On predicting roller milling performance

Campbell, Grant M.; Bunn, P.J.; Webb, Colin; Hook, S.C.W.

doi:10.1016/s0032-5910(00)00348-x

Cited by 89 publications

(53 citation statements)

References 12 publications

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“…However, for GY503 and XN979, the results were not affected so significantly. The particle size distribution is determined by the physical properties of wheat (Dexter and Wood 1996;Gaines 1985), especially the kernel hardness (Campbell et al 2001;Mousia et al 2004;Mehri and Francis 2008). The correlations (R 2 ) between the hardness of five wheat cultivars and the particle sizes of M flour were 0.64, 0.55, and 0.67 for debranning degree 2.5%, 5.0%, and 7.0%, respectively.…”

Section: Particle Size Falling Number and Starch Damagementioning

confidence: 99%

Effects of Different Debranning Degrees on the Qualities of Wheat Flour and Chinese Steamed Bread

Liu

et al. 2010

Food Bioprocess Technol

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Five Chinese wheat cultivars (Anhwei Bai Mai, Gao You 503, Ji Mai 20, Ji Nan 17, and Xi Nong 979, abbreviated as AHBM, GY503, JM20, JN17, and XN979, respectively) were milled after debranning at different degrees. The flour characteristics and qualities of steamed bread made from these flours were evaluated to study the effect of debranning degree. The results showed that the yields of flour of all samples were increased with the augment of debranning degree. The starch damage contents were decreased to a suitable scope for Chinese steamed bread (CSB) making, while the ash contents increased in most of the cases. The effects of debranning on the characteristics of flour were correlated with hardness of wheat cultivars and debranning degree. For AHBM and JM20, the qualities of steamed bread made from unpearled wheat were better than pearled ones. At the same time, for GY503, JN17, and XN979, 5.0% debranning degree improved the characteristics of flour and qualities of CSB.

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Section: Particle Size Falling Number and Starch Damagementioning

confidence: 99%

Effects of Different Debranning Degrees on the Qualities of Wheat Flour and Chinese Steamed Bread

Liu

et al. 2010

Food Bioprocess Technol

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“…To demonstrate the use of the proposed algorithm, let us re-consider a numerical example system [6,7] γ(y, u) = B 1 (y)w 1 + B 2 (y)w 2 + B 3 (y)w 3 + B 4 (y)w 4 (54) where, to simulate the system, a and b are selected as -3 and +3, respectively. To approximate the square root of the probability density function in equation (54), the following polynomial based B-spline function is used: …”

Section: Simulations Resultsmentioning

confidence: 99%

“…These systems are seen in papermaking processes [1,2] , chemical engineering, material science, combustion flame distribution systems and food processing industries. For example, in chemical engineering the control of particle size distribution has always been regarded as an important area of research [3] , whilst in the food processing industry the particle size distribution of wheat flour needs to be carefully predicted and controlled [4] . However, the algorithm design in stochastic distribution control has always been casedependent, and there is no uniform control framework available.…”

Section: Introductionmentioning

confidence: 99%

Linearized controller design for the output probability density functions of non-Gaussian stochastic systems

Kaboré¹,

Baki²,

Yue³

et al. 2005

Int J Automat Comput

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This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved

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“…Thus, the differences between wheat samples are amplified during milling and inherited from the produced flours. and Campbell, Bunn, Webb, and Hook (2001) attempting to understand how wheat kernels break during milling and predict the flours properties (i.e. particle size distribution), studied the effect of first breakage of wheat kernels and showed that wheat grains break individually depending on the shape, size and physical properties (i.e.…”

Section: Particle-size Determinationmentioning

confidence: 99%