scholarly journals Optimization and design of machine learning computational technique for prediction of physical separation process

2022 ◽  
pp. 103680
Author(s):  
Haiqing Li ◽  
Chairun Nasirin ◽  
Azher M. Abed ◽  
Dmitry Olegovich Bokov ◽  
Lakshmi Thangavelu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Separation Process ◽  
Computational Technique ◽  
Physical Separation

scholarly journals Rat Protein’s Enzyme Class Classification Using Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 655-663
Keyword(s):  
Machine Learning ◽  
Statistical Technique ◽  
Research Area ◽  
Experimental Result ◽  
Protein Classification ◽  
Computational Technique ◽  
Unknown Protein ◽  
Enzyme Class ◽  
Protein Enzyme

In the current era, bioinformatics has been an emerging research area in the context of protein enzyme classification from the unknown protein data. In bioinformatics, the prime goal is to manipulate the protein data and develop a computational technique to classify and predict the appropriate features for function predictions. In this context, several machine learning and statistical technique have been designed for classification of data. The classification of protein data is one the challenging task and generally the classification of protein data has been done on human protein data. In this article, we have considered rat enzyme class for classification and predictions. Here we have used like CRT, CHAID, C5.0, NEURAL, SVM, and Bayesian for classification of protein data and to measure the performance of the model, the accuracy, specificity, sensitivity, precision, recall, f-measures and MCC have been used. The experimental result highlights that the some of the protein data are imbalance that affects the performance. In this experiment, the Lyases, Isomerases and Ligases class of data are imbalanced and affect the performance of the models. The experimental results highlight that the C5.0 gives 91.5% accuracy and takes only 4 second for computation and can be used for protein classification and prediction of protein data.

scholarly journals Electrostatic Separation of Copper and Glass Particles in Pretreated Automobile Shredder Residue

Metals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 879
Author(s):  
Beom-Uk Kim ◽  
Chul-Hyun Park
Keyword(s):  
Separation Efficiency ◽  
Separation Process ◽  
Electrostatic Separation ◽  
Physical Separation ◽  
Shredder Residue ◽  
Conductive Materials ◽  
Automobile Shredder Residue ◽  
Small Glass ◽  
Increasing Demand ◽  
Optimum Separation

There is increasing demand for an efficient technique for separating automobile shredder residue (ASR) obtained from end-of-life vehicles (ELVs). A particular challenge is the physical separation of conductive materials from glass. In this study, the performance of pretreatment and induction electrostatic separation process was evaluated. The results show that a sieving/washing (combination of sieving and washing) pretreatment was the most effective for removing conductive material compared to electrostatic separation alone. The optimum separation efficiency of copper products was achieved with an applied voltage of 20 kV, a relative humidity of less than 35%, and a splitter position of 8 cm. Although the separation efficiency was slightly reduced when some small glass particles remained attached to the conductive materials, the separation efficiency of copper from the pretreated ASR dramatically increased to 83.1% grade and 90.4% recovery, compared to that of raw ASR (34.3% grade and 58.6% recovery). Based on these results, it was demonstrated that the proposed sieving/washing pretreatment was proficient at removing conductive materials from glass; thus, it has the potential to significantly improve the efficiency of electrostatic separation for ASR.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

Novel physical separation process for eco-friendly recycling of rare and valuable metals from end-of-life DVD-PCBs

2013 ◽  
Vol 111 ◽  
pp. 145-154 ◽  
Author(s):  
Vinod Kumar ◽  
Jae-chun Lee ◽  
Jinki Jeong ◽  
Manis Kumar Jha ◽  
Byung-su Kim ◽  
...  
Keyword(s):  
End Of Life ◽  
Separation Process ◽  
Physical Separation ◽  
Valuable Metals

scholarly journals THE EFFECTIVENESS OF PHYSICAL SEPARATION PROCESS FOR THE ALLUVIAL TIN (HEINDA) ORE, MYANMAR

2020 ◽  
Vol 6 (3) ◽  
pp. 25-42
Author(s):  
Nay Zaw Htay Win ◽  
◽  
Apisit Numprasanthai ◽  
Pipat Laowattanabandit ◽  
◽  
...  
Keyword(s):  
Separation Process ◽  
Physical Separation

scholarly journals Solving musculoskeletal biomechanics with machine learning

2021 ◽  
Vol 7 ◽  
pp. e663
Author(s):  
Yaroslav Smirnov ◽  
Denys Smirnov ◽  
Anton Popov ◽  
Sergiy Yakovenko
Keyword(s):  
Machine Learning ◽  
Degrees Of Freedom ◽  
Muscle Length ◽  
Gradient Boosting ◽  
Computational Technique ◽  
Model Errors ◽  
Ann Model ◽  
Moment Arms ◽  
Hand Model ◽  
Light Gradient

Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. In this study, we challenged general ML algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets, where joint angles were the input and the muscle length and moment arms were the output, were generated by our previous phenomenological model based on the autogenerated polynomial structures. Both models achieved a similar level of errors: ANN model errors were 0.08 ± 0.05% for muscle lengths and 0.53 ± 0.29% for moment arms, and LGB model made similar errors—0.18 ± 0.06% and 0.13 ± 0.07%, respectively. LGB model reached the training goal with only 103 samples, while ANN required 106 samples; however, LGB models were about 39 times slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of ML for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.

