Process control based on principal component analysis for maize drying

Liu, Xueqiang; Chen, Xiaoguang; Wu, Wenfu; Zhang, Yaqiu

doi:10.1016/j.foodcont.2005.06.008

Cited by 21 publications

(6 citation statements)

References 11 publications

(12 reference statements)

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“…Principal component analysis (PCA) is a statistical procedure that was developed by Pearson in 1901. [34] It delivers a roadmap for how to reduce a complex set of data to a lower dimension to disclose the hidden and simplified structure that often underlies it. Mathematically if ' ' is the original dataset matrix and ' ' is the new transformed matrix of the original dataset, then the goal of PCA is to find orthonormal matrix in Equation 2.…”

Section: Analysis Techniquementioning

confidence: 99%

Study of Low Flow Rate Ladle Bottom Gas Stirring Using Triaxial Vibration Signals

Yenus

Brooks

Dunn

et al. 2017

Metall Mater Trans B

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Section: Analysis Techniquementioning

confidence: 99%

Study of Low Flow Rate Ladle Bottom Gas Stirring Using Triaxial Vibration Signals

Yenus

Brooks

Dunn

et al. 2017

Metall Mater Trans B

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“…[1] The majority of research work on mixed-flow drying conducted and published so far has focused on how to increase the dryer performance and preserve product quality; for example, by improving the dryer control. [2][3][4][5] Although this type of dryer has been studied by many researchers, [6][7][8][9][10] individual processes such as solids transport, air flow, and heat and mass transfer have not yet been sufficiently considered in dryer modeling. The grain flow through laboratory MFDs with different shapes of air ducts was studied by Klinger [11] using colored grains for visualization.…”

Section: Introductionmentioning

confidence: 99%

Experimental Studies on a Newly Developed Mixed-Flow Dryer

et al. 2013

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Mixed-flow dryers are of great importance in worldwide agriculture for the drying of grain, corn, and rice. Unfavorable dryer designs can result in uneven particle and air flow distributions and, thereby, cause inhomogeneous gas-solids contact and drying conditions. As a consequence, the grain drying can locally be very uneven with high fluctuations of the moisture distribution over the dryer cross section. The main reasons are design and construction of the dryer apparatus and the discharge device. A new mixed-flow dryer design has been developed that promises more homogeneous drying, higher energy efficiency, and increased product quality. Firstly, the new dryer design was proved with respect to particle flow. For this purpose, a new test dryer was constructed. A series of particle flow experiments was performed using colored tracer particles. The flow of the tracer particles was observed through a transparent acrylic front wall by image analysis. Based on a comparison with the traditional design, the advantages and disadvantages of the new design were evaluated. The experimental investigations were accompanied by numerical simulations of the particle flow pattern using the discrete element method. The effects of design properties and different air duct arrangements were studied. The present results show that we are at the beginning of a new development concerning the optimization of mixed-flow drying apparatuses.

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“…adaptive modelling of an offset lithographic printing process in Englund and Verikas (2007); and . dynamic modelling of the maize drying process in Liu et al (2006).…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, multivariate techniques have been successfully applied in different complex processes for different purposes such as data mining, image analysis, process monitoring and fault detection diagnosis. Successful applications have been reported in different industrial processes such as:start‐up operation in a steel casting process in Zhang and Dudzic (2006);operation of a copper smelter in Ross (1988);monitoring product quality in the food processing industry both in Sheridan et al (2006) and Yu et al (2003);monitoring of combustion processes in Yu and MacGregor (2004);remote sensing image analysis in Villmann et al (2003);discovering operational strategies in the refinery fluid catalytic cracking process in Sebzalli and Wang (2001);medical image analysis in Li et al (2006);genomics data modelling in Eriksson et al (2004);increase pharmaceutical data process understanding in Jorgensen et al (2004);adaptive modelling of an offset lithographic printing process in Englund and Verikas (2007); anddynamic modelling of the maize drying process in Liu et al (2006).These wide varieties of examples demonstrate that considerable effort has been placed on applying these multivariate tools. Optimizations to par SOM, a software‐based parallel implementation of the SOM was first introduced by Tomsich et al (2000) which provides a better performance compared to other implementation attempts such as the one reported in Boniface et al (1999).…”

Section: Introductionmentioning

confidence: 99%

Application of Self Organizing Map (SOM) to model a machining process

Saraee

Moosavi

Rezapour

2011

Journal of Manufacturing Technology Management

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Purpose-This paper aims to present a practical application of Self Organizing Map (SOM) and decision tree algorithms to model a multi-response machining process and to provide a set of control rules for this process. Design/methodology/approach-SOM is a powerful artificial neural network approach used for analyzing and visualizing high-dimensional data. Wire electrical discharge machining (WEDM) process is a complex and expensive machining process, in which there are a lot of factors having effects on the outputs of the process. In this work, after collecting a dataset based on a series of designed experiments, the paper applied SOM to this dataset in order to analyse the underlying relations between input and output variables as well as interactions between input variables. The results are compared with the results obtained from decision tree algorithm. Findings-Based on the analysis of the results obtained, the paper extracted interrelationships between variables as well as a set of control rules for prediction of the process outputs. The results of the new experiments based on these rules, clearly demonstrate that the paper's predictions are valid, interesting and useful. Originality/value-To the best of the authors' knowledge, this is the first time SOM and decision tree has been applied to the WEDM process successfully.

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Process control based on principal component analysis for maize drying

Cited by 21 publications

References 11 publications

Study of Low Flow Rate Ladle Bottom Gas Stirring Using Triaxial Vibration Signals

Study of Low Flow Rate Ladle Bottom Gas Stirring Using Triaxial Vibration Signals

Experimental Studies on a Newly Developed Mixed-Flow Dryer

Application of Self Organizing Map (SOM) to model a machining process

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