Combining radio frequency heating and alkaline treatment for enhancement of sludge disintegration and volatile fatty acids production from anaerobic fermentation.

Sludge pretreatment plays a crucial role in solubilizing particulate matters to release organic matter for subsequent anaerobic fermentation (AF). This study innovatively combines radio frequency (RF) heating and alkaline treatment, and finds that the combined pretreatment achieved a sludge disintegration rate of 35.11 %, which is 15.19 % and 8.48 % higher than single RF or alkaline pretreatment. The dissociated ions from the alkali are conducive to RF action on sludge. Furthermore, the combined pretreatment significantly benefits the subsequent AF experiments, resulting in a 9-fold increase in volatile fatty acids production. Considering cost-effectiveness, the optimal operating condition is a 10-minute RF treatment at pH 10 with a total cost of 4.35 × 10-3 dollars per kg soluble chemical oxygen demand (SCOD) increased. These findings provide a foundational basis for the development of a novel technology for sludge pretreatment.

Preparation of biochar from anaerobic digested sludge for enhancement of sludge dewatering.

Effective dewatering is vital for both sludge treatment and resource recovery. This study focuses on converting post-anaerobic digested sludge into biochar to enhance sludge dewatering. The sludge-derived biochar is further modified with polyacrylamide (PAM-ADBC) and applied with sulfuric acid-modified montmorillonite (HMTS) for better performance. Significant advancements in dewatering were noted, even at reduced HMTS (0.1 g/g DS) and PAM-ADBC (25 g/kg DS) dosages. These improvements resulted in a remarkable 41.96% enhancement in capillary suction time (17.2 s) and a notable 20.26% reduction in moisture content (66.33%), respectively, all while maintaining a stable pH level. HMTS, with leached cations, improved dewatering by decomposing the extracellular polymeric substance structure through electro-neutralization to release the internal bound water within sludge flocs. Simultaneously, PAM-ADBC coagulated decomposed sludge particles into larger flocs to form a skeletal structure with itself to discharge internal water in compression dewatering. This study introduces a resource recovery method for anaerobically digested sludge and highlights its potential for sustainable utilization.

Separation of nutrients from SCFAs with a dynamic membrane in a sludge anaerobic fermenter.

The complexity and high cost to separate and recover short chain fatty acids (SCFAs), ammonium ions, and phosphates in the sludge fermentation liquid hinder the application of sludge anaerobic fermentation. In this study, an interesting phenomenon was found in a sludge anaerobic fermenter with a dynamic membrane (DM) which could not only enhance SCFAs production but also retain most SCFAs in fermenter. The separation factor of DM for NH3-N/SCFAs and PO43-/SCFAs throughout the DM development were about 40 and 80, respectively. Analysis reveals that rejection of SCFAs by DM could not be simply correlated to molecular weight or membrane pore size. The rejection mechanisms might be dominated by Donnan rejection. In addition, biodegradation in the DM may also have contribution. Findings of this study suggest the potential of DM as an economical technology for nutrients and SCFAs recover.

Applying machine learning to anaerobic fermentation of waste sludge using two targeted modeling strategies.

Anaerobic fermentation is an effective method to harvest volatile fatty acids (VFAs) from waste activated sludge (WAS). Accurately predicting and optimizing VFAs production is crucial for anaerobic fermentation engineering. In this study, we developed machine learning models using two innovative strategies to precisely predict the daily yield of VFAs in a laboratory anaerobic fermenter. Strategy-1 focuses on model interpretability to comprehend the influence of variables of interest on VFAs production, while Strategy-2 takes into account the cost of variable acquisition, making it more suitable for practical applications in prediction and optimization. The results showed that Support Vector Regression emerged as the most effective model in this study, with testing R2 values of 0.949 and 0.939 for the two strategies, respectively. We conducted feature importance analysis to identify the critical factors that influence VFAs production. Detailed explanations were provided using partial dependence plots and Shepley Additive Explanations analyses. To optimize VFAs production, we integrated the developed model with optimization algorithms, resulting in a maximum yield of 2997.282 mg/L. This value was 45.2 % higher than the average VFAs level in the operated fermenter. Our study offers valuable insights for predicting and optimizing VFAs production in sludge anaerobic fermentation, and it facilitates engineering practice in VFAs harvesting from WAS.