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artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2007年第1-2期466卷 274-283页

作者： Dashtbayazi, M. R. Shokuhfar, A. Simchi, A. KN Toosi Univ Technol Fac Mech Engn Tehran Iran Sharif Univ Technol Inst Nanosci & Nanotechnol Dept Mat Sci & Engn Tehran 14588 Iran

An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer perceptron (MLP) and radial basis function (RBF), were used. The steepest descent along with variable learning rate back-propagation algorithm known as a heuristic technique was utilized for training the MLP network. It was found that MLP network yields better results compared to RBF network, giving an acceptable mapping between the network responses and the target data with a high correlation coefficients. The response surfaces between the response variables, i.e. crystallite size, lattice strain of the aluminum matrix and the processing parameters are presented. The procedure modeling can be used for optimization of the MA process for synthesizing of nanostructured metal matrix nanocomposites. (c) 2007 Elsevier B.V. All rights reserved.

关键词： artificial neural network modeling metal matrix nanocomposite mechanical alloying crystallite size lattice strain

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artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients

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BIOMEDICINE & PHARMACOTHERAPY 2004年第4期58卷 239-244页

作者： Yamamura, S Kawada, K Takehira, R Nishizawa, K Katayama, S Hirano, M Momose, Y Toho Univ Sch Pharmaceut Sci Chiba 2748510 Japan Nippon Med Coll Hosp Dept Pharm Serv Bunkyo Ku Tokyo 1138603 Japan

The goal was to use an artificial neural network model to predict the plasma concentration of aminoglycosides in burn patients and identify patients whose plasma antibiotic concentration would be sub-therapeutic based on the patients' physiological data and taking into account burn severity. Physiological data and some indicators of burn severity were collected from 30 burn patients who received arbekacin. A three-layer artificial neural network with five neurons in the hidden layer was used to predict the plasma concentration of arbekacin. Linear modeling for prediction of plasma concentration and logistic regression modeling for the classification of patients were also used and the predictive performance was compared to results from the artificial neural network model. Dose, body mass index, serum creatinine concentration and amount of parenteral fluid were selected as covariates for the plasma concentration of arbekacin. Area of burn after skin graft was a good covariate for indicating burn severity. Predictive performance of the artificial neural network model including burn severity was much better than linear modeling and logistic regression analysis. An artificial neural network model should be helpful for the prediction of plasma concentration using patients' physiological data, and burn severity should be included for improved prediction in burn patients. Because the relationship between burn severity and plasma concentration of aminoglycosides is thought to be nonlinear, it is not surprising that the artificial neural network model showed better predictive performance compared to the linear or logistic regression models. (C) 2004 Elsevier SAS. All rights reserved.

关键词： artificial neural network modeling aminoglycoside antibiotics burn severity pharmacokinetic modeling

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One-pot synthesis of novel 3D graphene/Fe3O4/agro-based waste material (Sesamum indicum) nanocomposite for wastewater treatment and artificial neural network modeling

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年第1期190卷 451-463页

作者： Nasiri, Ramin Zarei, Mahmoud Arsalani, Nasser Pezhhanfar, Sakha Someh, Abolfazl Ahmadi Panahian, Yasaman Univ Tabriz Fac Chem Dept Organ & Biochem Res Lab Polymer Tabriz *** Iran Univ Tabriz Fac Chem Dept Appl Chem Res Lab Environm Remediat Tabriz *** Iran Univ Tabriz Fac Chem Dept Analyt Chem Tabriz *** Iran

