Optimization of Process Parameters for Protein Extraction from Sludge by Isoelectric Point Precipitation Based on Ensemble Learning

Municipal sewage sludge contains considerable amounts of protein, making protein recovery a viable route for sludge valorization. In this work, sludge disintegration was achieved by cyclone cutting coupled with ozone oxidation, and the mixed liquor of foam standing liquid and supernatant was used as the feedstock for protein recovery via isoelectric point precipitation. Pretreatment tests showed that under 60 mg/L ozone concentration, 10 °C and 60 min, alkaline conditions enhanced sludge lysis; the mixed liquor suspended solids (MLSS) removal rate reached 87.65% at pH 9, and the protein concentration in the foam layer reached 1530.14 mg/L at pH 11, yielding a protein-rich feedstock suitable for subsequent extraction. In the isoelectric point precipitation stage, single-factor and L9(34) orthogonal experiments were conducted to examine the effects of pH, temperature and centrifugal speed on extraction rate, and four ensemble learning algorithms (GBR, RF, XGBoost and CatBoost) were employed to build prediction models. The results showed that the factor influence order was pH > centrifugal speed > temperature, with pH being extremely significant (p < 0.01). Under leave-one-out cross-validation, the XGBoost model performed best (R2 = 0.9243, MAE = 2.78%, RMSE = 3.52%). Response surface analysis determined the optimal parameters as pH 4.0, 5 °C and 3500 r/min, with both predicted and measured precipitation-stage extraction rates of 86.19%. Amino acid analysis indicated that essential amino acids accounted for 39.9% of the extracted protein, with good rehydration and foaming stability. Ensemble learning algorithms can reveal the multi-factor nonlinear coupling in isoelectric point precipitation, providing data support for process optimization of sludge protein recovery.