Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important operational parameters, such as co-substrates ratio, their composition, volatile fatty acids/alkalinity ratio, organic loading rate, and solids/hydraulic retention time. AcoD process optimization, prediction and control, and early detection of system instability are often difficult to achieve through tedious manual monitoring processes. Recently, artificial intelligence (AI) has emerged as an innovative approach to computational modeling and optimization of the AcoD process. This review discusses AI applications in AcoD process optimization, control, prediction of unknown input/output parameters, and real-time monitoring. Furthermore, the review also compares standalone and hybrid AI algorithms as applied to AcoD. The review highlights future research directions for data preprocessing, model interpretation and validation, and grey-box modeling in AcoD process.

Automated summary

Anaerobic co-digestion (AcoD) offers various advantages in terms of better digestibility, process stability, and increased methane yield. However, the efficient operation of an AcoD system requires a comprehensive understanding of important operational parameters and monitoring processes can be tedious. Artificial intelligence (AI) has emerged as an innovative approach to optimize and control the AcoD process. This review discusses the applications of AI in AcoD process optimization, control, prediction, and real-time monitoring, as well as the comparison of different AI algorithms.