Predicting Cellulose Concentration in Lyocell Slurry Using Hybrid Ensemble of Machine Learning Models

Abstract

Accurate prediction of cellulose concentration in lyocell slurry is crucial for process control and product quality in sustainable lyocell fiber production, yet the complex, nonlinear nature of the swelling process makes it challenging to model using conventional parametric methods. This study develops a hybrid ensemble machine learning approach to predict the cellulose concentration of lyocell slurry based on industrial production data. A data set comprising 350 samples was collected from an industrial pulping process, covering 18 input variables related to raw material properties and process conditions. Six conventional machine learning models─Gaussian process regression (GPR), support vector regression (SVR), kernel regression (KR), multivariate adaptive regression spline (MARS), random forest (RF), and artificial neural network (ANN)─were first established and optimized using Bayesian optimization with 5-fold cross-validation. Subsequently, a hybrid ensemble model (HEM) was constructed by aggregating the predictions of the six base models using a random forest meta-learner selected through score analysis. The predictive performance of all models was evaluated using multiple metrics, including coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE). The results show that the HEM achieves the best overall testing performance (R2 = 0.761, RMSE = 0.067, MAE = 0.053), followed closely by the random forest model (R2 = 0.754, RMSE = 0.068, MAE = 0.053). The ANN exhibits the smallest training−testing performance gap, confirming the effectiveness of L2 regularization. Kernel-based methods and MARS yield inferior accuracy (testing R2 < 0.70), indicating the limitations of global smoothness or additive assumptions for this task. SHAP analysis identifies the NMMO-to-cellulose ratio, hydroxylamine concentration, and temperature parameters as the most influential features. The proposed HEM provides a reliable tool for online prediction of slurry cellulose concentration, enabling real-time process control and contributing to more efficient and sustainable lyocell fiber production.