Synergistic effects of trace metals on hydrogen and methane production from palm oil mill effluent using two-stage anaerobic digestion

• Optimal metal addition enhanced 86.4% of H 2 yield and 84% of CH 4 yield. • 10 mg·L −1 Mo 2+ , 6 mg·L −1 Ni 2+ , 6 mg·L −1 Co 2+ , 10 mg·L −1 Fe 2+ was optimized. • Ni 2+ and Fe 2+ had significant individual effects on H 2 and CH 4 production. • Mo 2+ and (Co 2+ and Fe 2+ ) had significant interactive effects. • Enhanced process stability and metal removal efficiency >90 %. The two-stage anaerobic digestion of palm oil mill effluent (POME) was optimized through trace metal supplementation (Mo 2+ , Ni 2+ , Co 2+ , and Fe 2+ ). Optimal concentrations were determined as 10 mg·L −1 Mo 2+ , 6 mg·L −1 Ni 2+ , 6 mg·L −1 Co 2+ , and 10 mg·L −1 Fe 2+ , resulting in significant biogas yield improvements on hydrogen yield increased by 86.4 % (29.5 ± 0.9 to 55.0 ± 2.1 mL-H 2 ·g −1 -VS) and methane yield by 84 % (from 173.8 ± 7.8 to 320.0 ± 8.4 mL-CH 4 ·g −1 -VS). Gas composition improved, with H 2 content increasing from 18.5 % to 32.0 % and CH 4 content from 58.2 % to 72.5 %. Maximum process efficiency was achieved at 4-day HRT for hydrogen production and 20-day HRT for methanogenesis with metal removal 0f 93.5–94.8 %. Statistical analysis revealed strong correlations between metal concentrations and enzyme activities (R 2 = 0.94, p < 0.001) and enzyme activities with biogas yields (R 2 = 0.92, p < 0.001). Metabolite profiles showed an 81 % increase in acetic acid (3,800 ± 120 mg·L −1 ) and a 93 % increase in butyric acid (2,900 ± 95 mg·L −1 ), while propionic acid decreased by 57 % in H 2 stage. Thermoanaerobacterium thermosaccharolyticum was dominant in the H 2 stage, while Methanobacter sp. and Methanosarcina sp. dominated in the CH 4 stage, with their abundance influenced by specific trace metal supplementation. Process stability was maintained through precise control systems (temperature stability index of 0.95 ± 0.05 and pH stability index of 0.92 ± 0.05) with rapid response times (<5 min). COD removal efficiency increased from 65.3 % to 85.2 %, while metal removal efficiencies exceeded 90 % for all supplemented metals. These findings demonstrate significant enhancement in biogas production through optimized trace metal supplementation and precise process control strategies.

Authors

Institutions

Publication Details

Journal
Carbon Resources Conversion
Published
2025-02-13
DOI
https://doi.org/10.1016/j.crcon.2025.100309
Citations
2
Primary Topic
Anaerobic Digestion and Biogas Production
Type
article
Field-Weighted Citation Impact
2.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Synergistic effects of trace metals on hydrogen and methane production from palm oil mill effluent using two-stage anaerobic digestion

Chonticha Mamimin, Wisarut Tukanghan, Supattra In-chan, Jiravut Seengenyoung et al.
2 citations
Carbon Resources Conversion
Anaerobic Digestion and Biogas Production
2.01
article

Synergistic effects of trace metals on hydrogen and methane production from palm oil mill effluent using two-stage anaerobic digestion

Chonticha Mamimin, Wisarut Tukanghan, Supattra In-chan, Jiravut Seengenyoung, Sompong O‐Thong, Sukonlarat Chanthong
article en
2 citations

Abstract

• Optimal metal addition enhanced 86.4% of H 2 yield and 84% of CH 4 yield. • 10 mg·L −1 Mo 2+ , 6 mg·L −1 Ni 2+ , 6 mg·L −1 Co 2+ , 10 mg·L −1 Fe 2+ was optimized. • Ni 2+ and Fe 2+ had significant individual effects on H 2 and CH 4 production. • Mo 2+ and (Co 2+ and Fe 2+ ) had significant interactive effects. • Enhanced process stability and metal removal efficiency >90 %. The two-stage anaerobic digestion of palm oil mill effluent (POME) was optimized through trace metal supplementation (Mo 2+ , Ni 2+ , Co 2+ , and Fe 2+ ). Optimal concentrations were determined as 10 mg·L −1 Mo 2+ , 6 mg·L −1 Ni 2+ , 6 mg·L −1 Co 2+ , and 10 mg·L −1 Fe 2+ , resulting in significant biogas yield improvements on hydrogen yield increased by 86.4 % (29.5 ± 0.9 to 55.0 ± 2.1 mL-H 2 ·g −1 -VS) and methane yield by 84 % (from 173.8 ± 7.8 to 320.0 ± 8.4 mL-CH 4 ·g −1 -VS). Gas composition improved, with H 2 content increasing from 18.5 % to 32.0 % and CH 4 content from 58.2 % to 72.5 %. Maximum process efficiency was achieved at 4-day HRT for hydrogen production and 20-day HRT for methanogenesis with metal removal 0f 93.5–94.8 %. Statistical analysis revealed strong correlations between metal concentrations and enzyme activities (R 2 = 0.94, p < 0.001) and enzyme activities with biogas yields (R 2 = 0.92, p < 0.001). Metabolite profiles showed an 81 % increase in acetic acid (3,800 ± 120 mg·L −1 ) and a 93 % increase in butyric acid (2,900 ± 95 mg·L −1 ), while propionic acid decreased by 57 % in H 2 stage. Thermoanaerobacterium thermosaccharolyticum was dominant in the H 2 stage, while Methanobacter sp. and Methanosarcina sp. dominated in the CH 4 stage, with their abundance influenced by specific trace metal supplementation. Process stability was maintained through precise control systems (temperature stability index of 0.95 ± 0.05 and pH stability index of 0.92 ± 0.05) with rapid response times (<5 min). COD removal efficiency increased from 65.3 % to 85.2 %, while metal removal efficiencies exceeded 90 % for all supplemented metals. These findings demonstrate significant enhancement in biogas production through optimized trace metal supplementation and precise process control strategies.

Carbon Resources ConversionVol. 8(3)
Mahidol University (TH), Thaksin University (TH)
National Research Council of Thailand
Zero hunger
Openalex Percentile: Top 16%
Anaerobic Digestion and Biogas Production
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.