A new approach for estimation of PVT properties of pure gases based on artificial neural network model

Moghadassi, Abdolreza; Parvizian, Fahime; Hosseini, S.M.; Fazlali, Alireza

doi:10.1590/s0104-66322009000100019

Cited by 43 publications

(15 citation statements)

References 11 publications

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“…The aim of the training procedure is to adjust the connection weights until the global minimum in the error surface has been reached. The network training process (Moghadassi et al, 2009) is summarized in Fig. 5.…”

Section: Bpannmentioning

confidence: 99%

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Chen

Bai

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R 2 ) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R 2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.

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Section: Bpannmentioning

confidence: 99%

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Chen

Bai

et al. 2016

Hydrol. Earth Syst. Sci.

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“…This technique has caught the interest of most researchers and has today become an essential part of the technology industry, providing a good ground for solving many of the most difficult prediction problems in various areas of engineering applications (Baughman 1995;Guler 2005;Inan et al 2006;Li and Jiao 2002;Moghadassi et al 2009;Mohaghegh 1995;Nascimento et al 2000;Phung and Bouzerdoum 2007;Ü beyli 2009). ANN has also gained vast popularity in solving various Civil Engineering problems (Baughman 1995;Beale and Demuth 2013;Chen et al 1995;Flood and Kartam 1994;Hasancebi and Dumlupınar 2013;Kang and Yoon 1994;Kirkegaard and Rytter 1994;Neaupane and Adhikari 2006;Pandey and Barai 1995;Rafiq et al 2001).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Imam

Anifowose

Azad

2015

International Journal of Concrete Structures and Materials

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Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor (C f ) and through the residual strength (M res ). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

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“…LM is the fastest training algorithm for networks of moderate size and it has the memory reduction feature to be used when the training set is large. One of the most important general purpose back propagation training algorithms is SCG (Dehghani et al, 2006; Moghadassi et al, 2009).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Therefore, a model based on some experimental results is proposed to predict the required data instead of doing more experiments. Artificial neural network (ANN) is a model that attempts to mimic simple biological learning processes and simulate specific functions of human nervous system (Moghadassi et al, 2009). This model creates a connection between input and output variables and keeps the underlying complexity of the process inside the system.…”

Section: Introductionmentioning

confidence: 99%

An expert model for estimation of the performance of direct dimethyl ether synthesis from synthesis gas

Moradi

Parvizian

2011

Can J Chem Eng

Self Cite

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In this work, an artificial neural network (ANN) has been trained and tested for estimation of the performance of direct synthesis of dimethyl ether (DME) from synthesis gas. Yield and selectivity of DME production and also conversion of CO could be predicted when temperature and pressure of reactor and H 2 /CO molar ratio in feed have been specified. The results of ANN estimation for yield of DME, selectivity of DME and CO conversion are in very good agreement with experimental values. For this development, database was collected from our previous experiment. The accuracy and trend stability of the trained networks were tested against unseen data. Different training schemes for the back-propagation learning algorithm, such as: Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Gradient Descent with Momentum (GDM), variable learning rate Back propagation (GDA) and Resilient back Propagation (RP) methods were used. The SCG algorithm with seven neurons in the hidden layer shows the best suitable algorithm with the minimum average absolute relative error 0.05231.Dans ce travail, une réseau de neurones artificiels (RNA) aété instruit et testé pour l'estimation du rendement de la synthèse directe d'oxyde de diméthyle (ODM)à partir de gaz de synthèse. On peut prédire le rendement et la sélectivité de la production de ODM et la conversion de CO lorsque la température et la pression du réacteur ainsi que le rapport molaire H2/CO de la charge de départ ontété spécifiés. Les résultats de l'estimation RNA du rendement de ODM, de la sélectivité de la production de ODM et la conversion de CO sont en bon accord avec les valeurs expérimentales. Pour ce dévelopment, une base de données de notre précédente expérimentation aété collectée. L'exactitude et la stabilité des tendances des réseaux instruits ontété testées par rapportà des données anonymes. Différents systèmes d'instruction pour l'algorithme de rétropropagation d'apprentissage ontété utilisés, par exemple, le gradient conjugué pondéré (SCG), Levenberg-Marquardt (LM), la descente de gradient avec momentum (GDM), la rétropropagationà taux d'apprentissage variable (GDA) et la rétropropagation résiliente (RP). L'algorithme SCG avec sept neurones dans la couche cachée se démontre commeétant l'algorithme le mieux approprié avec une erreur relative absolue moyenne minimale de 0,05231.

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A new approach for estimation of PVT properties of pure gases based on artificial neural network model

Cited by 43 publications

References 11 publications

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

An expert model for estimation of the performance of direct dimethyl ether synthesis from synthesis gas

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