Experimental Study and Artificial Neural Networks Simulation of Activated Carbon Synthesis in Fluidized Bed Reactor

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Hassan Hashemipour ◽  
Saeid Baroutian ◽  
Esmail Jamshidi ◽  
Alireza Abazari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fluidized Bed ◽  
Fluidized Bed Reactor ◽  
Operating Conditions ◽  
Synthesis Process ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Activation Temperature ◽  
Artificial Neural

In this work, thermal activation of pistachio shell was studied in a fluidized bed reactor. The effects of operating conditions on the pore development within the char particles were studied experimentally. The results showed that activation temperature, residence time, oxidizing gas type and concentration have main effects on the Iodine adsorption capacity of the product. The highest surface area was obtained using steam activation at temperature 850°C for 45 min. The synthesis process was also simulated using artificial neural networks (ANNs) to estimate the Iodine number of the product. The present work applied a Tan-sigmoid transfer function in three layers in the feed forward neural network with back propagation algorithm. The results of the network are in good agreement with the experimental data with a maximum relative deviation of 0.015%.

scholarly journals Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method

2012 ◽  
Vol 12 (1) ◽  
pp. 37-45 ◽  
Author(s):  
G-A. Tselentis ◽  
E. Sokos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Target Function ◽  
Historical Earthquakes ◽  
Strong Nonlinearity ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Abstract. In this paper we suggest the use of diffusion-neural-networks, (neural networks with intrinsic fuzzy logic abilities) to assess the relationship between isoseismal area and earthquake magnitude for the region of Greece. It is of particular importance to study historical earthquakes for which we often have macroseismic information in the form of isoseisms but it is statistically incomplete to assess magnitudes from an isoseismal area or to train conventional artificial neural networks for magnitude estimation. Fuzzy relationships are developed and used to train a feed forward neural network with a back propagation algorithm to obtain the final relationships. Seismic intensity data from 24 earthquakes in Greece have been used. Special attention is being paid to the incompleteness and contradictory patterns in scanty historical earthquake records. The results show that the proposed processing model is very effective, better than applying classical artificial neural networks since the magnitude macroseismic intensity target function has a strong nonlinearity and in most cases the macroseismic datasets are very small.

Analysis on Pattern Classification using Artificial Neural Networks

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
S. Kumar ◽  
S. Singh ◽  
V K Mishra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Artificial neural networks (ANN) is one of the most dynamic research and application areas for pattern classification. ANN is the branch of Artificial Intelligence (AI). The network is trained by 'n' number of algorithm like back propagation algorithm. The different combinations of performance functions are used for training the ANN. The back propagation neural network (BPNN) can be used as a highly successful algorithm for pattern classification with suitable combination of performance functions while training and learning ANN. When the maximum likelihood algorithm was compared with back propagation neural network method, the BPNN was more accurate than other algorithms. A Multilayer feed-forward neural network algorithm is also used for pattern classification. However BPNN gives more effective results than other pattern classification algorithms. Handwriting Recognition (or HWR) is the ability of a machine to receive and interpret handwritten input from different sources like paper documents, photographs, touch-screens and other input devices. Various performance functions is examined in this paper so as to get to a conclusion that which function would be better for usage in the network to produce an efficient and effective system. The training of back propagation neural network is done with the application of Offline Handwritten Character Recognition.

scholarly journals Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1101
Author(s):  
Elena M. Tosca ◽  
Roberta Bartolucci ◽  
Paolo Magni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Molecular Descriptor ◽  
Biological Properties ◽  
Training Dataset ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Qspr Model ◽  
Artificial Neural ◽  
Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

scholarly journals Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 705
Author(s):  
Josué Trejo-Alonso ◽  
Carlos Fuentes ◽  
Carlos Chávez ◽  
Antonio Quevedo ◽  
Alfonso Gutierrez-Lopez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hydraulic Conductivity ◽  
Input Data ◽  
Logistic Function ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Back Propagation Algorithm ◽  
Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

2021 ◽  
Author(s):  
Mateus Alexandre da Silva ◽  
Marina Neves Merlo ◽  
Michael Silveira Thebaldi ◽  
Danton Diego Ferreira ◽  
Felipe Schwerz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Back Propagation ◽  
Metropolitan Region ◽  
Climate Variables ◽  
Back Propagation Algorithm ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

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