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Modeling and optimization of Graphene Oxide (GO) membranes for nanofiltration with artificial neural networks

JOURNAL OF WATER PROCESS ENGINEERING 2023年 55卷

作者： Yang, Haodong Chen, Zhe Li, Yong Yao, Lei Wang, Geming Deng, Quanrong Fu, Ping Wang, Shenggao Wuhan Inst Technol Sch Mat Sci & Engn Hubei Key Lab Plasma Chem & Adv Mat Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

It is important to understand the correlation between the nanostructure and membrane performances (water flux and rejection) in membrane separation technology, which is helpful to develop novel membranes. In this study, a back propagation artificial neural network model optimized with genetic algorithms was proposed to predict the nanofiltration performances (water flux and rejection) of GO membranes. The aim is to explore the feature importance of the membrane structure parameters (such as interlayer spacing, Zeta potential, water contact angle, roughness and thickness) and the operating condition (operation pressure) on the membrane properties, so as to provide clues for improving the membrane performances. The obtained results showed that genetic algorithm-back propagation artificial neural network (GABPANN) exhibited more accurate performance to predict the correlation between the parameters (including membrane structure and operation pressure) and the membrane performances according to the published experimental results. Moreover, the GABPANN results indicated that the water contact angle is the most powerful parameter determining the water flux, while the surface charge is the most powerful one determining the rejection of GO membranes. This work provided a novel strategy to efficiently optimize the nanofiltration performance of GO membranes, beneficial for better under-standing and controlling the structural design of GO membranes.

关键词： GO membrane Genetic algorithm back-propagation artificial neural network Water flux Rejection

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Soft computing for sustainable drilling of AISI 316L stainless steel under formulated neem oil minimum quantity lubrication condition

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COGENT ENGINEERING 2023年第2期10卷

作者： Natesh, C. P. Shashidhara, Y. M. Amarendra, H. J. Shetty, Raviraj Nayak, Rajesh Shetty, S. V. UdayKumar Nayak, Madhukara Hegde, Adithya Malnad Coll Engn Dept Automobile Engn Hassan India Malnad Coll Engn Dept Mech Engn Hassan India Manipal Acad Higher Educ Manipal Inst Technol Dept Mech & Ind Engn Manipal 576104 India Shri Madhwa Vadiraja Inst Technol & Management Mech Engn Dept Bantakal India

This paper discusses about process input optimization to obtain desired output characteristics such as Surface Roughness (microns), Thrust Force (N) and Torque (N-m) during drilling of AISI 316 L Stainless Steel under minimum quantity formulated neem oil lubrication condition based on Taguchi Design of Experiments (TDOE), Response Surface Methodology (RSM) and Desirability Functional Analysis (DFA) by varying flow rate (***-1), stand-off distance (mm), flow pressure (Bar) and nozzle exit diameter (mm). Formulated Neem Oil possesses natural lubricating properties that reduce friction and heat generation, thus prolonging the tool life and improving surface finish. Additionally, it is biodegradable and environmentally friendly, making it a sustainable choice for machining operations. From the experimental investigation using TDOE, it was observed that there was considerable improvement in thrust force, surface roughness and torque with modified neem oil as a lubricant. Further, plot for main effects and Analysis of Variance (ANOVA) are successfully used to identify the optimum process input parameters and their percentage of contribution (P%) on output parameters. RSM is successfully used to generate a second order mathematical model which can be effectively used to analyze the process parameters. Further, from Desirability Functional Analysis (DFA), minimum surface roughness (0.34 microns), thrust force (1292.37 N) and torque (14.71 N-m) value were predicted. Finally, back propagation artificial neural network (BPANN) analysis has been adopted to predict the surface roughness, thrust force and torque with a minimal error of 1.46%, 0.017% & 0.17%, respectively. The adoption of Neem oil formulations has been successful in improving machining characteristics. Its versatility across an array of machining processes and materials, in tandem with the global momentum toward greener manufacturing paradigms, positions it as a promising lubricant for various machining practic

关键词： AISI 316L stainless steel drilling Taguchi design of experiments response surface methodology desirability function analysis back-propagation artificial neural network surface roughness thrust force torque

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Soft computing approach for optimization of turning characteristics of elastomers under different lubrication conditions

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COGENT ENGINEERING 2023年第2期10卷

