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Chinese Physics C 2022年第7期46卷 27-40页

作者： D.M.Habashy Mahmoud Y.El-Bakry Werner Scheinast Mahmoud Hanafy Ain Shams University Faculty of Education Physics Department11771RoxyCairoEgypt Joint Institute for Nuclear Research-Veksler and Baldin Laboratory of High Energy Physics Moscow Region141980 DubnaRussia Physics Department Faculty of ScienceBenha University13518BenhaEgypt

The entropy per rapidity dS/dy produced in central Pb-Pb ultra-relativistic nuclear collisions at LHC energies is calculated using experimentally identified particle spectra and source radii estimated from Hanbury Brown-Twiss (HBT) correlations for particles π, k, p, Λ, Ω, and ∑ and π, k, p, Λ, and K^(0)_(s) at √s =2.76 and 5.02 TeV, respectively. An artificial neural network (ann) simulation model is used to estimate the entropy per rapidity dS/dy at the considered energies. The simulation results are compared with equivalent experimental data, and a good agreement is achieved. A mathematical equation describing the experimental data is obtained. Extrapolation of the transverse momentum spectra at pT =0 is required to calculate dS/dy;thus, we use two different fitting functions, the Tsallis distribution and hadron resonance gas (HRG) model. The success of the ann model in describing the experimental measurements leads to the prediction of several spectra values for the mentioned particles, which may lead to further predictions in the absence of experiments.

关键词： HRG Tsallis ann RPropp

A New Hybrid SARFIMA-ann Model for Tourism Forecasting

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Computers, Materials & Continua 2022年第6期71卷 4785-4801页

作者： Tanzila Saba Mirza Naveed Shahzad Sonia Iqbal Amjad Rehman Ibrahim Abunadi Artificial Intelligence&Data Analytics Research Lab(AIDA) CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Statistics University of GujratGujrat50700Pakistan Department of Statistics University of SialkotSialkot51310Pakistan

Many countries developed and increased greenery in their country sights to attract international *** planning is now significantly contributing to their *** next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately *** accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal *** traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with *** contrast,the artificial neural network(ann)model deals better with nonlinear time *** get a better forecasting result,this study combines the merits of the SARFIMA and the ann models and the purpose of the hybrid SARFIMA-ann ***,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and *** results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ann ***,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

关键词： SARFIMA hybrid model tourists’arrival forecasting ann

Latent Heat Prediction of Nano Enhanced Phase Change Material by ann Method

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Energy Engineering 2022年第3期119卷 847-861页

作者： Farzad Jaliliantabar Rizalman Mamat Sudhakar Kumarasamy College of Engineering Department of Mechanical EngineeringUniversiti Malaysia PahangKuantan26300Malaysia Automotive Engineering Centre Universiti Malaysia PahangPekan26600Malaysia School of Mechanical Engineering Ningxia UniversityNingxia750000China Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia PahangPekan26600Malaysia Energy Center Maulana Azad National Institute of TechnologyBhopal462003India

Thermal characteristics of phase change material(PCM)are important in design and utilization of thermal energy storage or other *** have great latent heat but suffer from low thermal ***,in recent years,nano particles have been added to PCM to improve their thermophysical properties such as thermal *** of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate *** intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM(NEPCM).Then,in this study an artificial neural network(ann)has been developed to predict the latent heat of the NEPCM.A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network *** different types of Nano particle and paraffin based PCMs were used in ann *** most important properties which are used as the input for the developed ann model are NP size,density of NP,latent heat of PCM,density of PCM,concentration and latent heat of NEPCM in the range of 1-60 nm,100-8960 kg/m^(3),89.69-311 kJ/kg,760 to 1520 kg/m^(3),0.02-20 wt%and 60.72-338.6 kJ/kg,*** output variable was latent heat of *** result indicates that the ann model can be applied to predict the latent heat of nano enhanced PCM *** correlation coefficient of the created model was *** result shows ability of ann to predict the latent heat of NEPCM.

关键词： NEPCM ann latent heat prediction

Boiler combustion optimization based on ann and PSO-Powell algorithm

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Journal of Harbin Institute of Technology(New Series) 2009年第2期16卷 198-203页

作者：戴维葆邹平华冯明华董占双 School of Municipal and Environmental Engineering Harbin Institute of Technology Heilongjiang Electric Power Research Institute Heilongjiang Asia Power Xinbao Heating and Power Co. Ltd.

To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ann） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.

关键词： boiler combustion ann PSO-Powell algorithm multi-objective optimization section temperature field

Optimizing Biodiesel Production from Karanja and Algae Oil with Nano Catalyst:RSMand ann Approach

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Energy Engineering 2024年第9期121卷 2363-2388页

作者： Sujeet Kesharvani Sakhi Katre Suyasha Pandey Gaurav Dwivedi Tikendra Nath Verma Prashant Baredar Energy Centre Maulana Azad National Institute of TechnologyBhopal462003India Department of Mechanical Engineering Maulana Azad National Institute of TechnologyBhopal462003India

This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ann)model to optimize biodiesel *** of *** and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step *** reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar *** ann model outperforms RSM in yield prediction *** impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal *** analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy *** optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic ***,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation *** among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.

