scholarly journals Application of Artificial Neural Networks for Producing an Estimation of High-Density Polyethylene

Polymers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2319
Author(s):  
Akbar Maleki ◽  
Mostafa Safdari Shadloo ◽  
Amin Rahmat
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Superior Performance ◽  
Copolymer Composition ◽  
Mean Square ◽  
Multi Layer Perceptron ◽  
Hydrogen Flow ◽  
Generalized Regression Neural Networks ◽  
Artificial Neural

Polyethylene as a thermoplastic has received the uppermost popularity in a vast variety of applied contexts. Polyethylene is produced by several commercially obtainable technologies. Since Ziegler–Natta catalysts generate polyolefin with broad molecular weight and copolymer composition distributions, this type of model was utilized to simulate the polymerization procedure. The EIX (ethylene index) is the critical controlling variable that indicates product characteristics. Since it is difficult to measure the EIX, estimation is a problem causing the greatest challenges in the applicability of production. To resolve such problems, ANNs (artificial neural networks) are utilized in the present paper to predict the EIX from some simply computed variables of the system. In fact, the EIX is calculated as a function of pressure, ethylene flow, hydrogen flow, 1-butane flow, catalyst flow, and TEA (triethylaluminium) flow. The estimation was accomplished via the Multi-Layer Perceptron, Radial Basis, Cascade Feed-forward, and Generalized Regression Neural Networks. According to the results, the superior performance of the Multi-Layer Perceptron model than other ANN models was clearly demonstrated. Based on our findings, this model can predict production levels with R2 (regression coefficient), MSE (mean square error), AARD% (average absolute relative deviation percent), and RMSE (root mean square error) of, respectively, 0.89413, 0.02217, 0.4213, and 0.1489.

scholarly journals Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

2017 ◽  
Vol 23 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Mohsen Beigi ◽  
Mehdi Torki-Harchegani ◽  
Mahmood Mahmoodi-Eshkaftaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Mean Square Error ◽  
Back Propagation ◽  
Drying Kinetics ◽  
Mean Square ◽  
Rough Rice ◽  
Artificial Neural ◽  
Drying Curves

The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

scholarly journals Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4572
Author(s):  
Ioannis O. Vardiambasis ◽  
Theodoros N. Kapetanakis ◽  
Christos D. Nikolopoulos ◽  
Trinh Kieu Trang ◽  
Toshiki Tsubota ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sewage Sludge ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Food Waste ◽  
Hydrothermal Carbonization ◽  
Mean Square ◽  
Artificial Neural

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

Deep Learning Neural Networks with Metaheuristic Optimization Algorithms for Groundwater Contamination Vulnerability Mapping in Miryang Aquifer, South Korea

2020 ◽  
Author(s):  
Hussam Eldin Elzain ◽  
Sang Yong Chung ◽  
Venkatramanan Senapathi ◽  
Kye-Hun Park
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
South Korea ◽  
Groundwater Contamination ◽  
Mean Square Error ◽  
Hybrid Models ◽  
Vulnerability Mapping ◽  
Mean Square ◽  
Artificial Neural

<p>This study aims to use an integration of genetic algorithm (GA) model and particle swarm optimization (PSO) with the Deep Learning Neural Networks (DLNN) for groundwater contamination vulnerability. Miryang, a city in the northeastern portion of Gyeongnam Province, South Korea was selected as a case study since it showed urban and rural functions and had undergone groundwater pollution. To initialize the modeling purposes, parameters such as depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and land use were classified into numerical classes and used as input variables. Two-hybrid models of DLNN-GA and DLNN-PSO were implemented using 95 measured nitrate concentration from monitoring wells for the training and testing of artificial neural networks. The performance of the hybrid models was evaluated by several statistical criteria of error: Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Average Error (MAE). The hybrid vulnerability models were also validated by the Area Under the curve (AUC). DLNN-PSO showed the highest (AUC=0.974) performance in comparison with DLNN-GA (AUC=0.954) and Shallow Artificial Neural Networks model (AUC=0.70). The results showed that the proposed hybrid models were more superior than the benchmarked shallow artificial neural networks model used for groundwater contamination vulnerability mapping as a good alternative several years ago.</p>

scholarly journals Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

2020 ◽  
Author(s):  
Behzad Vaferi
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Convective Heat Transfer Coefficient ◽  
Industrial Applications ◽  
Mean Square ◽  
Ann Models ◽  
Artificial Neural ◽  
Mean Square Errors

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

scholarly journals The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 652 ◽  
Author(s):  
Sławomir Francik ◽  
Sławomir Kurpaska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square ◽  
Internal Temperature ◽  
Data Set ◽  
External Temperature ◽  
Factors Influencing ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

scholarly journals Penerapan Generalized Regression Neural Networks untuk Memprediksi Produksi Padi Terhadap Perubahan Iklim

2017 ◽  
Vol 2 (2) ◽  
pp. 117 ◽  
Author(s):  
Muhammad Alkaff ◽  
Yuslena Sari
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Input Data ◽  
Mean Square ◽  
Generalized Regression Neural Networks ◽  
Data Output ◽  
Generalized Regression

Padi sebagai bahan makanan pokok utama bagi masyarakat Indonesia merupakan tanaman pangan yang rentan terhadap perubahan iklim. Pendataan dan perhitungan ramalan hasil produksi padi sangat diperlukan untuk mendukung kebijakan yang berkaitan dengan ketahanan pangan. Penelitian ini bertujuan untuk melakukan peramalan terhadap produksi padi di Kabupaten Barito Kuala sebagai kabupaten penghasil padi terbesar di Kalimantan Selatan dengan menggunakan data iklim sebagai input. Data iklim yang digunakan berasal dari Stasiun Meteorologi Syamsudin Noor, sedangkan sebagai data output adalah data produksi padi dari Badan Pusat Statistika (BPS) Provinsi Kalimantan Selatan. Metode yang digunakan untuk melakukan peramalan produksi padi adalah Generalized Regression Neural Networks (GRNN). Dari hasil pengujian didapatkan nilai Root Mean Square Error (RMSE) sebesar 0,296 dengan menggunakan parameter smoothness bernilai 1.Kata kunci: padi, iklim, Barito Kuala, GRNN, RMSE

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Test Sample ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

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