Enhanced anaerobic digestion of Landfill Leachate and Food Waste using Zinc Chloride and Sodium Hydroxide activated Ceramic Bio-rings: a comparative study with Machine Learning prediction

The efficiency of anaerobic digestion (AD) is often constrained by limited microbial attachment surfaces and suboptimal environmental conditions. This study investigates the effectiveness of sodium hydroxide (NaOH)-activated and zinc chloride (ZnCl₂)-activated ceramic bio-rings (CBR) in enhancing biogas production. The objectives are threefold: (1) to evaluate biogas production from landfill leachate (LFL) and food waste (LFW) using Biomethane Potential (BMP) tests with non-activated, NaOH-activated and ZnCl₂-activated CBRs; (2) to compare the performance of NaOH-activated and ZnCl₂-activated CBR in a semi continuous study under varying organic loading rates (OLRs); and (3) to assess the forecasting accuracy of artificial neural networks (ANN) and support vector machines (SVM) in predicting biogas production. NaOH-activated CBR and ZnCl₂-activated CBR underwent sequential thermal treatment at 103 °C and 700 °C to enhance their surface area and pore structure, thereby improving their effectiveness as support media in anaerobic digestion. BMP test C (NaOH-activated CBR) produced a maximum of 5531 mL biogas, a 29% increase over BMP test A (without support). In the semi-continuous study, the NaOH-activated CBR achieved 34% and 32% increases in SMP and biogas yield, respectively, compared to the ZnCl₂-activated CBR. A stable ratio of intermediate-to-partial alkalinity (IA/PA) ratio of 0.25 indicated effective buffering. NaOH activation notably improved surface area (2.56 m2/g) and pore size (2159.03 nm), leading to superior biogas output. In forecasting, SVM outperformed ANN with higher accuracy (R2 = 0.9306 vs. 0.8846). These findings demonstrate that an integrated approach through activated CBR, a novel activation method, and machine learning prediction can enhance anaerobic digestion efficiency for high-strength organic waste.

Text 122933.pdf - Published Version Download (6MB) Official URL or Download Paper: https://link.springer.com/article/10.1007/s12155-0...

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