The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Soltani, Soroush; Shojaei, Taha Roodbar; Khanian, Nasrin; Choong, Thomas Shean Yaw; Rashid, Umer; Nehdi, Imededdine Arbi; Yusoff, Rozita

doi:10.1039/d0ra00892c

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…However, the performance of models developed using RSM is limited. Recently, with the advent of the 4th industrial revolution, arti cial neural network has been considered as a more e cient tool for model predictions of biodiesel production [17] and even in food processing [18]. An arti cial neural network (ANN) is a mathematical algorithm which has the capability of relating the input and output parameters, learning from examples through iteration, without requiring a prior knowledge of the relationships of the process parameters [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Ogundele

Akintola

Fasogbon

et al. 2022

Preprint

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Cowpea is widely grown and consumed in Sub-Saharan Africa because of its low cost and high mineral, protein, and other nutritional content. Nonetheless, cooking it takes considerable time, and there have been attempts on techniques for speeding up the cooking process without compromising its nutritious value. Infrared heating has recently been proposed as a viable way of preparing instantized cowpea grains that take a short amount of time to cook while maintaining desired sensory characteristics. Despite this, only a few studies have shown the impact of moisture, temperature, and cooking time on cooking characteristics such as bulk density, water absorption (WABS), and the pectin solubility of infrared heated cowpea precooked using this technology. Artificial neural network was used as a machine learning tool to study the effect of a prediction model on the infrared heating performance and cooking characteristics of precooked cowpea seeds. With R values of 0.987, 0.991, and 0.938 for the bulk density, WABS, and pectin solubility, respectively, the prediction model created in this study utilizing an artificial neural network (a type of machine learning) outperformed the traditional linear, 2-Factor Interaction (2FI), and quadratic models.

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Section: Introductionmentioning

confidence: 99%

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Ogundele

Akintola

Fasogbon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the performance of models developed using RSM is limited. Recently, with the advent of the 4 th industrial revolution, artificial neural network (ANN) has been considered as a more efficient tool for model predictions of biodiesel production 17 and even in food processing 18 . ANN is a mathematical algorithm which has the capability of relating the input and output parameters, learning from examples through iteration, without requiring a prior knowledge of the relationships of the process parameters 18 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive model for evaluating the cooking characteristics of moisture conditioned and infrared heated cowpea

Ogundele

Akintola

Fasogbon

et al. 2022

Sci Rep

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Cowpea is widely grown and consumed in sub-Saharan Africa because of its low cost and high mineral, protein, and other nutritional content. Nonetheless, cooking it takes considerable time, and there have been attempts on techniques for speeding up the cooking process without compromising its nutritious value. Infrared heating has recently been proposed as a viable way of preparing instantized cowpea grains that take a short amount of time to cook while maintaining desired sensory characteristics. Despite this, only a few studies have shown the impact of moisture, temperature, and cooking time on cooking characteristics such as bulk density, water absorption (WABS), and the pectin solubility of infrared heated cowpea precooked using this technology. Artificial neural network was used as a machine learning tool to study the effect of a prediction model on the infrared heating performance and cooking characteristics of precooked cowpea seeds. With R values of 0.987, 0.991, and 0.938 for the bulk density, WABS, and pectin solubility, respectively, the prediction model created in this study utilizing an artificial neural network (a type of machine learning) outperformed the traditional linear, 2-factor interaction, and quadratic models.

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“…The neural network is a parallel dispersed mainframe containing uncomplicated handling components known as neurons that have biases for accumulating trial data and prepare it ready to be utilized 36. ANN works like the human brain in two ways, i.e., acquiring the information from its nature by a learning procedure and collecting the obtained information utilizing the power of interneuron networks known as synaptic weights 37.…”

Section: Introductionmentioning

confidence: 99%

Porosity Estimation of Mesoporous TiO₂‐ZnO Nanocrystalline by Artificial Neural Network Modeling

et al. 2021

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Artificial neural network was introduced for modeling and verifying the relative significance of operational variables to attain the best possible average pore diameter (Dp) of the mesoporous TiO2‐ZnO nanocrystalline synthesized by microwave‐assisted hydrothermal method. To optimize the system, the autonomous variables calcination temperature and concentrations of D‐glucose, polyethylene glycol, and Zn‐Ti were employed as input factors, Dp was designated as output. Dp was achieved from the implementation of the experimental model of the variables by the Barrett‐Joyner‐Halenda method. The network was trained by IBP, BBP, LM, QP, and GA algorithms as a model. The BBP‐4‐7‐1 model provided the optimum numerical properties among four other algorithms. Furthermore, the calcination temperature acted as the most efficient variable whilst D‐glucose concentration showed the least effect. By validating the microwave‐assisted hydrothermal conditions, in situ polymerization of poly(sodium 4‐styrene sulfonate) was introduced to activate mesopore walls by –SO3H functional groups used for ester production by transesterification of used cooking oil, resulting in a methyl ester content of 97.4 % and excellent reusability for ten sequential reactions without any further regeneration.

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The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Abstract: In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.

Cited by 8 publications

References 37 publications

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Machine learning predictive model for evaluating the cooking characteristics of moisture conditioned and infrared heated cowpea

Porosity Estimation of Mesoporous TiO₂‐ZnO Nanocrystalline by Artificial Neural Network Modeling

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The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Abstract: In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.

Cited by 8 publications

References 37 publications

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network

Machine learning predictive model for evaluating the cooking characteristics of moisture conditioned and infrared heated cowpea

Porosity Estimation of Mesoporous TiO2‐ZnO Nanocrystalline by Artificial Neural Network Modeling

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Product

Resources

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Porosity Estimation of Mesoporous TiO₂‐ZnO Nanocrystalline by Artificial Neural Network Modeling