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

Abstract

AbstractThe 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.