A One-Step hydrothermal process was applied in this study to synthesize magnetic Fe3O4/ 3D graphene/Sesamum indicum nanocomposite (M3DGSN). Acid Red 88 (AR88) was set at the target pollutant to be adsorbed and removed by the synthesized nanocomposite from the aqueous media. Field Emission scanning electron microscopy, Fourier transform infrared spectroscopy, vibrating sample magnetometery, and X-Ray diffraction analyses were applied to characterize the product. artificial neural network modeling has also been employed in the adsorption process of AR88 using M3DGSN. Adsorption studies were performed in the dye concentration range of 5-30 mg L-1 with the adsorbent weight of 25-100 mg. It was observed that the dye removal efficiency using M3DGSN was dependent on different operational parameters and increased with rising pH, time, and weight of M3DGSN. Also, decreasing the dye concentration demonstrated the enhancement of dye removal efficiency. The optimum adsorbent amount and dye concentrations were observed to be 100 mg and 5 mg L-1, respectively. Also, the optimum removal performance was seen at the pH of 9. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Adsorption Fe3O4 3D graphene Sesamum indicum Acid Red 88 artificial neural network modeling Water treatment

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Analysis of diclofenac removal by metal-organic framework MIL-100(Fe) using multi-parameter experiments and artificial neural network modeling

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2021年 121卷 257-267页

作者： Jang, Ho-Young Kang, Jin-Kyu Lee, Seung-Chan Park, Jeong-Ann Kim, Song-Bae Seoul Natl Univ Dept Rural Syst Engn Environm Funct Mat & Water Treatment Lab Seoul South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul South Korea Kangwon Natl Univ Dept Environm Engn Chunchon 24341 South Korea

The aim of study was to analyze diclofenac (DCF) removal from aqueous solutions by metal-organic framework MIL-100(Fe) using multi-parameter batch experiments and artificial neural network (ANN) modeling. First, single-parameter experiments were performed in terms of initial solution pH, MIL-100(Fe) dosage, initial DCF concentration, and temperature. The DCF removal decreased with an increase in pH from 5 to 10 and became negligible at pH 12. The kinetic and equilibrium data showed that DCF removal reached an equilibrium at 12 h, with a maximum capacity of 414.6 mg/g from the Langmuir isotherm model. The DCF removal was enhanced with increasing temperature. Multi-parameter experiments (n = 56) conducted under 28 duplicate experimental conditions showed DCF removal rates between 70.8 - 90.8% with a final pH range of 4.5 - 5.4 for most of the experimental conditions. The ANN model was developed based on the multi-parameter experimental data. The optimal topology for the ANN model was determined to be 4:7:6:2 (4 input variables, 7 neurons in the first hidden layer, 6 neurons in the second hidden layer, and 2 output variables). Among the four input variables, temperature was the most important variable affecting DCF removal rate under the given experimental ranges. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Metal-organic frameworks MIL-100(Fe) Diclofenac artificial neural network modeling Adsorption Optimization

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Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling

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BIORESOURCE TECHNOLOGY 2015年 188卷 128-135页

作者： Vani, Sankar Sukumaran, Rajeev Kumar Savithri, Sivaraman CSIR Natl Inst Interdisciplinary Sci & Technol Ctr Biofuels Div Biotechnol Trivandrum 695019 Kerala India CSIR Natl Inst Interdisciplinary Sci & Technol Proc Engn & Environm Technol Div Computat Modeling & Simulat Sect Trivandrum 695019 Kerala India

The present investigation was carried out to study application of ANN as a tool for predicting sugar yields of pretreated biomass during hydrolysis process at various time intervals. Since it is known that biomass loading and particle size influences the rheology and mass transfer during hydrolysis process, these two parameters were chosen for investigating the efficiency of hydrolysis. Alkali pretreated rice straw was used as the model feedstock in this study and biomass loadings were varied from 10% to 18%. Substrate particle sizes used were < 0.5 mm, 0.5-1 mm, > 1 mm and mixed size. Effectiveness of hydrolysis was strongly influenced by biomass loadings, whereas particle size did not have any significant impact on sugar yield. Higher biomass loadings resulted in higher sugar concentration in the hydrolysates. Optimum hydrolysis conditions predicted were 10 FPU/g cellulase, 5 IU/g BGL, 7500 U/g xylanase, 18% biomass loading and mixed particle size with reaction time between 12-28 h. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Particle size Biomass loading Biofuel artificial neural network modeling Biomass hydrolysis