scholarly journals Modeling of Experimentally verified Effect of Basic Input Parameters on Fuel Consumption of oil fired furnace using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 2178-2181
Keyword(s):  
Machine Learning ◽  
Fuel Consumption ◽  
Multiple Regression ◽  
Process Parameters ◽  
Rotational Speed ◽  
Melting Rate ◽  
Computational Technique ◽  
Foundry Industry ◽  
Input Parameters ◽  
Basic Input

The authors have attempted to create a model, based on actual experiments conducted on an oil fired Rotary Tilting Furnace of 200 kg melting capacity installed in a foundry unit of Agra. Multiple regression machine learning has been considered as a suitable and novel tool for Modeling of basic input process parameters of rotary tilting furnace. The basic process parameters in a rotary furnace considered are RPM (rotational speed per minute), Time of melting of one charge of 200 kgs (minute) ,melting rate of furnace (kg/hr) and fuel consumed for melting of one charge need to be controlled during whole process. In this paper the relation between input parameters such as rotational speed of the furnace, time, and melting rate have been attempted to be established with output parameter fuel consumption. This model of multiple regression machine learning may found its practical applicability in foundry industry to predict the fuel consumption of furnace before putting it in actual operation and accordingly the input parameters can be controlled for desired optimal fuel consumption. The methodology consists of experimental investigations, modeling of process parameters using machine learning, followed by result analysis. The fossil fuels may not last forever and till no other alternate fuels are developed for melting the foundry industry need to optimize it. The optimized results obtained by this model and computational technique are in line with the results of actual experiments.

Recycling of electrode materials from spent lithium-ion power batteries via thermal and mechanical treatments

2020 ◽  
pp. 0734242X2096980
Author(s):  
Zhongwei Wu ◽  
Huabing Zhu ◽  
Haijun Bi ◽  
Ping He ◽  
Song Gao
Keyword(s):  
Cathode Materials ◽  
High Speed ◽  
Electrode Materials ◽  
Lithium Ion ◽  
High Energy ◽  
Dissociation Rate ◽  
Separation Process ◽  
Physical Separation ◽  
Current Collectors ◽  
Energy Conserving

This study developed a physical separation process that recovers active cathode materials from current collectors in spent lithium-ion power batteries (LIBs). The physical separation process, implemented via thermal and mechanical treatments, was examined based on cohesive zone models (CZMs) and verified by physical separation experiments. The most efficient condition was determined by optimising the key parameters (temperature and time) of selective heating. Among several mechanical separation methods, high-speed shearing best separates positive electrode materials into active cathode materials (LiFePO4) and current collectors (Al fragments). The separation effect was verified by computing the dissociation rate and microscopic observation of the separated materials. The feasibility and efficiency of the above process were assessed in a work-of-force analysis, flow field simulation, high-speed crushing experiment and material property analysis. The above analyses realised a feasible, efficient and environmentally friendly separation route without changing the chemical structure and properties of the electrode materials. Under non-high (energy-conserving) temperature conditions, the LiFePO4 dissociation rate stabilises at 80–85%. Under high-speed crushing, the LiFePO4 dissociation rate reaches 85% at 32,000-r/min crushing and a maximum shearing velocity of the blade edge v ≈ 500 m/s. This approach can effectively recycle electrode materials, gain valuable resources and can be used to recycle and utilise spent LIBs, thus addressing two grave issues – environmental pollution and resource wastage to achieve the sustainable development of LIBs and electric vehicle industry.

scholarly journals INVESTIGATION OF ABILITY LIBERATION OF METALS FROM PRINTED CIRCUIT BOARDS BY MECHANICAL PROCESSES FOR PHYSICAL SEPARATION PROCESSES

2018 ◽  
Vol 54 (2A) ◽  
pp. 237
Author(s):  
Ha Vinh Hung
Keyword(s):  
Printed Circuit Boards ◽  
Separation Efficiency ◽  
Low Cost ◽  
Separation Process ◽  
Air Separation ◽  
Hammer Mill ◽  
Circuit Boards ◽  
Metallic Component ◽  
Physical Separation ◽  
Printed Circuit

Physical separation process was widely applied for the separation of metallic component from Printed Circuit Boards (PCBs) due to their advantages as friendly-environment, facilitated control, and low-cost. However, the efficiency of physical separation depends on a level of the liberation between the metallic and non-metallic components which is conducted by mechanical processing.In this study, the liberation of metals from computer PCBs was conducted in detail by mechanical processes including cutting and crushing. The obtained results demonstrate the distribution metallic and non-metallic component weighs as a function of particle sizes. The separation efficiency of metals was conducted by air separation using vacuum sorter equipment. The results showed that the comminution processes using hammer mill for reach the highest efficiency with 92 % recovery and 87 % grade of metallic components in the heavy fraction with particle size 1.0 - 1.4 mm by air separation process.

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