A neuro-fuzzy decoupling approach for real-time drying room control in meat manufacturing

Zhang, Wei; Ma, Hong; Yang, Simon X.

doi:10.1016/j.eswa.2014.09.013

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…Menlik et al (2010) predicted the drying behaviors (MC, moisture ratio (MR), and drying rate) of apple slices in the freeze‐drying method by ANNs. Zhang et al (2015) proposed as separation system for temperature and relative humidity of control systems. Simulation results revealed significant reductions in fluctuation of relative humidity values.…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

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In this study, three different apple cultivars were dried using five different drying methods and moisture ratio (MR), moisture content (MC) and drying rate values were determined. Then, different machine learning algorithms (artificial neural network, k‐nearest neighbors, random forest, gaussian processes, and support vector regression) were used to estimate MR, MC and drying rate. For MR estimation of Golden Delicious, Oregon Spur and Granny Smith cultivars, Random Forest was most successful algorithm with correlation coefficients (R) of 0.9800, 0.9873, and 0.9841, respectively. This was followed by SVR with R: 0.9323 for Golden Delicious, ANN with R: 0.9766 for Oregon Spur and 5‐NN with R: 0.9827 for Granny Smith. MC and drying rate estimation results showed that RF, SVR, and k‐NN achieved higher R for all cultivars. It was concluded that machine learning algorithms are an effective approach for the accurate estimation of the drying characteristics of apple slices. Practical applications Machine learning‐like precise modeling techniques are used to estimate the drying characteristics of agricultural commodities. Models should be assessed and compared for optimization of drying conditions and operational costs. Machine learning models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting drying behaviors.

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Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

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“…Zhao et al [10] developed a novel online moisture monitoring method for vacuum drying lithium-ion battery powder. Zhang et al [11] proposed a real-time temperature and humidity decoupling control method. A modified inhalation hygrometer was employed as a relative humidity measurement unit and an adaptive neuro-fuzzy inference system was used to control the temperature and relative humidity of the drying chamber.…”

Section: Introductionmentioning

confidence: 99%

Real-time monitoring system for quality monitoring of jujube slice during drying process

Cao

Yao

Zang

et al. 2022

International Journal of Agricultural and Biological Engineering

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The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring. Firstly, machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time. Secondly, correlation models between color parameter (a* value) and nutritional quality attributes (vitamin C, reducing sugar) were established to predict vitamin C and reducing sugar content of jujube slices during the drying process. Finally, the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument, and the real-time monitoring system was tested and validated. Results showed that: the changing trends of color (L*, a*, and b* values) monitored by the system were basically the same as the results detected by the color difference meter, and the average errors of L*, a*, and b* values were 0.93, 0.52, and 0.73, respectively. The average relative error of moisture content between the system monitoring and manual static detection was 0.18%. The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g on dry basis and 0.71g/100 g on dry basis, respectively. The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.

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“…9 Decoupling control is a practical and popular method these years, and its basic idea is to design a decoupling compensator to make the transfer function matrix of the generalized system composed of the decoupling compensator and the controlled object into a diagonal matrix, thereby decoupling multivariable systems into independent single-variable systems. 10 This design approach is gaining more and more attention and generally combines with other advanced control methods such as adaptive control, 11,12 neural network, [13][14][15][16][17] dynamic feedforward, 18 fuzzy logic, and sliding mode 19 and have obtained higher tracking accuracy and better robustness.…”

Section: Introductionmentioning

confidence: 99%

Dynamic feedforward control in decoupling space for a four-degree-of-freedom parallel robot

Song

Zhao

Jin

et al. 2019

International Journal of Advanced Robotic Systems

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This article proposes a dynamic feedforward control method for a four-degree-of-freedom parallel robot in decoupling space to improve the control accuracy and robust stability. The mass matrix and the gravity component are obtained from the rigid-body dynamic model that is formulated by means of the link Jacobian matrices and the principle of virtual work. Then using the positive definiteness of the mass matrix and singular value decomposition algorithms, a decoupling transformation matrix is obtained to convert the physical joint space to the decoupling modal space. In the modal space, a decoupling closed-loop controller design has been implemented for each driven leg. Afterward, by applying the gravity component of the dynamic model, a feedforward control subsystem has been designed to compensate the influence of gravity load on the parallel robot, which can further reduce the negative impacts caused by modeling inaccuracies. This numerical simulation analysis shows that the ideal control accuracy and robust stability have been achieved using the dynamic feedforward decoupling control method for the nonlinear and strongly coupled systems of the parallel robot. The described controller has a simple structure and can be easily realized in practice.

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A neuro-fuzzy decoupling approach for real-time drying room control in meat manufacturing

Cited by 14 publications

References 11 publications

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Real-time monitoring system for quality monitoring of jujube slice during drying process

Dynamic feedforward control in decoupling space for a four-degree-of-freedom parallel robot

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