Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity
| dc.contributor.author | AlSayed, Ahmed | |
| dc.contributor.author | Soliman, Moomen | |
| dc.contributor.author | Shakir, Rahma | |
| dc.contributor.author | Snieder, Everett | |
| dc.contributor.author | ElDyasti, Ahmed | |
| dc.contributor.author | Khan, Usman T | |
| dc.date.accessioned | 2022-01-24T16:00:22Z | |
| dc.date.available | 2022-01-24T16:00:22Z | |
| dc.date.issued | 2021-09 | |
| dc.description.abstract | The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bioprocesses. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through developing and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of different input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bioprocesses are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study successfully utilized DDM to model methanotrophic- based bioprocesses. The findings of this research suggest that DDM are a powerful, alternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs. | en_US |
| dc.identifier.citation | A. AlSayed, M. Soliman, R. Shakir, E. Snieder, A. Eldyasti, and U. T. Khan. Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity. Journal of Environmental Informatics 38.1 (2021): 27-40. doi: 10.3808/jei.202000446 | en_US |
| dc.identifier.issn | 1726-2135 | |
| dc.identifier.uri | 10.3808/jei.202000446 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10315/38941 | |
| dc.language.iso | en | en_US |
| dc.publisher | International Society for Environmental Information Sciences | en_US |
| dc.rights.article | http://www.jeionline.org/index.php?journal=mys&page=article&op=view&path%5B%5D=202000446 | en_US |
| dc.rights.journal | http://www.jeionline.org/ | en_US |
| dc.subject | Artificial neural networks, Data driven models, Kinetics, Kinetics-based models, Modelling, Methanotrophs, Upscaling, Water resources recovery facilities | en_US |
| dc.title | Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity | en_US |
| dc.type | Article | en_US |