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artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems

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WATER RESEARCH 2024年 259卷 121876页

作者： Liao, Ziqiao Choi, Kungwon Ullah, Zahid Son, Moon Ahn, Yongtae Khan, Moonis Ali Prabhu, Subbaiah Muthu Jeon, Byong-Hun Hanyang Univ Dept Earth Resources & Environm Engn 222 Wangsimni Ro Seoul 04763 South Korea Korea Inst Sci & Technol Ctr Water Cycle Res 5 Hwarang Ro 14 gil Seoul 02792 South Korea Univ Sci & Technol KIST Sch Div Energy & Environm Technol Seoul 02792 South Korea Kyonggi Univ Dept Civil & Energy Syst Engn Suwon 16227 South Korea King Saud Univ Coll Sci Chem Dept Riyadh 11451 Saudi Arabia VIT AP Univ Sch Adv Sci Dept Chem Amaravati 522237 Andhra Pradesh India

This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO3-) concentrations, and sodium hypochlorite (NaClO) oxidant concentrations. Results showed that if the molar ratio of NaClO: As(III) was >1, the oxidation of As(III) could be achieved within a minute with an extremely high oxidation rate of 99.7 %. In the binary system, the removal of As(III) prevailed over Mn(II). The As(III) oxidation efficiency increased from 59.8 +/- 0.6 % to 70.8 +/- 1.9 % when pH rose from 5.7 to 8.0. The oxidation reaction between As(III) and NaClO releases H+ ions, decreasing the pH from 6.77 to 6.19 and reducing the removal efficiency of Mn(II). The presence of HCO3- reduced the oxidation rate of Mn(II) from 63.2 % to 13.9 % within four hours. Instead, the final oxidation rate of Mn(II) increased from 68.1 % to 87.7 %. This increase can be attributed to HCO3- ions competing with the free Mn(II) for the adsorption sites on the sediments, inhibiting the formation of H+. Moreover, kinetic studies revealed that the oxidation reaction between Mn(II) and NaClO followed first-order kinetics based on their R-2 values. The significant factors affecting the Mn(II) oxidation efficiency were the initial concentration of NaClO and pH. Applying an artificial neural network (ANN) model for data analysis proved to be an effective tool for predicting Mn(II) oxidation rates under different experimental conditions. The actual Mn(II) oxidation data and the predicted values obtained from the ANN model showed significant consistency. The training and validation data sets yielded R-2 values of 0.995 and 0.992, respectively. Moreover, the ANN model highlights the importance of pH and NaClO concentrations in influencing the oxidation rate of Mn(II).

关键词： Oxidation Manganese Arsenic Kinetics artificial neural network modeling

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Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

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ALEXANDRIA ENGINEERING JOURNAL 2023年 84卷 184-203页

作者： Rostamzadeh-Renani, Reza Baghoolizadeh, Mohammadreza Sajadi, S. Mohammad Pirmoradian, Mostafa Rostamzadeh-Renani, Mohammad Baghaei, Sh. Salahshour, Soheil Energy Dept Politecn Milano Via Lambruschini 4 I-20156 Milan Italy Shahrekord Univ Dept Mech Engn Shahrekord *** Iran Cihan Univ Erbil Dept Nutr Erbil Kurdistan Regio Iraq Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran Istanbul Okan Univ Fac Engn & Nat Sci Dept Genet & Bioengn Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (phi) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap be-tween experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

关键词： Thermal behavior Hybrid nanofluid Regressors Evolutionary algorithms artificial neural network modeling

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Prediction of aminoglycoside response against methicillin-resistant Staphylococcus aureus infection in burn patients by artificial neural network modeling

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BIOMEDICINE & PHARMACOTHERAPY 2008年第1期62卷 53-58页

