Integrated deep learning neural network and desirability analysis in biogas plants: A powerful tool to optimize biogas purification

Performing anaerobic digestion is affected by different slurry properties, and its optimization poses many practical constraints. In high-dimensional input parameters with small sample size data, regression models and simple artificial neural network models may not be good enough at estimating responses. Therefore, a deep learning neural network (DNN) model was developed to estimate the responses (biogas compounds) according to the slurry properties. This model was able to predict the biogas compounds with high accuracy in comparison with regression models and back propagation neural network models. The DNN model was integrated with desirability analysis to determine optimum amounts of the slurry properties, and thus, increase biogas purification. Accordingly, the optimum ranges of C/N (15.04-18.95), BOD/COD (0.763-0.818), TS (8.1-10.6%) and T.VS (38.19-49.46%) were more precise than the ranges reported in the literature. The results indicated that large amounts of BOD/COD had a deterrent effect on desirability values, and therefore had an inhibitory effect on biogas purification. Further, pH amounts slightly above neutral could improve biogas purification. Suitable amounts of the slurry properties for the second repetition of experiments were all in the determined optimum ranges, indicating that the optimum ranges were practical to be used in biogas plants. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A deep learning neural network model was developed to estimate biogas compounds based on slurry properties, and integrated with desirability analysis to determine optimum ranges of slurry properties for increased biogas purification effectiveness. The results showed that the model performed better than traditional regression and neural network models, and the determined optimum ranges were practical for use in biogas plants.