Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC)
By B. Najafi, Sina Faizollahzadeh Ardabili. Resources, Conservation and Recycling, 2018
142 citations recorded by the source corpus.
Abstract In this study, a small-scale production of biogas was undertaken using spent mushroom compost (SMC). The carbon to nitrogen (C/N) ratio, temperature of the reactor (T), and retention time (RT) were the independent variables of the study. Maximum production of biogas is related to the C/N ratio of 20 at temperature of 35 °C (cumulative values of 40.5183 ml/g of VS) and C/N ratio of 30 at temperature of 55 °C (cumulative value of 44.1001 ml/g of VS). Logistic, ANFIS, and ANN models were employed in modeling the production process of biogas. Comparing the values of the results indicate that the total values of RMSE and r in case of mesophilic temperature (35 (°C)) for ANFIS network are 0.1940 and 0.9998, for MLP network are 0.780 and 0.9981, and for logistic model are 0.5111 and 0.9992, respectively. And also at the thermophilic temperature (55 (°C)), the total values of RMSE and R were calculated as 0.3033 and 0.9997 for ANFIS network, 0.3430 and 0.9992 for MLP network, and 0.5506 and 0.9991 for the logistic model, respectively. Therefore, it can be reported that the ANFIS network accurately predicted the output values of the both thermophilic and mesophilic situations.
Subfields: Biomedical Engineering, Control and Systems Engineering, Ecology
View Paper: https://www.semanticscholar.org/paper/708814c4fcd8bd8d854acfa3e1dab25d40e22281
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Citations in the corpus
- State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, methodology): “The most popular comparative performance parameters are the root mean square error (RMSE) and correlation coefficient (r), which are used to indicate the error and precision of the models [27,28]; Equations (1) and (2) represent these parameters.”