Extraction and Conversion of Carboxymethyl Cellulose from Okara Soybean Residue via Soda AQ Pulping: Integration of Predictive Models and Process Control

Abstract

This study investigates the effect of bases NaOH and KOH on okara, the soybean residue, in conventional pulping, based on 136 pulping conditions used as a dataset for random forest regression and gradient boosting predictive models. Okara CMC was formed and identified using Fourier-transform infrared spectroscopy (FTIR) to demonstrate a wide range of applications comparable to commercial CMC, with a low degree of substitution. The quality of okara pulp after basic pulping was analyzed based on the extracted cellulose yield and remaining protein content. The optimized pulping condition was a mixture of NaOH and KOH at a 30% concentration, resulting in an extracted cellulose yield of 24.5 wt% and a remaining protein content of 25.1%. The obtained okara pulp was converted into okara CMC with a controllable degree of substitution. The implemented dataset was used to train two predictive models: random forest regression and gradient boosting, to forecast key parameters for pulping (NaOH, KOH, AQ, and H2O). Both models demonstrated excellent prediction performance, with R2 values of 0.94 and 0.89, respectively, and showed similar residuals and predicted values. The close clustering of residuals around zero, along with the sharp and narrow curves observed, indicates that both the random forest and gradient boosting models provide precise and reliable predictions. The localized deviations observed in the residuals suggest that these models effectively capture detailed patterns in the data, leading to minimized prediction errors within specific ranges.