作者： Bagawan, Malikasab Dundur, Suresh T. Gurani, Rajesh Shetty, Raviraj Nayak, Rajesh Shetty, S. V. Udaya Kumar Nayak, Madhukara Hegde, Adithya Manipal Acad Higher Educ Manipal Inst Technol Mech & Ind Engn Dept Manipal 576104 India Mathuri VenkataSubba Rao Engn Coll Dept Automobile Engn Hyderabad India Basaveshwara Engn Coll Dept Ind & Prod Engn Bagalakot India BharataRatna Indira Gandhi Coll Engn Dept Mech Engn Sollapur India Manipal Acad Higher Educ Manipal Inst Technol Mech & Ind Engn Dept Manipal India Shri MadhwaVadiraja Inst Technol & Management Mech Engn Dept Bantakal India

Elastomers are the class of materials that are widely used in a variety of industrial, commercial, and consumer applications due to their unique mechanical properties, including high elasticity, high flexibility, and high resilience. However, there are many challenges in machining of elastomers such as poor surface finish, build up of heat, degradation of elastomers, etc. To overcome these challenges, cryogenic cooling assistance has been introduced as a means of improving the machinability of elastomers. This paper presents a soft computing approach for optimizing the surface roughness and cutting force during turning of elastomers under different lubrication conditions. Three types of elastomers, namely Nitrile Rubber (NBR), Polyurethane Rubber (PU), and Neoprene Rubber (CR), are studied, and a cryogenic fluid delivery system is employed to improve the machining process. Taguchi's L27 array is used to vary the input parameters, and a back-propagation artificial neural network (BPANN) model is developed to predict the cutting force and surface roughness. The cutting force and surface roughness are analyzed under different cooling conditions, cutting speeds, feeds, and depths of cut for various elastomers. The results show that changes in cutting conditions significantly affect the cutting force and that the type of lubrication used affects the cutting force by altering the material's physical properties. Cutting force is significantly influenced by cutting conditions, and NBR requires the highest cutting force compared to PU and CR. Further, at a cutting speed of 55 m/min, a feed of 0.11 mm/rev, and a depth of cut of 0.25 mm, the cutting force for NBR (85.1 N), while for PU (75.1 N) and CR (80.3 N), respectively. Finally, with LN2 lubrication conditions, the Cutting Force decreased by 45% and Surface Roughness decreased by 16.9%. This study provides insights into the factors affecting the elastomer machining process, which can be useful for optimizing the machining

关键词： elastomers nitrile rubber polyurethane rubber neoprene rubber tetrafluoroethane liquid nitrogen cryogenic turning back-propagation artificial neural network surface roughness cutting force

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Data mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年第0期196卷 103885-000页

作者： Lu, Hui Hu, Xiaojun Cao, Bin Ma, Liang Chai, Wanqiu Yang, Yunchuan Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 10083 Peoples R China Guiyang Aluminium Magnesium Design & Res Inst Co Guiyang 550081 Peoples R China

Liquidus temperature for primary crystallization is an important physical and chemical property for electrolyte system. It plays a crucial role on the stability of the electric cell in electrolysis production process. So how to accurately predict the liquidus temperature for primary crystallization of electrolyte based on the composition of electrolyte is a meaningful research subject. In this work, data mining assisted prediction of liquidus temperature for primary crystallization of electrolyte systems was proposed. The essential differences between the complex industrial electrolyte system and electrolyte system prepared in laboratory were revealed by means of comparing the micro-morphology, phase composition and thermal analysis. To some extent, it was verified that the empirical formula has no versatility in the two different electrolyte systems. The prediction model of liquidus temperature for primary crystallization of different electrolyte systems was constructed by using SVM(support vector machine), BPANN(back-propagation artifical neural networks), RFR(random forest regression) and GBR(gradient boosting regression) algorithm, respectively. The electroyte system inculdes Na3AlF6(CR)-Al2O3-AlF3-CaF2, Na3AlF6(CR)-Al2O3-MgF2-CaF2-LiF, Na3AlF6(CR)Al2O3-MgF2-CaF2-KF-LiF, and Na3AlF6(CR)-Al2O3-AlF3-CaF2-MgF2-LiF-KF-NaF. For different electrolyte systems, ANN, SVM, RFR and other models all have good performances, they can effectively predict the liquidus temperature for primary crystallization of each electrolyte systems. For some electrolyte systems, ANN, SVM, RFR models are obviously superior to the prediction level of empirical formula described in the literature. It can be seen that data mining has a good application prospect in the prediction of the liquidus temperature for primary crystallization of electrolyte systems. We provide a new method for predicting the liquidus temperature for primary crystallization of different electrolyte systems based on the el