关键词： Non-edible oil algae RSM ann optimization environmental factor

Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ann-PSO approach

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Artificial Intelligence in Agriculture 2021年第1期5卷 1-12页

作者： C.M.Pareek V.K.Tewari Rajendra Machavaram Brajesh Nare Agricultural and Food Engineering Department Indian Institute of Technology KharagpurWest Bengal 721302India Indian Council of Agricultural Research-Central Potato Research Station JalandharPunjab 144026India

Uniformseed distribution within the row is the prime objective of precision planters for better crop growth and *** plate planters are generally used for sowing bold seeds likemaize,groundnut,chickpea,and their operating parameters like the forward speed of operation,the seedmetering plate inclination,and the seed level in the hopper affect the cell fill and subsequently the uniformseed ***,to achieve precise seed distribution,these parameters need to be *** the present study,out of the different optimization techniques,a new intelligent optimization technique based on the integrated ann-PSO approach has been used to achieve the set goal.A 3–5-1 artificial neural network(ann)model was developed for predicting the cell fill of inclined plate seedmetering device,and the particle swarmoptimization(PSO)algorithmwas applied to obtain the optimum values of the operating parameters corresponding to 100%cell *** most appropriate optimal values of the forward speed of operation,the seed metering plate inclination,and the seed level in the hopper for achieving 100%cell fill were found to be 3 km/h,50-degree,and 75%of total height,*** proposed integrated ann-PSO approach was capable of predicting the optimal values of operating parameters with amaximumdeviation of 2%compared to the experimental results,thus confirmed the reliability of the proposed optimization technique.

关键词： Precision planter Inclined plate seed metering device Cell fill percentage ann PSO

Thermal stability enhancement and prediction by ann model

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Energy and AI 2024年第2期16卷 125-136页

作者： Ziyu Liu Xiaoyi Yang School of Energy and Power Engineering Energy and Environment International CenterBeihang UniversityBeijing100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing100191China

Supersonic aircraft requires thermal endurance of aviation fuel in the process of cooling engine and aircraft. As the composition of petroleum-based jet fuel (RP-3) is confined by crude oil and refining process, sustainable alternative jet fuel with green house gas reduction become to undertake the composition optimization for improving thermal stability. For designing aviation fuel with robust thermal stability and the detail understanding of thermal stability mechanism, RP-3, Fischer–Tropsch fuel, and additives with cyclic structure for absorbing free radical, were investigated thermal stability by modifying different blend ratios under different conditions. Thermal endurance degree was assessed by chroma and deposition tendency. FT blend with cyclic hydrocarbon can improve thermal endurance degree. In compliance with individual optimized blend ratio, the contribution follows methyl cyclopentane > decalin > methyl cyclohexane > tetralin > n-propyl-benzene > 1,2,4 trimethyl-benzene. The appropriate blend ratio could undertake hydrogen donors for terminating the propagation of oxygen-carrying radical, but hydrocarbons with cyclic structure could enhance deposition tendency. Methyl cyclopentane and its oxidation derivatives take the roles of solvent by anti-polymerization and hydrogen donor by opening cyclic structure in the thermal endurance process, and thus lead to a wide range of blend ratio for improving significantly thermal stability. β-scission leading to C–C bond cleavage is the major reaction at the early decomposition stage, which resulted in most abundant derivatives plus C2. The effects of additives on thermal stability are complex and nonlinear on the tendency of thermal deposits and thermal endurance degree, and thus the appropriate ann-thermal stability model has been trained based on the experiment data and can achieve above 0.995 correlation coefficient. ann - thermal stability model can predict not only the content of derivatives including ester, o

关键词： Sustainable jet fuel Thermal stability ann Thermal endurance Fuel pyrolysis

AI Method for Improving Crop Yield Prediction Accuracy Using ann

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Computer Systems Science & Engineering 2023年第10期47卷 153-170页

作者： T.Sivaranjani S.P.Vimal Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical CampusCoimbatore642109India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India

Crop Yield Prediction(CYP)is critical to world food *** safety is a top priority for *** rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural *** Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their *** is a significant agrarian ***,CYP is the main task due to many composite factors,such as climatic conditions and soil *** Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific ***,Artificial Neural Networks(ann)are usually used to predict the behaviour of complex non-linear *** a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available ***,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction *** performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ann models that had been *** experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.

关键词： Crop prediction accuracy ann precision agriculture crop yield

Aluminum-zinc alloy squeeze casting technological parameters optimization based on PSO and ann

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China Foundry 2007年第3期4卷 202-205页

作者： SHU Fu-hua College of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan 4.40070 Hubei China

This paper presents a kind of ZA27 squeeze casting process parameter optimization method using artificial neural network (ann) combined with the particle swarm optimizer (PSO). Regarding the test data as samples and using neural network create ZA27 squeeze casting process parameters and mechanical properties of nonlinear mapping model. Using PSO optimize the model and obtain the optimum value of the process parameters. Make full use of the non-neural network mapping capabilities and PSO global optimization capability. The network uses the radial direction primary function neural network,using the clustering and gradient method to make use of network learning,in order to enhance the generalization ability of the network. PSO takes dynamic changing inertia weights to accelerate the convergence speed and avoid a local minimum.

关键词： aluminum-zinc alloy squeeze casting process parameters ann PSO

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ann

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Journal of Groundwater Science and Engineering 2021年第1期9卷 1-11页

作者： GUI Chun-lei WANG Zhen-xing MA Rong ZUO Xue-feng The Institute of Hydrogeology and Environmental Geology Key Laboratory of Groundwater Sciences and EngineeringMNRShijiazhuang 050061China

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value *** grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ann)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ann model for hydraulic conductivity prediction and uncertainty *** means of applying the GLUE-ann model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources *** global best parameter gained through posterior distribution test indicates that the GLUEann model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological ***,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.

关键词： Grain-size distribution Hydraulic conductivity ann GLUE MCMC Stochastic Observation Error(SOE)