Towards monitoring biodegradation of starch-based bioplastic in anaerobic condition: Finding a proper kinetic model

Bioplastic biodegradation showed varying behavior during the process of biodegradation. The First-order and Gompertz models are the most prevalent models for monitoring biodegradation in an anaerobic digestion (AD) process, which do not suit adequately bioplastics fermentation modeling. This research aimed at studying the kinetics of methane production during AD of starch-based bioplastic by using a large library of non-linear regressions (NLRs) and an artificial neural network (ANN). Although 26 NLR models (25 were outlined in the AD literature + 1 modified by authors) have been analyzed, 9 of them were proper predictors for the whole AD process for methane production. In the end M9, which has been proposed by authors, was selected owing to the simplicity of regression as well as good statistical criteria. Moreover, MLP-ANN could outperform the NLR model and has been selected as the superior model that can define the kinetics of bioplastic AD.

Automated summary

This research studied the kinetics of methane production during anaerobic digestion of starch-based bioplastic using non-linear regressions and artificial neural networks. The findings showed that the M9 model proposed by the authors was the best predictor for methane production in the entire anaerobic digestion process.