Farid Muhammad Usman, Olbert Indiana A, Bück Andreas, Ghafoor Abdul, Wu Guangxue
Institute of Particle Technology (LFG), Department of Chemical and Biological Engineering, Friedrich-Alexander University Erlangen-Nuremberg Cauerstr, 4, D-91058, Erlangen, Germany.
Civil Engineering, School of Engineering, University of Galway, Galway, H91HX31, Ireland.
Heliyon. 2025 Jan 10;11(2):e41911. doi: 10.1016/j.heliyon.2025.e41911. eCollection 2025 Jan 30.
Anaerobic digestion (AD) has been recognized as one of the most viable options for the treatment of a wide range of waste materials. Complex structure of wastes is safely broken down to destroy pollutants and pathogens. Biogas is produced as a by-product of this process which is considered as a clean energy resource. However, provision of controlled environment for microbial activities is critical to ensure the required process efficiency. This can only be achieved with the efficient design of controlled vessels used for anaerobic digestion, termed as AD reactors. AD functions such as mixing, hydrodynamics, multiphase interaction, heat transfer, temperature distribution and bio kinetics are significantly affected by the reactor shape, design and configurations, hence making it essential to optimize the reactor design before installation at large scale. Mostly, such optimization is carried out with the help of lab scale experimentations and testing protocols which result in high costs for repeating several design experiments. Computational fluid dynamics (CFD) is an applied mathematical tool which helps to understand and predict the fluid dynamics, heat flow as well as species transport in different domains. This approach contributes to minimize the experimental costs while optimizing the reactor configurations in less time. The current review is presented to summarize and discuss the core characteristics of AD process followed by concerned CFD attributes. Research gaps and critical challenges are identified in different aspects such as reactor design, and configuration, mixing, multiphase flow, heat transfer, biokinetics as well as machine learning approaches.
厌氧消化（AD）已被公认为是处理多种废料的最可行方法之一。废料的复杂结构被安全分解，以消除污染物和病原体。沼气作为该过程的副产品产生，被视为一种清洁能源。然而，为微生物活动提供可控环境对于确保所需的工艺效率至关重要。这只能通过高效设计用于厌氧消化的受控容器（称为AD反应器）来实现。AD的功能，如混合、流体动力学、多相相互作用、传热、温度分布和生物动力学，会受到反应器形状、设计和配置的显著影响，因此在大规模安装之前优化反应器设计至关重要。大多数情况下，这种优化是借助实验室规模的实验和测试协议进行的，这导致重复多个设计实验的成本很高。计算流体动力学（CFD）是一种应用数学工具，有助于理解和预测不同领域中的流体动力学、热流以及物质传输。这种方法有助于在更短的时间内优化反应器配置的同时将实验成本降至最低。本综述旨在总结和讨论AD过程的核心特征以及相关的CFD属性。在反应器设计、配置、混合、多相流、传热、生物动力学以及机器学习方法等不同方面确定了研究差距和关键挑战。
