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Updated: Feb 15, 2026

Evaluation of Integrated Anaerobic Digestion and Hydrothermal Carbonization for Bioenergy Production
Published on: June 15, 2014
Biochar for next-generation anaerobic digestion: Global trends, multi-mechanism enhancement, and AI-driven prospects
Minyu Suo1, Lingxiu Liu2, Yujia Li2
1Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Zhejiang Collaborative Innovation Center for Full-Process Monitoring and Green Governance of Emerging Contaminants, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou, 310015, China; College of Geography and Environmental Science, Zhejiang Normal University, Jinhua, 321004, China.
Abstract:
Biochar-enhanced anaerobic digestion is widely recognized as a promising strategy for converting organic waste into renewable energy. This article presents a comprehensive review that integrates a bibliometric analysis of 636 research articles on biochar application in anaerobic digestion published between 2008 and 2024-analyzed via CiteSpace and VOSviewer-with a critical assessment of 298 existing review papers. The quantitative results highlight a research landscape where China contributes a leading 63.84% of the global output, supported by a consolidated core author network that drives over 50% of research productivity. Concurrently, the qualitative analysis traces the field's development and indicates that while earlier reviews primarily focused on feasibility and later studies shifted towards stability and electron transfer mechanisms, there remains a notable gap regarding predictive process optimization. Beyond identifying these evolutionary trends, the study synthesizes current academic debates regarding physicochemical trade-offs, such as the balance between microbial colonization and pore size exclusion, as well as the conflict between inhibitor adsorption and nutrient deprivation. Furthermore, the critical evaluation suggests that the enhanced electron transfer attributed to biochar likely relies on a synergy between bulk conductivity and surface redox properties rather than conductivity alone. To address the identified challenges related to nonlinear dosage responses and scaling constraints, the article proposes that future research could benefit from transitioning from empirical observation to data-driven strategies, where integrating artificial intelligence models may offer a pathway to improve the predictability of engineering applications.
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