作者： Yamamura, Shigeo Kawada, Keiko Takehira, Rieko Nishizawa, Kenji Katayama, Shirou Hirano, Masaaki Momose, Yasunori Toho Univ Sch Pharmaceut Sci Chiba 2748510 Japan Nippon Med Coll Hosp Dept Pharm Serv Bunkyo Ku Tokyo 1138603 Japan

Objective: To predict the response of aminoglycoside antibiotics (arbekacin: ABK) against methicillin-resistant Staphylococcus aureus (MRSA) infection in burn patients after considering the severity of the burn injury by using artificial neural network (ANN). Predictive performance was compared with logistic regression modeling. Methodology: The physiologic data and some indicators of the severity of the burn injury were collected from 25 burn patients who received ABK against MRSA infection. A,three-layered ANN architecture with six neurons in the hidden layer was used to predict the ABK response. The response was monitored using three clinical criteria: number of bacteria, white blood cell count, and C-reactive protein level. Robustness of models was investigated by the leave-one-out cross-validation. Results: The peak plasma level, serum creatinine level, duration of ABK administration, and serum blood sugar level were selected as the linear input parameters to predict the ABK response. The area of the burn after skin grafting was the best parameter for assessing the severity of the burn injury in patients to predict the ABK response in the ANN model. The ANN model with the severity of the burn injury was superior to the logistic regression model in terms of predicting the performance of the ABK response. Conclusion: Based on the patients physiologic data, ANN modeling would be useful for the prediction of the ABK response in burn patients with MRSA infection Severity of the burn injury was a parameter that was necessary for better prediction. (c) 2007 Elsevier Masson SAS. All rights reserved.

关键词： artificial neural network modeling aminoglycosides burn patients

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Comment on "artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders" by Dashtbayazi et al. [Mater. Sci. Eng. A 466 (2007) 274]

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2008年第1-2期492卷 491-492页

作者： Sha, W. Queens Univ Belfast Sch Planning Architecture & Civil Engn Met Res Grp Belfast BT7 1NN Antrim North Ireland

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Informing the Work Zone Safety Policy Analysis: Reconciling Multivariate Prediction and artificial neural network modeling

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2024年第2期150卷

作者： Shaer, Amin Talebian, Ahmadreza Mishra, Sabyasachee Isfahan Univ Technol Dept Transportat Engn Esfahan *** Iran Univ Memphis Dept Civil Engn 112D Engn Sci Bldg Memphis TN 38152 USA

Work zone safety, as an issue requiring complex solutions and tech-driven investment, has been a major concern for transportation agencies and policymakers. To help decision-makers prioritize investments, there is a need for tools enabling them to assess policies designed to reduce the cost and count of work zone crashes at the same time. Traditional statistical methods, however, are unable to consider two or more target variables when they are intrinsically different (e.g., when a mixture of continuous and dichotomous/discrete variables is of interest). As a result, the dependencies among the outputs of interest may be neglected, which potentially causes biased decision-making. In this study, we used two machine learning models, i.e., group method of data handling (GMDH) and multilayer perceptron (MLP), to simultaneously predict count and cost of work zone crashes in the state of Tennessee in the United States. We further used these tools to assess four policies that could help improve work zone safety. We compared predictability of these models in individual prediction of cost and count with the three statistical models of Poisson regression, negative binomial regression, and multivariate regression. Also, we compared the importance of different variables to evaluate the role that each input variable plays in prediction and recognized the most effective factors. Our results indicated that reduction of annual average daily traffic (AADT) and speed limit are the most effective policies to simultaneously lower work zone crash count and costs, ensued by reducing the percentage of trucks and prohibiting commercial vehicles on segments. We observed that MLP and GMDH perform almost equally in the simultaneous prediction, while these models provide significantly greater degrees of accuracy in crash count and cost prediction, compared with statistical models. The results depicted that the major predictors of cost and count prediction of work zone crashes are speed limit, i

关键词： Multivariate prediction Policy prioritization Work zone crashes artificial neural network modeling Variable importance analysis Crash causal factors

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