Abstract
Abstract Methane is currently an emerging alternative feedstock for biological processes. In this paper, we study a particular methane bioconversion process based on the bacterium Methylomicrobium buryatense 5GB1, with the aim of improving accessibility of the process to state estimation and control. First, a non-linear unsegregated, unstructured model, consisting of 8 dynamic mass flow balance equations and 8 state variables, is constructed and implemented as a dynamic simulator in MATLAB and Simulink. Second, an observability analysis is performed with the aim of finding suitable sensor configurations for state estimation, and the resulting observable configurations are further evaluated by checking the performance of linear Kalman filters for each configuration. This evalution shows that including a biomass sensor in the measurements for the Kalman filter may have conflicting effects on estimation accuracy.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
