Details

Autor(en) / Beteiligte

• Andrade Cruz, Ianny
• Silva Nascimento, Victor Ruan
• Alves Felisardo, Raul José
• Gualberto dos Santos, Ataíde Mateus
• Alles de Jesus, Anderson
• Rego de Vasconcelos, Bruna
• Kumar, Vineet
• Bezerra Cavalcanti, Eliane
• Lucena de Souza, Ranyere
• Romanholo Ferreira, Luiz Fernando

Titel

Evaluation of artificial neural network models for predictive monitoring of biogas production from cassava wastewater: A training algorithms approach

Ist Teil von

• Biomass & bioenergy, 2023-08, Vol.175, p.106869, Article 106869

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• Anaerobic digestion (AD) is an established technology for resource recovery and renewable energy production. However, its performance is dictated by diverse variables including operating parameters and system design. Then, the development of modelling and optimization strategies, such as the application of machine learning (ML), to predict/maintain its performance have received significant attention. In this study, ML integrated based models for predicting cumulative biogas from cassava wastewater AD were developed using artificial neural networks (ANN). Biogas generation was investigated for 21 days with and without the addition of calcium particles from milled calcined chicken waste as a low-cost substitute for buffering. Digestion time, pH, and calcium eggshell-based concentration were selected as input features for biogas prediction through the implementation of the ANN approach. Moreover, seven different training algorithms were used to train the model, and a genetic algorithm (GA) was also implemented to optimize the ANN architecture, resulting in faster learning and higher prediction performance. Given the data set, the Levenberg-Marquardt algorithm with a hyperbolic tangent as a transfer function to the hidden and output layers was the most efficient model in predicting the biogas produced with an R-value of 0.9999. [Display omitted] •Biogas from CW supplemented with calcium was produced in an experimental setup.•Different neural architecture models were employed to predict the biogas production.•Genetic algorithm optimization achieved higher prediction performance.