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

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Nur Ain Fitriah Zamrisham, Syazwani Idrus, Siti Fatimah Ismail, Mohd Shahrizal Ab Razak, Afrouzossadat Hosseini Abari, Khairina Jaman, Sri Suhartini, Mohd Razif Harun

2026 Bioenergy Research Vol. 19 Issue 1 Article Cited by 1 Quartile

Abstract

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. © The Author(s) 2025.

Affiliations

Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Malaysia; Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia; Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 8174673441, Iran; Department of Agro-Industrial Technology, Faculty of Agricultural Technology, Universitas Brawijaya, East Java, Malang, Indonesia; Centre or Excellence in Bioenergy and Biorefinery, Faculty of Agricultural Technology, Universitas Brawijaya, East Java, Malang, Indonesia; Technical Implementation Unit UB Green Campus, Universitas Brawijaya, East Java, Malang, Indonesia