Application of genetic algorithm for optimization of vegetable oil hydrogenation process

Izadifar, Mohammad; Jahromi, Mansoor Zolghadri

doi:10.1016/j.jfoodeng.2005.08.044

Cited by 81 publications

(43 citation statements)

References 7 publications

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“…It is also used for an optimal value of a complex function by simulation of the biological evolutionary process based on crossover and mutation (Izadifar and Jahromi, 2007). The GA can be used to select the appropriate number of nodes in the hidden layer of ANNs.…”

Section: Introductionmentioning

confidence: 99%

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Nasirahmadi¹,

Abbaspour‐Fard²,

Emadi³

et al. 2014

International Agrophysics

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A b s t r a c t. The present investigation deals with analyzing the compressive strength properties of two varieties (Tarom and Fajr) of parboiled paddy and milled rice including: ultimate stress, modulus of elasticity, rupture force and rupture energy. Combined artificial neural network and genetic algorithm were also applied to model these properties. The parboiled samples were prepared with three soaking temperatures (25, 50 and 75°C) and three steaming times (10, 15 and 20 min). The samples were then dried to final moisture contents of 8, 10 and 12% (w.b.). In general, Tarom variety had higher compressive strength properties for paddy and milled rice than Fajr variety. With increase in steaming time from 10 to 20 min, all mentioned properties increased significantly, whereas these properties were decreased with increasing moisture content from 8 to 12% (w.b.). Coupled artificial neural network and genetic algorithm model with one hidden layer, three inputs (soaking temperature, steaming time and moisture content), was developed to predict the compressive strength properties as model outputs. Results indicated that this model could predict these properties with high correlation and low mean squared error.K e y w o r d s: artificial neural network, genetic algorithm, paddy, milled rice INTRODUCTION Rice (Oryza sativa L.) is the principal food cereal in the world and it is the main food of over half of the world population (Razavi and Farahmandfar, 2008). The majority of Iranian rice (75%) is grown in Mazandaran and Guilan provinces (Zareiforoush et al., 2010). The production rate is still far from the country rice self-sufficiency. Increasing the grain severity and hence reducing the processing losses could be a possible way of compenzation for the increasing demand. The primary step in rice processing is dehusking of paddy, which results in brown rice. Polishing or removing of bran to yield white rice is the second step. Knowledge of the compressive strength properties of rice is important in the design of milling equipment to predict their cracking behaviour and to minimize the losses during handling, drying, cleaning and milling processes. For this reason, extensive studies have been done in this area, that would be reviewed here. Shitanda et al. (2001) explored the physical and mechanical properties of paddy and rice. They showed that shortgrain rice was harder than long-grain variety and had less broken grain. They noted that husking characteristics of rice are related to shape and size of rice. Cao et al. (2004) explored the effect of moisture content on some mechanical properties of brown rice. They showed that moisture content has a significant effect on mechanical properties ie compressive strength and tensile strength decreased with increasing moisture content. Corrêa et al. (2007) explored mechanical behaviour of grains in rice processing. They showed that compressive force was significantly affected by processing. Zareiforoush et al. (2010) investigated the mechanical properties of two rice varieti...

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Section: Introductionmentioning

confidence: 99%

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Nasirahmadi¹,

Abbaspour‐Fard²,

Emadi³

et al. 2014

International Agrophysics

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“…GA can be simply described as follows (Izadifar and Jahromi, 2007). First, a population of individuals (genes) is randomly generated.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In recent years, ANN has been successfully applied to optimize reaction conditions (Huang et al, 2007), estimate cell growth models (García-Gimeno et al, 2005), and realize the on-line adaptive control in fed-batch fermentation (Duan et al, 2006). Genetic algorithm (GA), based on principles of evolution, is an efficient tool used to search the optimum inputs predicted by the ANN model (Erenturk and Erenturk, 2007;Izadifar and Jahromi, 2007). In this work, the RSM and ANN models for PCA yield were constructed using common data to enhance PCA yield; the prediction accuracy of both models was compared.…”

Section: Introductionmentioning

confidence: 99%

Medium factor optimization and fermentation kinetics for phenazine‐1‐carboxylic acid production by Pseudomonas sp. M18G

Zhang

2007

Biotech & Bioengineering

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We investigated the production of biofungicide phenazine-1-carboxlic (PCA) by Pseudomonas sp. M18G, a gacA-deficient mutant of M18 for PCA high-production. Glucose was chosen as the optimal carbon source and soy peptone as the nitrogen source. A Plackett-Burman design revealed that glucose, soy peptone and NaCl were the most significant factors in PCA fermentation. Response surface methodology (RSM) and artificial neural network (ANN) models involving the significant factors were developed using common data. The prediction accuracy of ANN was slightly higher compared to RSM. The genetic algorithm (GA) was used to search the optimal input space of the trained ANN model and find the corresponding PCA yield. The optimum composition was found to be: glucose 34.3 g L(-1), soy peptone 43.2 g L(-1), NaCl 5.7 g L(-1), and the predictive maximum PCA yield reached 980.1 microg mL(-1). The optimized medium allowed PCA yield to be increased from 673.3 to 966.7 microg mL(-1) after verification experiment tests. Additionally, PCA fermentation kinetics was investigated. Kinetic models based on the modified Logistic and Luedeking-Piret equations were developed, providing a good description of temporal variations of biomass (X), product (P), and substrate (S) in PCA fermentation.

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“…Los resultados mostraron un incremento en la calidad y una reducción en el consumo de energía y tiempo de secado. (Izadifar et al, 2007) Optimización del proceso de hidrogenación de aceite vegetal usando GA.…”

Section: Métodos De Aprendizaje Y Optimizaciónunclassified

Análisis estadistico multivariado como herramienta Estratégica para el Control de procesos de calidad en la industria agroalimentaria

Domínguez

2011

Publ. investig.

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El uso de técnicas de estadística multivariada para el control de procesos y calidad en la industria agroalimentaria ha venido en crecimiento significativo desde la mitad de los años setenta, como una consecuencia de la revolución informática que facilitó el análisis de matrices de datos de gran tamaño. A diferencia de los métodos univariados de exploración de datos, la estadística multivariada utiliza como gran pilar el análisis de información descrita por tres o más variables que pueden ser estudiadas simultá-neamente y comprendidas de una manera rápida, eficiente y sencilla. Debido al extraordinario avance en las máquinas informáticas, hoy es posible aplicar estas metodologías para resolver problemas extremadamente complejos. Este artículo presenta las técnicas de estadística multivariada más reconocidas, así como la compilación de algunos trabajos que sirven como demostración de su aplicabilidad en el campo de los alimentos. Palabras clave: clasificación, exploración, modelamiento, optimización, regresión AbstractThe use of multivariate statistical techniques for quality and process control in the food industry has been growing significantly since the mid-seventies, as a result of the informatics revolution which facilitated the analysis of large data sets. Unlike univariate methods of data exploration, multivariate statistics uses as a major pillar the analysis of information described by three or more variables that can be simultaneously studied and understood in a fast, efficient and easy way. Thanks to the extraordinary advance in computing machines, it is now possible to apply these methodologies to solve extremely complex problems. This article presents the most recognized multivariate statistical techniques, as well as the compilation of some papers that serve as a demonstration of its applicability in the field of foods.

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Application of genetic algorithm for optimization of vegetable oil hydrogenation process

Cited by 81 publications

References 7 publications

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Medium factor optimization and fermentation kinetics for phenazine‐1‐carboxylic acid production by Pseudomonas sp. M18G

Análisis estadistico multivariado como herramienta Estratégica para el Control de procesos de calidad en la industria agroalimentaria

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