Analysis of influencing factors of the production performance of an enhanced geothermal system (EGS) with numerical simulation and artificial neural network (ANN)

2019 ◽  
Vol 200 ◽  
pp. 31-46 ◽  
Author(s):  
Ling Zhou ◽  
Yanjun Zhang ◽  
Zhongjun Hu ◽  
Ziwang Yu ◽  
Yinfei Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Production Performance ◽  
Geothermal System ◽  
Enhanced Geothermal System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 188
Author(s):  
Shu Ji ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Intelligent Manufacturing ◽  
Evaluation Index System ◽  
Vocational Schools ◽  
Participation Motivation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

Artificial Neural Network to Predict the Thermal Drawdown of Enhanced Geothermal System

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
S. N. Pandey ◽  
M. Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mass Flux ◽  
Operating Conditions ◽  
Percentage Error ◽  
Geothermal System ◽  
Fracture Aperture ◽  
Enhanced Geothermal System ◽  
Fracture Transmissivity ◽  
Artificial Neural

Abstract This work presents the prediction of thermal drawdown of an enhanced geothermal system (EGS) using artificial neural network (ANN). A three-dimensional numerical model of EGS was developed to generate the training and testing data sets for ANN. We have performed a quantitative study of geothermal energy production for various injection operating conditions and reservoir fracture aperture. Input parameters for ANN include temperature, mass flux, pressure, and fracture transmissivity, while the production well temperature is the output parameter. The Levenberg–Marquardt back-propagation learning algorithm, the tan-sigmoid, and the linear transfer function were used for the ANN optimization. The best results were obtained with an ANN architecture composed of eight hidden layers and 20 neurons in the hidden layer, which made it possible to predict the production temperature with a satisfactory range (R2 > 0.99). An appropriate accuracy of the ANN model was obtained with a percentage error less than (± 4.5). The results from the numerical simulations suggest that fracture transmissivity has less effect on thermal drawdown than the injection mass flux and temperature. From our results, we confirm that ANN modeling may predict the thermal drawdown of an EGS system with high accuracy.

Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

scholarly journals Photodegradation of roxarsone in the aquatic environment: influencing factors, mechanisms and artificial neural network modeling

2021 ◽  
Author(s):  
Jizhong Meng ◽  
Arong Arong ◽  
Shoujun Yuan ◽  
Wei Wang ◽  
Juliang Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Aquatic Environment ◽  
Feed Additive ◽  
Neural Network Modeling ◽  
Factors Affecting ◽  
Indirect Photolysis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Roxarsone (ROX) is an organoarsenic feed additive, and can be discharged into aquatic environment. ROX can photodegrade into more toxic inorganic arsenics, causing arsenic pollution. However, the photodegradation behavior of ROX in aquatic environment is still unclear. To better understand ROX photodegradation behavior, this study investigated the ROX photodegradation mechanism and influencing factors, and modeled the photodegradation process. The results showed that ROX in the aquatic environment was degraded to inorganic As(III) and As(V) under light irradiation. The degradation efficiency was enhanced by 25 % with the increase of light intensity from 300 µW/cm2 to 800 µW/cm2 via indirect photolysis. The photodegradation was temperature dependence, but was only slightly affected by pH. Nitrate ion (NO3−) had an obvious influence, but sulfate, carbonate, and chlorate ions had a negligible effect on ROX degradation. Dissolved organic matter (DOM) in the solution inhibited the photodegradation. ROX photodegradation was mainly mediated by reactive oxygen species (in the form of single oxygen 1O2) generated through ROX self-sensitization under irradiation. Based on the data of factors affecting ROX photodegradation, ROX photodegradation model was built and trained by an artificial neural network (ANN), and the predicted degradation rate was in good agreement with the real values with a root mean square error of 1.008. This study improved the understanding of ROX photodegradation behavior and provided a basis for controlling the pollution from ROX photodegradation.

scholarly journals An Evaluation Model of Subgrade Stability Based on Artificial Neural Network

2020 ◽  
Vol 10 (5) ◽  
pp. 679-688
Author(s):  
Youkun Cheng ◽  
Zhenwu Shi ◽  
Fajin Zu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Mapping Function ◽  
Traffic Load ◽  
Fuzzy Neural ◽  
Artificial Neural ◽  
The Law ◽  
Artificial Neural Network Ann

In special areas, the highways may suffer from such diseases as deformation, cracking, subsidence, and potholes, making highway maintenance a complex and difficult task. To obtain the law-term deformation law of the subgrade and accurately evaluate the subgrade and pavement stability, this paper establishes a subgrade stability evaluation model based on artificial neural network (ANN). Firstly, the law of unstable subgrade deformation in highways of special areas was derived by analyzing the influencing factors on subgrade stability, namely, temperature field, moisture field, and traffic load. Next, the correlations between input and output characteristic quantities were extracted, and used to construct the nonredundant mapping function between influencing factors of subgrade unstable deformation and the levels of subgrade stability. Finally, a fuzzy neural network (FNN) was constructed based on Takagi-Sugeno model, realizing the evaluation of subgrade stability. The proposed model was proved effective and accurate through experiments.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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