关键词： Aluminium electrolysis industry Electrolyte Liquidus temperature Support vector machine back-propagation artificial neural network Random forest regression Gradient boosting regression

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Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

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Transactions of Nonferrous Metals Society of China 2021年第6期31卷 1665-1679页

作者： Shuang-xi SHI Xiu-sheng LIU Xiao-yong ZHANG Ke-chao ZHOU State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410083China Wuhan Research Institute of Materials Protection Wuhan 430030China Shenzhen Research Institute Central South UniversityShenzhen 518057China

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and *** stress−strain data extended by two models were implanted into finite element to simulate hot compression *** results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing *** the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of *** cross-validation tests also confirm that the BPANN model has high prediction *** models are effective and feasible in simulation,but BPANN model is superior in accuracy.

关键词： Ti-55511 alloy flow stress Arrhenius constitutive equation back-propagation artificial neural network finite element

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Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2011年第3期233卷 457-463页

作者： Pan, Lei-qing Zhan, Ge Tu, Kang Tu, Sicong Liu, Peng Nanjing Agr Univ Coll Food Sci & Technol Nanjing 210095 Jiangsu Peoples R China Univ New S Wales Sydney NSW 2052 Australia

An experimental system utilizing acoustic response system (ARS) and computer vision system (CVS) for eggshell crack detection was implemented. Firstly, the acoustic response signals were captured and analyzed, and six parameters (f (1), f (2), f (3), f (4): the dominant response frequency;CS: the mean value of coefficient of skewness;CE: the mean value of coefficient of excess) were analyzed. The ARS including a back-propagation artificial neural network model with a structure of 6 input nodes, 15 hidden nodes, and one output node was built to detect eggshell cracks. Secondly, the eggshell images were captured and processed by the computer vision system, and five geometrical characteristic parameters of crack and noise regions on the eggshell images were acquired. The CVS including a back-propagation artificial neural network model with a structure of 5 input nodes, 10 hidden nodes, and one output node was built to detect eggshell cracks. Finally, the quality of eggs, with or without cracks, was evaluated based on detection results from both CVS and ARS. This method allows the fusion of information obtained from CVS to ARS. The results showed that the detection accuracy of cracked eggs were 68 and 92%, respectively, by CVS and ARS. However, the accuracy equaled to 98% by the information infusion of two techniques. The result was superior to only one technique, and the method based on the information fusion of computer vision and acoustic response was applicable for detecting egg cracks. This research provides a new technology detection of cracked egg.

关键词： Eggshell Crack Detection CVS ARS back-propagation artificial neural network

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Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 209卷 106320-106320页

作者： Ferjaoui, Radhia Cherni, Mohamed Ali Boujnah, Sana Kraiem, Nour El Houda Kraiem, Tarek Univ Tunis El Manar Res Lab Biophys & Med Technol LRBTM ISTMT Tunis 1006 Tunisia Univ Tunis LR13 ES03 SIME Lab ENSIT Montfleury 1008 Tunisia Univ Tunis El Manar Natl Engn Sch Tunis Tunis Tunisia Hosp Aziza Othmana Tunis Radiol Dept Tunis 1006 Tunisia Univ Tunis El Manar Fac Med Tunis Tunis 1007 Tunisia

background: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. Methods: The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. Results: The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F-1-measure, and geometric-mean w

关键词： Evolutive lymphoma Residual masses Machine learning Deep learning back-propagation artificial neural network Support vector machine K-nearest neighbours Random forest Relevance vectors machine (RVM) Convolutional neural network (CNN)

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Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms

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COMPOSITE STRUCTURES 2021年 268卷 113904-113904页

作者： Chen, Wenguang Xu, Jinjun Dong, Minhao Yu, Yong Elchalakani, Mohamed Zhang, Fengliang Nanjing Tech Univ Coll Civil Engn Int Ctr Integrated Protect Res Engn Struct Nanjing 211816 Peoples R China Univ Western Australia Sch Civil Environm & Min Engn 35 Stirling Highway Crawley WA 6009 Australia Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China Shaanxi Architecture Sci Res Inst Co LTD Xian 710082 Peoples R China

