Anaerobic Co-digestion Feedstock Blending Optimization
Moretta, Federico
Goracci, Alessia
Manenti, Flavio
Bozzano, Giulia

How to Cite

Moretta F., Goracci A., Manenti F., Bozzano G., 2022, Anaerobic Co-digestion Feedstock Blending Optimization, Chemical Engineering Transactions, 96, 295-300.


Anaerobic Digestion represents an economically and environmentally friendly technology that allows the production of biogas starting from substrates made of waste (e.g., animal manure, agro-industrial and organic waste types, sludges) while also disposing and valorising them. Single substrate digestion frequently unexploits the true bacterial capacity, resulting in low methane production. On the other hand, it has been demonstrated that it might be significantly increased by combining two or more substrates, performing an anaerobic co-digestion. In the last years, many studies have been carried out to understand how different feedstocks interact with each other when put together. However, an easy-to-use and quick technology for the calculation of their optimal blending ratios doesn't exist in the literature, being able to estimate the optimal feedstock composition of co-digestion configurations. Consequently, this work aims to develop a tool that, by understanding how substrates should be combined, allows to obtain the highest possible methane yield. The high number of possible raw materials and the high variability of their composition depending on their source reflects the high complexity of the problem, leading to the creation of a wide database where data about commonly used substrates have been collected from literature. These data have been then analysed and exploited to build a data-driven optimization algorithm – elaborated using PythonTM programming language – that, through the maximization of an objective function, it can evaluate the optimal blending ratios of the substrates entering industrial batch and CSTR-based digesters. Furthermore, the model considers supply-chain issues such as substrate availability and storage options to be more trustworthy in a wide range of industrial settings. Finally, the model was validated by comparing it to experimental batch tests published in the literature as well as industrial data provided by two Italian companies, yielding satisfactory and practical findings.