et al.
ELECTRIC POWER SYSTEMS RESEARCH
(2020)
Article
Green & Sustainable Science & Technology
Wei Sun et al.
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Engineering, Mechanical
Qinglin Liu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Automation & Control Systems
Jiankai Xue et al.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2020)
Article
Energy & Fuels
Runzhao Li et al.
Review
Computer Science, Artificial Intelligence
Mohammed Elbes et al.
EVOLUTIONARY INTELLIGENCE
(2019)
Article
Statistics & Probability
Alexander Ly et al.
STATISTICA NEERLANDICA
(2018)
Article
Automation & Control Systems
Pablo C. Giordano et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2017)
Proceedings Paper
Physics, Applied
Qiong Ren et al.
ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS I
(2017)
Proceedings Paper
Engineering, Multidisciplinary
Shanshan Fang et al.
13TH GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT
(2017)
Article
Computer Science, Artificial Intelligence
Haomiao Zhou et al.
Article
Chemistry, Analytical
Nikos Pasadakis et al.
ANALYTICA CHIMICA ACTA
(2006)
Article
Chemistry, Applied
N Pasadakis et al.
FUEL PROCESSING TECHNOLOGY
(2006)
Article
Chemistry, Applied
CS Song