Monitoring biological processes using univariate statistical process control

Abstract

AbstractBiological modelling is a challenging task specifically when state variables are difficult or even impossible to be measured. Consequently, monitoring quality of biological process will be impacted negatively due to the lack of an accurate model capable of reflecting precisely the process dynamics. Moreover, the faults in such systems cannot be detected robustly. The current work proposes a novel approach that combines state estimation with process monitoring techniques. The developed approach, named as particle filter (PF)'based multiscale maximum double exponentially weighted moving average (MS‐M‐DEWMA) chart, includes two main phases. In the first phase, the PF technique is applied to estimate the unknown nonlinear states of the biological processes. In the second phase, the statistical univariate chart, MS‐M‐DEWMA is adopted to address fault detection in biological processes. Therefore, in this work, we propose a monitoring approach capable of detecting shifts in mean and/or variance in biological systems (pre‐defined structure obtained using material and energy balances) where the variables are estimated using state estimation techniques. The detection chart MS‐M‐DEWMA is applied to the residuals computed using the PF. The advantages of PF‐based MS‐M‐DEWMA method are threefold: (i) extract features and decorrelate measurements using dynamical multiscale representation; (ii) estimate the state of nonlinear biological processes using the PF technique; and (iii) enhance monitoring of biological processes through detecting shifts of both variance and mean using MS‐M‐DEWMA chart. The proposed approach is validated using a Cad system in E. coli (CSEC) model.