Neural network based approach for optimization of industrial chemical processes

Nascimento, Cláudio Augusto Oller do; Giudici, Reinaldo; Guardani, Roberto

doi:10.1016/s0098-1354(00)00587-1

Cited by 148 publications

(78 citation statements)

References 10 publications

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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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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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“…In general, the weight is adjusted using some learning algorithms, where in this work, the Back Propagation (BP) learning algorithm [28] was used in the training. The BP learning algorithm can be described as the following [24].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Concurrent Modeling of Magnetic Field Parameters, Crystalline Structures, and Ferromagnetic Dynamic Critical Behavior Relationships: Mean-Field and Artificial Neural Network Projections

Laosiritaworn¹,

Laosiritaworn²

2014

Journal of Magnetics

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In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

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“…Many applications in modeling and optimization of chemical processes are available in the open literature (Nascimento et al, 1999;Nascimento et al, 2000). In nuclear technology, the use of neural networks dates bake to the end of the 80's.…”

Section: Neural Networkmentioning

confidence: 99%

Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

Migliavacca

Rodrigues

Nascimento

2002

Braz. J. Chem. Eng.

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-Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.

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Neural network based approach for optimization of industrial chemical processes

Cited by 148 publications

References 10 publications

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

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

Concurrent Modeling of Magnetic Field Parameters, Crystalline Structures, and Ferromagnetic Dynamic Critical Behavior Relationships: Mean-Field and Artificial Neural Network Projections

Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

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