Application of Artificial Intelligence for Prediction, Monitoring, Optimization and Control of Anaerobic Digestion Processes—A Review

Artificial intelligence (AI) has emerged as an innovative approach to the computer modeling and optimization of anaerobic digestion (AD) and anaerobic co-digestion (AcoD) processes. AI-based algorithms are ideally suited to capture the complex nonlinear behavior of these processes. Compared to conventional methods and models, AI-based algorithms have made modeling these processes much easier. Various AI algorithms, including multivariate statistical analyses, tree-based machine learning, nature-inspired optimization, support vector machine, and artificial neural networks (ANN) have been widely used to model the AD and AcoD processes. Researchers have successfully used stand-alone and hybrid ANMs to predict biogas yield and composition, as well as for efficient process monitoring and control. Furthermore, the development of advanced optimization algorithms, including genetic algorithms and particle swarm optimization, helps to optimize the ratio of mixing of co-substrates in AcoD and important process parameters (i.e., temperature (T), pH, retention time, total solids and volatile solids). This review discusses AI applications for AD and AcoD process modeling, optimization, prediction of unknown parameters and variables, and real-time monitoring and control. A critical comparison is made with some of the popular mathematical models and algorithms for monitoring and optimization designed on their basis. The review presents also future research directions in this area and some of our own results.