scholarly journals Application of Artificial Neural Networks to Predict the Catalytic Pyrolysis of HDPE Using Non-Isothermal TGA Data

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1813 ◽  
Author(s):  
Mohammed Al-Yaari ◽  
Ibrahim Dubdub
Keyword(s):  
Thermogravimetric Analysis ◽  
Catalytic Pyrolysis ◽  
Model Fitting ◽  
Main Reaction ◽  
Ann Model ◽  
Heating Rates ◽  
Highly Efficient ◽  
Model Free ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a comprehensive kinetic study of the catalytic pyrolysis of high-density polyethylene (HDPE) utilizing thermogravimetric analysis (TGA) data. Nine runs with different catalyst (HZSM-5) to polymer mass ratios (0.5, 0.77, and 1.0) were performed at different heating rates (5, 10, and 15 K/min) under nitrogen over the temperature range 303–973 K. Thermograms showed clearly that there was only one main reaction region for the catalytic cracking of HDPE. In addition, while thermogravimetric analysis (TGA) data were shifted towards higher temperatures as the heating rate increased, they were shifted towards lower temperatures and polymer started to degrade at lower temperatures when the catalyst was used. Furthermore, the activation energy of the catalytic pyrolysis of HDPE was obtained using three isoconversional (model-free) models and two non-isoconversional (model-fitting) models. Moreover, a set of 900 input-output experimental TGA data has been predicted by a highly efficient developed artificial neural network (ANN) model. Results showed a very good agreement between the ANN-predicted and experimental values (R2 > 0.999). Besides, A highly-efficient performance of the developed model has been reported for new input data as well.

scholarly journals Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction

Polymers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 891 ◽  
Author(s):  
Ibrahim Dubdub ◽  
Mohammed Al-Yaari
Keyword(s):  
Kinetic Parameters ◽  
Transfer Functions ◽  
Model Fitting ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Ann Model ◽  
Heating Rates ◽  
Highly Efficient ◽  
Model Free ◽  
Good Agreement

Pyrolysis of waste low-density polyethylene (LDPE) is considered to be a highly efficient, promising treatment method. This work aims to investigate the kinetics of LDPE pyrolysis using three model-free methods (Friedman, Flynn-Wall-Qzawa (FWO), and Kissinger-Akahira-Sunose (KAS)), two model-fitting methods (Arrhenius and Coats-Redfern), as well as to develop, for the first time, a highly efficient artificial neural network (ANN) model to predict the kinetic parameters of LDPE pyrolysis. Thermogravimetric (TG) and derivative thermogravimetric (DTG) thermograms at 5, 10, 20 and 40 K min−1 showed only a single pyrolysis zone, implying a single reaction. The values of the kinetic parameters (E and A) of LDPE pyrolysis have been calculated at different conversions by three model-free methods and the average values of the obtained activation energies are in good agreement and ranging between 193 and 195 kJ mol−1. In addition, these kinetic parameters at different heating rates have been calculated using Arrhenius and Coats-Redfern methods. Moreover, a feed-forward ANN with backpropagation model, with 10 neurons in two hidden layers and logsig-logsig transfer functions, has been employed to predict the thermogravimetric analysis (TGA) kinetic data. Results showed good agreement between the ANN-predicted and experimental data (R > 0.9999). Then, the selected network topology was tested for extra new input data with a highly efficient performance.

scholarly journals Kinetic studies on the pyrolysis of plastic waste using a combination of model-fitting and model-free methods

2020 ◽  
Vol 38 (1_suppl) ◽  
pp. 77-85 ◽  
Author(s):  
Zhitong Yao ◽  
Shaoqi Yu ◽  
Weiping Su ◽  
Weihong Wu ◽  
Junhong Tang ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Thermogravimetric Analysis ◽  
Compensation Effect ◽  
Model Fitting ◽  
Plastic Waste ◽  
Plastic Shell ◽  
Ozawa Method ◽  
Heating Rates ◽  
Helium Atmosphere ◽  
Model Free

In this work, the pyrolysis behavior of plastic waste—TV plastic shell—was investigated, based on thermogravimetric analysis and using a combination of model-fitting and model-free methods. The possible reaction mechanism and kinetic compensation effects were also examined. Thermogravimetric analysis indicated that the decomposition of plastic waste in a helium atmosphere can be divided into three stages: the minor loss stage (20–300°C), the major loss stage (300–500°C) and the stable loss stage (500–1000°C). The corresponding weight loss at three different heating rates of 15, 25 and 35 K/min were determined to be 2.80–3.02%, 94.45–95.11% and 0.04–0.16%, respectively. The activation energy ( Ea) and correlation coefficient ( R2) profiles revealed that the kinetic parameters calculated using the Friedman and Kissinger–Akahira–Sunose method displayed a similar trend. The values from the Flynn–Wall–Ozawa and Starink methods were comparable, although the former gave higher R2 values. The Eα values gradually decreased from 269.75 kJ/mol to 184.18 kJ/mol as the degree of conversion ( α) increased from 0.1 to 0.8. Beyond this range, the Eα slightly increased to 211.31 kJ/mol. The model-fitting method of Coats–Redfern was used to predict the possible reaction mechanism, for which the first-order model resulted in higher R2 values than and comparable Eα values to those obtained from the Flynn–Wall–Ozawa method. The pre-exponential factors (ln A) were calculated based on the F1 reaction model and the Flynn–Wall–Ozawa method, and fell in the range 59.34–48.05. The study of the kinetic compensation effect confirmed that a compensation effect existed between Ea and ln A during the plastic waste pyrolysis.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Thermo-catalytic decomposition of polystyrene waste: Comparative analysis using different kinetic models

2019 ◽  
Vol 38 (2) ◽  
pp. 202-212 ◽  
Author(s):  
Ghulam Ali ◽  
Jan Nisar ◽  
Munawar Iqbal ◽  
Afzal Shah ◽  
Mazhar Abbas ◽  
...  
Keyword(s):  
Activation Energy ◽  
Copper Oxide ◽  
Solid Waste ◽  
Thermodynamic Parameters ◽  
Model Fitting ◽  
Catalytic Decomposition ◽  
Decomposition Reaction ◽  
Inert Atmosphere ◽  
Heating Rates ◽  
Model Free

Due to a huge increase in polymer production, a tremendous increase in municipal solid waste is observed. Every year the existing landfills for disposal of waste polymers decrease and the effective recycling techniques for waste polymers are getting more and more important. In this work pyrolysis of waste polystyrene was performed in the presence of a laboratory synthesized copper oxide. The samples were pyrolyzed at different heating rates that is, 5°Cmin−1, 10°Cmin−1, 15°Cmin−1 and 20°Cmin−1 in a thermogravimetric analyzer in inert atmosphere using nitrogen. Thermogravimetric data were interpreted using various model fitting (Coats–Redfern) and model free methods (Ozawa–Flynn–Wall, Kissinger–Akahira–Sunose and Friedman). Thermodynamic parameters for the reaction were also determined. The activation energy calculated applying Coats–Redfern, Ozawa–Flynn–Wall, Kissinger–Akahira–Sunose and Friedman models were found in the ranges 105–148.48 kJmol−1, 99.41–140.52 kJmol−1, 103.67–149.15 kJmol−1 and 99.93–141.25 kJmol−1, respectively. The lowest activation energy for polystyrene degradation in the presence of copper oxide indicates the suitability of catalyst for the decomposition reaction to take place at lower temperature. Moreover, the obtained kinetics and thermodynamic parameters would be very helpful in determining the reaction mechanism of the solid waste in a real system.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

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