Simulation-Based Optimization of the Anaerobic Digestion/Co-Digestion Process: A Comprehensive Review

The production of sustainable gaseous fuels via anaerobic digestion (AD) is a promising pathway to decarbonize fossil-reliant heat and electricity systems. Despite their potential, the development and optimization of biogas and renewable natural gas (RNG) production systems remain constrained by the time-consuming nature of experiments and the limitations of extrapolating pilot-scale results. This review examines the use of computer-aided simulations as tools for the analysis, replication, and assessment of AD systems. First, a background discussion of the underlying biochemical processes and the major substrate categories is presented to establish key considerations for process modelling. Subsequently, articles published over the past five years are systematically surveyed to explore the recent application of solver-oriented (including single-equation, multi-step, and machine learning models) and flowsheet-oriented models (principally in Aspen Plus v9.0, v10.0, v12.1, and v14.0; and in SuperPro Designer v8.5 and v9.0). This review demonstrated substantial diversity in model structures, input requirements, and validation strategies. From an investigative perspective, the predictive performance of models is found to strongly depend on the quantity, quality, and representativeness of input and reference data. From an industrial standpoint, the observed simulation-derived techno-economic benchmarks illustrate the applicability of computer-assisted approaches for the preliminary comparison of project scenarios within delimited case-specific frameworks. A key knowledge gap is identified in the limited availability of studies comparing models under consistent process conditions, input datasets, and validation criteria. To enable proper comparison of model strengths and limitations, future research should prioritize the development of standardized cross-model benchmarking and explore the integration of machine learning and life cycle assessments.