Soft sensor with deep feature extraction for a sugarcane milling system

Meng, Yanmei; Chen, Jie; Li, Zhengyuan; Zhang, Yue; Liang, Lisheng; Zhu, Jihong

doi:10.1111/jfpe.14066

Cited by 2 publications

(6 citation statements)

References 41 publications

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“…The current stop time indicates the downtime within a minute. These factors, whether in practice or through data‐driven modeling, have been proven to be related to the quality of the sugarcane milling (Meng et al, 2022; Zhu et al, 2016). The shift reports included quality parameters data, such as ER, IJSC, sugar content of mixed juice, bagasse moisture, and other quality parameters of the whole sugarcane sugar production, with a sampling frequency of 3 samples/day.…”

Section: Methodsmentioning

confidence: 99%

“…Step 3: Retrain the model using 594 and 599 labeled data samples Moreover, ELM, a shallow learning method, has gained widespread utilization in industrial quality prediction due to its rapid learning capabilities and impressive generalization performance (Huang et al, 2006). The ELM-based models, namely KELM and DK-ELM, which are enhanced versions of ELM, have demonstrated favorable prediction outcomes when applied to the estimation of quality parameters in sugarcane pressing (Meng et al, 2022;Qiu et al, 2023).…”

Section: Tl-cnn-kelmmentioning

confidence: 99%

“…Oktarini Asnawi et al (2019) used the Backpropagation Neural Network (BPNN) to predict the yield of sugarcane juice by the installation position parameters of milling rollers. Meng et al (2022) used a multilevel filtering method based on mutual information and a wrapper method based on hybrid chicken swarm optimization for feature selection. The research demonstrated a significant correlation between process parameters (e.g., crusher current, material level on the belt, and squeezer current) and quality parameters such as extraction rate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

Yang,

Mao,

Yin

et al. 2023

J Food Process Engineering

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The data‐driven model for predicting sugarcane milling quality parameters is crucial for optimization. Current challenges include real‐time data acquisition and limited labeled data, reducing model accuracy and raising data collection costs. There is an urgent need to utilize unlabeled data to establish data‐driven models for sugarcane milling. This study introduces novel hybrid strategies, namely combining Convolutional Neural Network (CNN) with Kernel Extreme Learning Machine (KELM), utilizing pre‐training and fine‐tuning techniques. These strategies include CNN‐KELM and Transfer Learning CNN‐KELM (TL‐CNN‐KELM). Results demonstrate superior accuracy compared to single algorithms. TL‐CNN‐KELM, leveraging unlabeled data through transfer learning, achieves reductions of 0.007 in mean squared error and 0.005 in mean absolute error for extraction rate predictions. For initial juice sugar content predictions, reductions of 0.027 in mean squared error and 0.012 in mean absolute error are attained. This approach demonstrates enhanced accuracy in predicting quality parameters while maintaining robustness to data size.Practical ApplicationsThis work can reduce the data acquisition cost for quality parameter prediction and accelerate the intellectualization of sugarcane milling. In production practice, there are many process industries similar to sugarcane milling, so the novel algorithms also provide an idea for the quality parameter optimization and prediction of other process industries.

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Section: Methodsmentioning

confidence: 99%

Section: Tl-cnn-kelmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

Yang,

Mao,

Yin

et al. 2023

J Food Process Engineering

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“…In order to obtain the state parameters of each flow, a multilevel filtering method based on mutual information proposed by Meng et al was used [ 29 ]. On the basis of the analysis of the influencing factors of the milling system and the on-site testing data, the operating parameters of the sugarcane milling system were determined, as shown in Table 1 .…”

Section: Establishment Of a Collaborative Optimization Model Of Mf-ef...mentioning

confidence: 99%

“…At the same time, the feature extraction method described in Section 2.3 only considers the effects of individual features on the output target, and the effects of different feature combinations on the model are not considered. In order to obtain better performance, the wrapper method based on the improved chicken swarm optimization (ICSO) proposed by Meng et al is used to obtain the optimal parameter combinations [ 29 ]. The training model’s determination coefficient (

) is taken as the fitness, and a combination of the ICSO and trial-and-error methods is used to optimize the hyperparameters.…”

Section: Establishment Of a Collaborative Optimization Model Of Mf-ef...mentioning

confidence: 99%

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

Chen

Meng

et al. 2022

Foods

Self Cite

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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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Soft sensor with deep feature extraction for a sugarcane milling system

Cited by 2 publications

References 41 publications

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

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

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