Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Nandi, Somnath; Badhe, Yogesh; Lonari, Jayaram; Sridevi, U. K.; Rao, B.S.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D.

doi:10.1016/s1385-8947(03)00150-5

Cited by 108 publications

(67 citation statements)

References 27 publications

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“…The GA is one of the strategic randomized search techniques, which are well known for its robustness in finding the optimal or near-optimal solution since it does not depend on gradient information in its walk of life to find the best solution. Various kinds of algorithm were reported by previous researchers (Tarca et al, 2002;Nandi et al, 2002Nandi et al, , 2004Kundu et al, 2009;Bhatti et al, 2011).…”

Section: Process Variables Responses/ Dependent Variablesmentioning

confidence: 99%

“…The detail hybrid algorithm for simultaneous modelling and multiobjective optimization has been developed in previous publication which focused on plasma reactor application (Istadi & Amin, 2005, 2006, 2007. They reported that the hybrid ANN-GA technique is a powerful method for process modelling and multi-objectives optimization (Nandi et al, 2002(Nandi et al, , 2004Ahmad et al, 2004;Stephanopoulos & Han, 1996;Huang et al, 2003;Radhakrishnan & Suppiah, 2004;Fissore et al, 2004;Nandi et al, 2002Nandi et al, , 2004Ahmad et al, 2004;Kundu et al, 20009;Marzbanrad & Ebrahimi, 2011;Bhatti et al, 2011). The method is better than other technique such as response surface methodology (RSM) (Istadi & Amin, 2006, 2007, particularly for complex process model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

Amin¹,

Istadi²

2012

Real-World Applications of Genetic Algorithms

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Section: Process Variables Responses/ Dependent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

Amin¹,

Istadi²

2012

Real-World Applications of Genetic Algorithms

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“…SVM was first reported in the field of solid catalysis as a classifier (Baumes et al, 2006;Serra, Baumes, Moliner, Serna & Corma, 2007). It can be used also for regression, and by SVM the inputs are mapped into a high-dimensional space in nonlinear manner and then the modified inputs are correlated linearly with the output (Fan et al, 2005;Nandi et al, 2004). These reported results show clearly the superior generalization capability of a SVM and better availability through open source program makes SVM more applicable than an artificial neural network.…”

Section: Support Vector Regression (Model 3)mentioning

confidence: 99%

Facile Tool for Prediction of Catalytic Activity — Cu-Lanthanoid Catalyst for Methanol Synthesis

Omata¹,

Umegaki²,

Yamada³

2011

Artificial Neural Networks - Application

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“…In recent years, they have been introduced as a new technique for solving a variety of learning, classification and prediction problems (Cristianini & Shawe-Taylor, 2000). Support vector regression (SVR), the regression version of SVM, was developed to estimate regression functions (Drucker et al, 1997) and similar to SVM, it is capable of solving non-linear problems (Nandi et al, 2004). SVR models have been successfully applied across a broad range of areas in engineering, science and economics (e.g.…”

mentioning

confidence: 99%

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Faridi

Golian

Mottaghitalab³

et al. 2013

Span. j. agric. res.

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IntroductionCorn (Zea mays L.) is the main energy source in diets for intensively reared avian species (broilers and ducks), therefore accurate information on its effective energy content is of importance to nutritionists. A number of studies have been conducted to estimate the metabolizable energy (ME) content of corn based on its physical characteristics and chemical composition (e.g. Leeson et al., 1993;Zhao et al., 2008). The energy content of feedstuffs depends strongly on their chemical composition. Nutritionists are interested in using models that predict the nutritive value of poultry feedstuffs accurately. Recently, artif icial neural network (ANN) models have received attention among poultry nutritionists, e.g. for estimating the ME of poultry offal meal (Ahmadi et al., 2008) and sorghum grain (Sedghi et al., 2011) AbstractSupport vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AME n ), true metabolizable energy (TME), and TME corrected for nitrogen (TME n ) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg -1 ) of corn were crude protein, ether extract, crude fibre, and ash (g kg -1 ). Goodness of fit of the models was examined using R 2 , mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R 2 ) on the full data set (0.937 for AME, 0.954 for AME n , 0.860 for TME, and 0.937 for TME n ) was better than that of ANN (0.907 for AME, 0.922 for AME n , 0.744 for TME, and 0.920 for TME n ) and MLR (0.887 for AME, 0.903 for AME n , 0.704 for TME, and 0.902 for TME n ). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.Additional key words: maize; poultry; nutritive value; chemical composition; artificial neural network; multiple linear regression. Abbreviations used: AME (apparent metabolizable energy); AME n (apparent metabolizable energy corrected for nitrogen); ANN (artificial neural network); CF (crude fibre); CP (crude protein); EE (ether extract); ME (metabolizable energy); MLR (multiple linear regression); SVM (support vector machine); SVR (support vector regression); TME (true metabolizable energy); TME n (true metabolizable energy corrected for nitrogen); VIF (variance inflation factor).

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Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Cited by 108 publications

References 27 publications

Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

Facile Tool for Prediction of Catalytic Activity — Cu-Lanthanoid Catalyst for Methanol Synthesis

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

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