96/03623 Deep hydrodesulphurization of atmospheric gas oil. Effects of operating conditions and modelling by artificial neural network techniques

1996 ◽  
Vol 37 (4) ◽  
pp. 257
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Gas Oil ◽  
Artificial Neural

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

scholarly journals A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

scholarly journals Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5886
Author(s):  
Jiwon Park ◽  
Jungkeun Cho ◽  
Heewon Choi ◽  
Jungsoo Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Alternative Fuels ◽  
Combustion Engine ◽  
Gas Production ◽  
Operating Conditions ◽  
Hydrogen Yield ◽  
Fuel Reforming ◽  
Artificial Neural ◽  
Partial Oxidation Reforming

Facing the reinforced emission regulations and moving toward a clean powertrain, hydrogen has become one of the alternative fuels for the internal combustion engine. In this study, the prediction methodology of hydrogen yield by on-board fuel reforming under a diesel engine is introduced. An engine dynamometer test was performed, resulting in reduced particulate matter (PM) and NOx emission with an on-board reformer. Based on test results, the reformed gas production rate from the on-board reformer was trained and predicted using an artificial neural network with a backpropagation process at various operating conditions. Additional test points were used to verify predicted results, and sensitivity analysis was performed to obtain dominant parameters. As a result, the temperature at the reformer outlet and oxygen concentration is the most dominant parameters to predict reformed gas owing to auto-thermal reforming driven by partial oxidation reforming process, dominantly.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

scholarly journals Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

2020 ◽  
Vol 11 (1) ◽  
pp. 505
Author(s):  
Zozan Saadallah Hussain ◽  
Ahmed J. Ali ◽  
Ahmed A. Allu ◽  
Rakan Khalil Antar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transmission Line ◽  
Circuit Breaker ◽  
Logic Gates ◽  
Operating Conditions ◽  
System Stability ◽  
Phase System ◽  
Three Phase ◽  
Artificial Neural

This paper presents a developed logical tripping scheme to improve conventional protection performance. Adaptive single pole auto reclosure (ASPAR) system is proposed that considers, automatically tripping and reclosing of a multi-shot independent pole technique of a circuit breaker at a predetermined sequence, which can be used to boost the synchronization of the power grid under the transient fault conditions. Moreover, the ASPAR can be utilized to enhance the electrical system stability and reliability at the same operating conditions. Based on the three-phase system, the Artificial neural network (ANN) in this work has been done in order to diagnose and detect healthy and faulted phases. The proposed ANN fault classifier method consists of the logic gates, router circuits, timers, and positive and negative sequence analyses circuit. In addition, it is used to give the ability to recognize a fault type, which by training on the sequence angle values and coordination of the transmission line. Three-phase overhead transmission line including the proposed ASPAR is built in MATLA \SIMULINK environment. Thus the performance ANN-fault classified is tested under different fault conditions. Simulation results show that the proposed ASPAR based on ANN is accurate and well performance. Whereas resultant tripping and reclosing signals of ASPAR are successfully provided that enhances the circuit breaker mechanism under these operating condition.

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.

scholarly journals Process Configuration Studies of Methanol Production via Carbon Dioxide Hydrogenation: Process Simulation-Based Optimization Using Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6608
Author(s):  
Prapatsorn Borisut ◽  
Aroonsri Nuchitprasittichai
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Production Cost ◽  
Operating Conditions ◽  
Price Sensitivity ◽  
Methanol Production ◽  
Artificial Neural

Methanol production via carbon dioxide (CO2) hydrogenation is a green chemical process, which can reduce CO2 emission. The operating conditions for minimum methanol production cost of three configurations were investigated in this work. An artificial neural network with Latin hypercube sampling technique was applied to construct model-represented methanol production. Price sensitivity was performed to study the impacts of the raw materials price on methanol production cost. Price sensitivity results showed that the hydrogen price has a large impact on the methanol production cost. In mathematical modeling using feedforward artificial neural networks, four different numbers of nodes were used to train artificial neural networks. The artificial neural network with eight numbers of nodes showed the most suitable configuration, which yielded the lowest percent error between the actual and predicted methanol production cost. The optimization results showed that the recommended process design among the three studied configurations was the process of methanol production with two reactors in series. The minimum methanol production cost obtained from this configuration was $888.85 per ton produced methanol, which was the lowest methanol production cost among all configurations.

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