scholarly journals Optimization and Characterization of Ultrasound-Assisted Pectin Extracted from Orange Waste

2021 ◽  
Vol 22 (2) ◽  
pp. 344-357
Author(s):  
Ketema Beyecha Hundie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Content ◽  
Extraction Process ◽  
Infrared Spectrometry ◽  
Spectrometry Analysis ◽  
Performance Indexes ◽  
Orange Waste ◽  
Artificial Neural ◽  
And Performance

The concept of waste to valuable products is a hot topic with exploring ongoing worldwide to minimize food-based feedstocks. This work utilized a citric acid solution and an ultrasoundassisted to extract pectin from orange waste, a critical agroindustry byproduct. Artificial neural network and central composite design were utilized to assess the extraction of pectin using different levels of the extraction parameters and in turn to optimize the extraction process. The extraction of pectin from orange waste is found to be highly affected by pH solution and ultrasound power. The result of an artificial neural network was found to be better in terms of prediction capability and performance indexes. Fourier transform infrared spectrometry analysis confirmed the existence of functional groups in the fingerprint region of orange waste pectin. Ash and crude protein content of orange wastes are found to be low; meaning low ash and protein content contributes to better gelling ability of the pectin. The extracted pectin has a higher degree of esterification. The result of the current work highlighted that orange wastes are a good source of pectin. In addition, the extracted pectin from orange wastes can be used as a food additive as it fulfills all the standard requirements pectin for application.

scholarly journals A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 279 ◽  
Author(s):  
Yi Dong ◽  
Jianmin Liu ◽  
Yanbin Liu ◽  
Xinyong Qiao ◽  
Xiaoming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Heavy Duty ◽  
Performance Indexes ◽  
Heavy Duty Diesel Engine ◽  
Artificial Neural ◽  
Heavy Duty Diesel ◽  
And Performance

In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.

Prediction of Characteristic and Performance of Laser Cladding for Al Alloy Based on Artificial Neural Network

2008 ◽  
Vol 35 (10) ◽  
pp. 1632-1636 ◽  
Author(s):  
黄安国 Huang Anguo ◽  
李刚 Li Gang ◽  
汪永阳 Wang Yongyang ◽  
李磊 Li Lei ◽  
李志远 Li Zhiyuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Laser Cladding ◽  
Al Alloy ◽  
Artificial Neural ◽  
And Performance

scholarly journals Simultaneous Determination of Some Components in Detergent Washing Powder by Mid-Infrared Spectrometry and Artificial Neural Network

2011 ◽  
Vol 94 (1) ◽  
pp. 322-326
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Mohammad Babaei Rouchi ◽  
Nafiseh Khoddami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Sodium Tripolyphosphate ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Feed Forward Back Propagation ◽  
Powder Samples ◽  
Artificial Neural

Abstract A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800–1550 cm−1). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

Prediction of the Properties of an Alumina Green Body Using an Artificial Neural Network by a New PSO-Gain Backpropagation Algorithm

2008 ◽  
Vol 368-372 ◽  
pp. 1642-1644
Author(s):  
Bing Li ◽  
Hui Ling Zhong ◽  
Hong Jie Li ◽  
Ling Chen ◽  
Lin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bending Strength ◽  
Ceramic Materials ◽  
Linear Shrinkage ◽  
Bp Algorithm ◽  
Green Body ◽  
The Neural Network ◽  
Artificial Neural ◽  
And Performance

Artificial neural networks have been successfully used in classification, formulation optimization, defect diagnosis and performance prediction in ceramic industry. However, an artificial neural network based on the traditional backpropagation (BP) algorithm showed some disadvantages in mapping the nonlinear relationship between the composition and contents of the ceramic materials and their properties. In this paper, a new PSO-Grain (Particle Swarm Optimization Gain) BP algorithm was introduced, and an improved artificial neural network model was employed to predict the properties of an alumina green body. The training performance of the neural network using the PSO-Gain BP algorithm was analyzed and it was indicated the POS-Gain BP based neural network could reduce convergence to local minima and was more efficient than the traditional BP based network. The prediction accuracy of the properties such as linear shrinkage and bending strength using the PSO-Gain BP based neural network was higher than that of the BP based neural network.

scholarly journals ARTIFICIAL NEURAL NETWORK BASED ONLINE SENSOR CALIBRATION AND COMPENSATION

2014 ◽  
pp. 74-78
Author(s):  
Shakeb A. Khan ◽  
Tarikul Islam ◽  
Gulshan Husain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Modeling ◽  
Sensor Response ◽  
Modeling Technique ◽  
Sensor Calibration ◽  
Online Sensor ◽  
Artificial Neural ◽  
And Performance ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) based generalized online method for sensor response linearization and calibration. Inverse modeling technique is used for sensor response linearization. Multilayer ANN is used for inverse modeling of sensor. The inverse model based technique automatically compensates the associated nonlinearity and estimates the measurand. The scheme is coded in MATLAB® for offline training and for online measurement and successfully implemented using NI PCI-6221 Data Acquisition (DAQ) card and LabVIEW® software. Manufacturing tolerances, environmental effects, and performance drifts due to aging bring up a need for frequent calibration, this ANN based inverse modeling technique provides greater flexibility and accuracy under such conditions.

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