Artificial Neural Networks: Principle and Application to Model Based Control of Drying Systems - A Review

Ttayagarajan, T.; Ponnavaikko, M.; Shanmugam, J.; Panda, Rames C.; Rao, P. Gangadhara

doi:10.1080/07373939808917449

Cited by 31 publications

(8 citation statements)

References 49 publications

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“…These are mostly in drying and heat transfer studies such as the control of drying systems; quality degradation during rice and maize drying; modeling of drying process dynamics; prediction of process parameters in spray drying; prediction of rheological parameters of bread dough; optimum prediction of psychometric parameters; evaluation of the surface heat transfer coefficient; and so on. [10,[22][23][24][25][26][27][28][29][30][31][32] In this study, a response surface (RS) model and an artificial neural network (ANN) were developed to predict several quality parameters (drying yield, solubility, color change, total anthocyanin content, and antioxidant activity) of spray-dried pomegranate juice.…”

Section: Comparison Of Artificial Neural Network (Ann) and Response Smentioning

confidence: 99%

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

2009

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Response surface methodology (RSM) is a frequently used method for empirical modeling and prediction in the processing of biological media. The artificial neural network (ANN) has recently grown to be one of the most efficient methods for empirical modeling and prediction, especially for nonlinear systems. This article presents comparative studies between an ANN and RSM in the modeling and prediction of quality parameters of spray-dried pomegranate juice. In this study, the effects of the carrier type, carrier concentration, and concentration of crystalline cellulose in a pomegranate juice spray-drying process were investigated on five quality parameters-drying yield, solubility, color change, total anthocyanin content, and antioxidant activity-using RSM and ANN methods. A central composite rotatable experimental design (CCRD) and a feed-forward multilayered perceptron (MLP) ANN trained using back-propagation algorithms for three independent variables were developed to predict the five outputs. The final selected ANN model (3-10-8-5) was compared to the RSM model for its modeling and predictive abilities. The predictive abilities of both the ANN and RSM were compared using a separate dataset of 18 unseen experiments based on RMSE (root mean square error), MAE (mean absolute error), and R 2 (correlation coefficient) for each output parameter. The results indicate the superiority of a properly trained ANN in capturing the nonlinear behavior of the system and the simultaneous prediction of five outputs.

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Section: Comparison Of Artificial Neural Network (Ann) and Response Smentioning

confidence: 99%

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

2009

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“…Now we apply Crossover and mutation operator of Genetic algorithm to generate Next generation. Similarly for the next generation, we use trained BNN for generating the fitness of every chromosome [8,9,12,13,14,15]. Finally, we collect all those combinations of different fertilizers for which grain production is maximum.…”

Section: Implementation Of Proposed Algorithmmentioning

confidence: 99%

Hybrid computational method for optimizing isabgol production

Singh

Mishra

Punhani

et al. 2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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Hybrid computational method was used on the basis of available experimental data and production-nutrient ratios were obtained to suggest best nutrient combinations for getting more profitable production of Isabgol. Nutrient combinations could be computed for the choice of farmer. However nutrient combination N 54.435 + P 2 O 5 12.097 + K 2 O 4.839 Kgha -1 could be detected as best solution for farmers recommendation.

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“…However, their studies did not address the optimal parameter for the drying operation. In addition, authors in (Ttayagarajan et al 1998) considered the application of GA for optimizing solar system including utilization of the neural networks which feature was to consider the precision and the consistency of the training method. Recently, authors in (Omid, Baharlooei, Ahmadi 2009) proposed simulation of drying kinetics of pistachio nuts.…”

Section: Introductionmentioning

confidence: 99%

The Optimization of Solar Drying of Grain by Using a Genetic Algorithm

Rahman

Mustayen

Mekhilef

et al. 2015

International Journal of Green Energy

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Particularly in the low drying temperature drying is an energy intensive process and requires a suitable optimization technique. This work represents the kinetic simulation and energy efficient optimization technique for drying long grain, i.e. rice. At 30-60 • C temperature the grains have been dried in a solar powered air dryer. After certain time interval the enzymatic activity and the moisture content have been measured. Genetic Algorithm (GA) has been used for the simulation and the optimization process while the experimental data have been used to fit the thin layer drying model. The results indicate that between 40 and 50 • C temperature and the 1.3-1.5 m/s air flow rate the drying time for the rice is around 120-180 min. The kinetic simulation and the quality analysis confirm that the dried products have a better enzymatic activity which resembles the quality standard and the required energy for the optimized drying process is lower than the others.

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Artificial Neural Networks: Principle and Application to Model Based Control of Drying Systems - A Review

Cited by 31 publications

References 49 publications

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

Hybrid computational method for optimizing isabgol production

The Optimization of Solar Drying of Grain by Using a Genetic Algorithm

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