Department of Civil Engineering
Permanent URI for this collection
Browse
Recent Submissions
Item Open Access Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity(International Society for Environmental Information Sciences, 2021-09) AlSayed, Ahmed; Soliman, Moomen; Shakir, Rahma; Snieder, Everett; ElDyasti, Ahmed; Khan, Usman TThe 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.Item Open Access Electro‐Thermal Subsurface Gas Generation and Transport: Model Validation and Implications(American Geophysical Union, 2019-06-07) Molnar, Ian; Mumford, Kevin; Krol, MagdalenaGas generation and flow in soil is relevant to applications such as the fate of leaking geologically sequestered carbon dioxide, natural releases of methane from peat and marine sediments, and numerous electro‐thermal remediation technologies for contaminated sites, such as electrical resistance heating. While traditional multiphase flow models generally perform poorly in describing unstable gas flow phenomena in soil, Macroscopic Invasion Percolation (MIP) models can reproduce key features of its behavior. When coupled with continuum heat and mass transport models, MIP has the potential to simulate complex subsurface scenarios. However, coupled MIP‐continuum models have not yet been validated against experimental data and lack key mechanisms required for electro‐thermal scenarios. Therefore, the purpose of this study was to (a) incorporate mechanisms required for steam generation and flow into an existing MIP‐continuum model (ET‐MIP), (b) validate ET‐MIP against an experimental lab‐scale electrical resistance heating study, and (c) investigate the sensitivity of water boiling and gas (steam) transport to key parameters. Water boiling plateaus (i.e., latent heat), heat recirculation within steam clusters, and steam collapse (i.e., condensation) mechanisms were added to ET‐MIP. ET‐MIP closely matched observed transient gas saturation distributions, measurements of electrical current, and temperature distributions. Heat recirculation and cluster collapse were identified as the key mechanisms required to describe gas flow dynamics using a MIP algorithm. Sensitivity analysis revealed that gas generation rates and transport distances, particularly through regions of cold water, are sensitive to the presence of dissolved gases.