Modelling anaerobic biofilm reactors--a review

Area of Science:

• Environmental Engineering
• Biotechnology
• Chemical Engineering

Background:

• Anaerobic treatment is a viable option for liquid effluent management, driven by advancements in reactor technologies like Upflow Anaerobic Sludge Blanket (UASB) and Expanded Granular Sludge Bed (EGSB) reactors.
• Mathematical modeling is crucial for optimizing the design, process control, and operational efficiency of these anaerobic reactors.
• Existing literature lacks a comprehensive review of mathematical models applicable to various anaerobic reactor configurations.

Purpose of the Study:

• To critically review and consolidate the diverse mathematical models developed for anaerobic treatment reactors.
• To assess the applicability and limitations of current models in optimizing reactor design and operation.
• To introduce the unified anaerobic digestion model (ADM1) and its relevance to anaerobic biofilm reactors.

Main Methods:

• Literature review of scientific publications on mathematical modeling of anaerobic reactors.
• Comparative analysis of different mathematical models based on their complexity, accuracy, and applicability.
• Discussion of the unified anaerobic digestion model (ADM1) and its specific application to anaerobic biofilm reactors.

Main Results:

• Several mathematical models exist for Upflow Anaerobic Sludge Blanket (UASB) reactors, Expanded Granular Sludge Bed (EGSB) reactors, anaerobic biofilters, and anaerobic fluidized bed reactors (AFBRs).
• Models vary in their complexity, incorporating different biochemical and physical processes.
• The anaerobic digestion model (ADM1) provides a unified framework applicable to various anaerobic systems, including biofilm reactors.

Conclusions:

• Mathematical models are essential tools for advancing the design and efficiency of anaerobic wastewater treatment.
• A critical understanding of available models, including ADM1, is necessary for effective implementation.
• Further research is needed to refine models and ensure their robust application across diverse operational conditions.