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This work focused on improving circulation and mixing of the massecuite and reducing the energy loss in the cane sugar continuous crystallization system. The developed Computational Fluid Dynamics (CFD) model is based on the continuous crystallization system of a sugarcane mill in Guangxi, China and verified with the actual operation data. The calculated entropy production and pressure drop of the system are used as indices for assessing the performance of the system. Next, 350 CFD simulations are conducted in the parametric experiment platform with 9 parameters and produces a collection of data. Then based on the CFD data set, the data‐driven model is employed for regression of the relations between key parameters and indices. The implemented data‐driven model is used for Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to obtain the optimal result of Pareto frontier. CFD simulation with the optimized parameters reduces entropy by 9.76% and reduces pressure drop by 11.52%. Novelty impact statement: Multi‐objective optimization of cane sugar continuous crystallization system is performed. The entropy is reduced by 9.76% and the pressure drop is reduced up to 11.52%. The proposed method show the good applicability. [ABSTRACT FROM AUTHOR]

Crystal size distribution (CSD) is important in evaluating crystal quality in cane sugar crystallization process. Due to the complex non‐linearity, time‐delay and strong coupling in cane sugar crystallization process, it is difficult to directly modeling in the mechanism of cane sugar crystallization process to obtain CSD parameters. In order to obtain two main CSD parameters so that to achieve better control and production of cane sugar, this article constructs a data‐driven model based on least squares support vector regression (LSSVR) and particle swarm optimization (PSO). Based on LSSVR, the model takes the easy to measureable variables (massecuite brix, massecuite level, massecuite temperature, steam pressure, feeding volume, and vacuum degree) as input variables, and outputs sugar CSD parameters (crystal average size, coefficient of variation of crystal size). PSO algorithm is used to optimize the key parameters of primary model to get better modeling performance. Compared with other modeling methods such as back propagation, extreme learning machine, radial basis function, and SVR, the constructed PSO‐LSSVR model has obvious advantages over other models in learning speed and predictive effect, generalization ability. This model has potential to be applied to the control system of cane sugar crystallization process and get better product of sugar. Practical applications: The constructed model uses the measureable variables to obtain main crystal size distribution (CSD) parameters (which is not easy to be measured online directly), and the measureable variables can be easy to control. According the relation between the measureable variables and CSD parameters, the model is useful to set up better operation conditions, and form better crystallization environment, and improve the quality of sugar crystal. So, this method will benefit the control system of cane sugar crystallization process and improve the quality and efficiency of cane sugar crystallization process, and the model is also useful for the study of continuous sugar crystallization process. [ABSTRACT FROM AUTHOR]

Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model. [ABSTRACT FROM AUTHOR]

Understanding the process of the sugarcane milling is important for improving sugar extraction. The Arbitrary Lagrangian–Eulerian algorithm within LS-DYNA was successfully used for the first time to simulate the dynamic process of milling. The predictions of the numerical model were validated using experimental crushing data from a previous study and dynamic process of milling was examined in detail. The draining performance during milling needs to be carefully considered. It was found that the deformation of the sugarcane in the XY cross-section under loads consisted of two main phases; compression and expansion with reabsorption mostly occurring during the expansion phase. The feeding side of the nip is the main area for the draining of juice. A sensitivity analysis of parameters influencing load and pressure was conducted. This research method could be applicable to similar engineering problems where there is contact, collision, extrusion, large displacement, and large deformation. • Arbitrary Lagrangian–Eulerian algorithm used to simulate milling process. • Corresponding changes in the physical quantities of milling investigated. • Sensitivity analysis of parameters influencing load and pressure carried out. [ABSTRACT FROM AUTHOR]

Highlights • A sugarcane node recognition algorithm based on multi-threshold and multi-scale wavelet transform is proposed. • A multisensory redundancy algorithm based on Gauss membership function is proposed. • The laser displacement sensor is used to obtain the surface contour signal of sugarcane. • The proposed algorithm shows high recognition rate, effectiveness and anti-interference. Abstract In order to realize automatic production of sugarcane seed, a sugarcane node recognition analysis based on multi-threshold and multi-scale wavelet transform is proposed. The laser displacement sensor is used to obtain the surface contour signal of sugarcane. After analyzing the signal characteristics, the 5th order of Daubechies wavelet base db5, is chosen as the wavelet mother function, and the signal is decomposed to eight layers by discrete wavelet. The fifth, sixth, and seventh layer coefficients are extracted to perform threshold processing, and the reconstructed signals processed by each threshold value are superimposed to characterize the characteristics of the sugarcane nodes. A multisensory redundancy algorithm based on Gauss membership function is proposed to improve the accuracy of recognition. Experiments show that the recognition rate of the algorithm is 100%, the maximum positioning error is less than 2.5 mm, and the maximum delay is 0.25 s. Compared with other four algorithms based on image processing, the proposed algorithm has higher effectiveness and recognition rate. [ABSTRACT FROM AUTHOR]

Sugarcane juice clarification is an important operation in the production process of sugar industry. At present, the two major production indicators (clear juice color value and gravity purity of the mixed juice) in sugarcane juice clarification process still cannot be measured online, and can only be obtained by offline test. In addition, when production condition changes, the setting and adjustment of the key operational variables are made by experienced workers. The delay caused by the offline test and the randomness of the key operational variables caused by manual adjustment will affect the quality of final product. To address these two issues, we firstly constructed a data-driven model based on the deep kernel extreme learning machine (DK-ELM) to predict the key production indicators for clarification process. The model which combined the kernel method and the multi-layer extreme learning machine (ML-ELM) proved successful in prediction of clear juice color value and gravity purity. Then, we proposed a strategy for optimization of operational variables, avoiding the randomness caused by manual setting. A multi -objective function including the purity and color value was constructed. The niche multi-objective particle swarm optimization (PSO) algorithm was used to solve the multi -objective function under typical working conditions in the production process to get optimal set of key operational variables. The results showed that the niche multi-objective particle swarm optimization (PSO) algorithm can provide optimum production indexes for sugarcane juice clarification. • A data-driven model based on deep kernel extreme learning machine is proposed. • The proposed data-driven model has good prediction performance and high efficiency. • The optimization model is constructed on the base of data-driven model. • A niche multi-objective particle swarm optimization algorithm is proposed. [ABSTRACT FROM AUTHOR]