The existing models for predicting the ultimate axial strain of FRP-confined concrete cylinders are mainly derived from the regression analyses on small datasets. Such models usually targeted more specific use cases and could give inaccurate outcomes when generalized. To this end, this paper presents the data-driven Bayesian probabilistic and machine learning prediction models (i.e., back-propagation artificial neural network, multi-gene genetic programming and support vector machine) with high accuracy. First, a comprehensive database containing 471 test results on the ultimate conditions of FRP-confined concrete cylinders was elaborately compiled from the open literature, and the quality of the database was examined and evaluated in detail. Then, an updating procedure characterized by the Bayesian parameter estimation technique was developed to evaluate the critical parameters in the existing models and refine the selected existing models accordingly. The database was also employed for deriving machine learning models. The computational efficiency, transferability and precision of the proposed models are verified. Results show that the proposed Bayesian posterior models, back-propagation artificial neural network, multi-gene genetic programming and support vector machine models achieved outstanding predictive performance, with the support vector machine yielding the highest prediction accuracy. The superior accuracy of the proposed models should assist various stakeholders in optimal use of FRP-confined concrete columns in diverse construction applications.

关键词： FRP-confined concrete Ultimate axial strain Bayesian theory Machine learning back-propagation artificial neural network Multi-gene genetic programming Support vector machine

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Enhanced anaerobic digestion of swine manure via a coupled microbial electrolysis cell

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BIORESOURCE TECHNOLOGY 2021年 340卷 125619-125619页

作者： Zou, Lifei Wang, Changmei Zhao, Xingling Wu, Kai Liang, Chengyue Yin, Fang Yang, Bin Liu, Jing Yang, Hong Zhang, Wudi Yunnan Normal Univ Kunming 650500 Yunnan Peoples R China Xingyi Normal Univ Nationalities Xingyi 562400 Peoples R China Yunnan Res Ctr Biogas Technol & Engn Kunming 650500 Yunnan Peoples R China Jilin Dongsheng Inst Biomass Energy Engn Tonghua 134118 Peoples R China

Microbial electrolysis cell coupled anaerobic digestion (MEC-AD) is a new technology in energy recovery and waste treatment, which could be used to recycle swine manure. Here, different applied voltage effects were studied using MEC-AD with swine manure as a substrate. The maximum cumulative biogas and methane yields, both occurring with 0.9 V, were 547.3 mL/g total solid (TS) and 347.7 mL/g TS, respectively. The increased energy can counterbalance the electrical input. First order, logistic, gompertz, and back-propagation artificial neural network (BP-ANN) models were used to study cumulative biogas and methane yields. The BP-ANN model was superior to the other three models. The maximum degradation rate of hemicellulose, cellulose, and lignin was 60.97%, 48.59%, and 31.59% at 0.9 V, respectively. The BP-ANN model establishes a model for cumulative biogas and methane yields using MEC-AD. Thus, MEC-AD enhanced biogas and methane production and accelerated substrate degradation at a suitable voltage.

关键词： Microbial electrolysis cell Anaerobic digestion Swine manure Kinetic models back-propagation artificial neural network

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Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas)

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FOOD ANALYTICAL METHODS 2019年第3期12卷 799-810页

作者： Chen, Lipin Li, Zhaojie Yu, Fanqianhui Zhang, Xu Xue, Yong Xue, Changhu Ocean Univ China Coll Food Sci & Engn Qingdao 266003 Peoples R China

Total volatile basic nitrogen (TVB-N) content is used to evaluate Pacific oyster (Crassostrea gigas) freshness. In this work, hyperspectral imaging (HSI;400-1000nm) was used to measure the TVB-N content in Pacific oysters. Accordingly, Pacific oyster samples stored in 15 degrees C water were assessed at intervals after 1, 3, 5, 7, or 9 days. Minimum noise separation processing of the hyperspectral images was performed before determining the region of interest for data dimension reduction. The effects of multiplicative scatter correction (MSC) on the obtained data were then investigated. To simplify the calibration model, 12 characteristic wavelengths were selected from the Pacific oyster hypercube using the correlation coefficient method. Finally, multiple linear regression (MLR) and back-propagation artificial neural network (BP-ANN) models were built from the selected wavelengths. The experimental results showed that the correlation coefficients between the corrected, predicted, and cross-validated datasets were lower in the MLR model than in the BP-ANN. However, the MLR model outperformed the BP-ANN in terms of the root-mean-square errors of correction, prediction, and interaction verification. Overall, both the MLR and BP-ANN models demonstrated that the combination of HSI with chemometric methods can be used to detect and accurately predict Pacific Oyster freshness during storage.

关键词： Total volatile basic nitrogen Pacific oysters (Crassostrea gigas) Hyperspectral imaging Multiplicative scatter correction Multiple linear regression back-propagation artificial neural network

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