Study on elastoplastic coupling mechanics model of the milled mixture of sugar cane

Duan, Qingshan; Mao, Hanling; Mao, Hanying; Huang, Zhenfeng; Li, Bing; Li, Xinxin

doi:10.1590/fst.02518

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…The milling system is a complex production process with strong coupling and nonlinearity. The system includes the milling process and also crushing, infiltration and other processes (Duan et al, 2019). Establishing a partial mechanism model to accurately describe the mathematical model of the entire process is difficult.…”

Section: Introductionmentioning

confidence: 99%

Soft sensor with deep feature extraction for a sugarcane milling system

Meng

Chen

et al. 2022

J Food Process Engineering

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Extraction rate and energy consumption are two important indicators of the sugarcane milling system, which are calculated by offline experiments and inspection methods. Soft sensors are inferential models, which are enabling online prediction of these indicators. Selection of input variables is one of the most critical issues in soft sensors design. So a deep feature extraction method is proposed by combining mutual information theory and hybrid chicken swamp algorithm to determine the input and parameters of model. Then, input the obtained variables into the data‐driven model of deep kernal extreme learning machine (DK‐ELM) with optimized parameters to realize the prediction of extraction and hourly energy consumption. It combines the kernel method with the multi‐layer ELM. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Comparing the proposed framework with the four experiments designed on a sugarcane milling system, the proposed framework for predicting extraction and energy consumption with better feasibility and effectiveness, and provided a good reference for end‐point control and judgment of quick direct tapping. Practical Applications Extraction and energy consumption are two important indicators of the milling process and whether they meet standards will affect the smooth operation and economic benefits of sugar production. This study presents a novel modeling method to predict these indicators based on the deep feature extraction and deep kernal extreme learning machine. These methods provided a good reference for end‐point control and judgment of quick direct tapping. In addition, it a new concept for the design of soft sensors for such complex industrial processes.

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Section: Introductionmentioning

confidence: 99%

Soft sensor with deep feature extraction for a sugarcane milling system

Meng

Chen

et al. 2022

J Food Process Engineering

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“…The evolutionary material properties parameters are averaged to calculate the average parameters. The material evolutionary physical parameters and parameter averages of the milled mixture of sugar cane (Young's modulus, Poisson's ratio, and plastic parameters of the modified DPC model) were the data of Duan et al (). In this paper, permeability coefficient is related to void ratio, the permeability coefficient of the milled mixture of sugar cane came from Kannapiran ().…”

Section: Methodsmentioning

confidence: 99%

“…Tests showed Poisson's ratio and Young's modulus of the milled mixture were not constant (Adam & Loughran, , ; Duan & Loughran, ; Kannapiran, ; Plaza et al, ). Using uniaxial confined loading–unloading tests, the results indicated Young's modulus increased from 3,874,309 Pa to 12,084,321 Pa as the void ratio decreased from 8.51 to 2.79, Poisson's ratio rose from 0.15 to 0.25 when the void ratio dropped from 23.4 to 2.38; and when the plastic volume strain increased from 0 to 1.47, the hydrostatic yield stress increased from 41,250 Pa to 1,176,119 Pa (Duan et al, ). The reason for the change of all parameters can be attributed to the phenomena of elastic‐plastic coupling and solid–liquid coupling during the milling process.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of important parameters in a three‐dimensional simulation of the milling process of sugar cane with modified Drucker–Prager Cap model based on evolutionary material properties

Duan

Mao

Huang

et al. 2019

J Food Process Preserv

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The properties of the milled mixture of sugar cane change greatly during the milling process. The evolution of its properties was modeled using three‐dimensional simulation method of the modified Drucker–Prager Cap model. Parameters for the model were determined by dynamic compression tests and simulated contrast test and employed to analyze the changes of roll load, roll torque, the maximum speed of sugar juice, stress, pore pressure, and the maximum void ratio under compression ratios of 1.5–3.5, blanket thicknesses of 40–140 mm, roll diameters of 700–1000 mm, and roll surface speeds of 0.1–0.5 m/s. The following results have been found: compression ratio plays the leading role in stress, pore pressure, roll load, and roll torque which increase with it; blanket thickness is of primary importance for maximum void ratio which increases with it; and roll surface speed has the most obvious effect on maximum sugar juice speed which increases with it. The method of this paper might provide a more accurate prediction for the optimization of these important parameters during the milling process of sugar cane. Practical applications The properties of the milled mixture of sugar cane change greatly during the milling process. However, the material properties for the constitutive model adopted by the researchers were the mean values of those measured. As a result, the predicted results obtained by the model based on the average values of these parameters are different from those obtained by the actual tests. We evolved the law of variation of parameters and adopted a three‐dimensional simulation method of the modified Drucker–Prager Cap model to the milling process of sugar cane. Our results show compression ratio is most important for stress, pore pressure, roll load, and roll torque which increase with it; blanket thickness and roll surface speed are of primary importance for maximum void ratio and the most obvious effect on maximum sugar juice speed which increases with them. Information presented here can serve as a guidance for a more accurate prediction for the optimization of these important parameters during the milling process of sugar cane.

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“…Adam et al simulated the pressing of sugarcane using finite factors and studied the effect of sugarcane thickness on the extraction of cane juice, without considering the dynamic effects [ 6 ]. Duan et al proposed a two-step method to analyze the relevant parameters of the pressing process and determine the factors that have a significant influence on the pressing process [ 7 ]. Qiu et al proposed an arbitrary Lagrangian–Euler algorithm to simulate the dynamic process of sugarcane milling and study the corresponding changes in the physical quantities [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…This method might provide a more accurate prediction for the optimization of these important parameters during the milling process of sugarcane [ 9 ]. However, the sugarcane milling system consists of not only the pressing process, but also the processes of crushing, tearing, and seepage [ 7 , 10 ]. Hence, a mechanistic model of the process is difficult to establish.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

Chen

Meng

et al. 2022

Foods

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The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers’ experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF–EF–IF) by introducing synergetic theory, and each flow’s order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters—including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction—are produced as outputs. A collaborative optimization model of the MF–EF–IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

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Study on elastoplastic coupling mechanics model of the milled mixture of sugar cane

Cited by 6 publications

References 14 publications

Soft sensor with deep feature extraction for a sugarcane milling system

Soft sensor with deep feature extraction for a sugarcane milling system

Sensitivity of important parameters in a three‐dimensional simulation of the milling process of sugar cane with modified Drucker–Prager Cap model based on evolutionary material properties

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

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