Modification of anaerobic digestion model No.1 with Machine learning models towards applicable and accurate simulation of biomass anaerobic digestion

• An M-ADM1 framework based on ML and ADM1 was proposed to simulate biomass anaerobic digestion. • SVM model was embed in M-ADM1 to predict crucial kinetic parameters during anaerobic digestion. • An average R 2 of 0.92 and RMSE of 0.167 were obtained for predicting the crucial kinetic parameters. • The TIC for municipal solid waste, kitchen waste, and sludge reached 0.0163, 0.0327, and 0.0361. • The results provide promising potentials towards simulation of biomass anaerobic digestion. This work proposed a so-called M-ADM1 model for anaerobic digestion simulation, which uses machine learning model to predict the kinetic parameters in anaerobic digestion model No.1 (ADM1). A total of 75 biomass samples were used to establish the machine learning model. Inputs used to predict the kinetic parameters included C, H, O, N, S contents, and digestion temperature. The sensitivities of 17 kinetic parameters were evaluated, and 7 kinetic parameters with the highest sensitivities were selected as model outputs. After model optimization, the average R 2 for predicting the 7 kinetic parameters reached 0.92, and the root mean square error reached 0.167. The accuracy of the overall M-ADM1 expressed by Theil inequality coefficient of municipal solid waste, kitchen waste, and sludge were 0.0163, 0.0327, and 0.0361, respectively. The results validated the hypothesis that accurately predicting some crucial intermediate parameters using machine learning models could enhance the performance of tradition ADM1.