preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints202208.0393.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 21 August 2022 / Approved: 23 August 2022 / Online: 23 August 2022 (04:00:30 CEST)
Version 2 : Received: 24 August 2022 / Approved: 25 August 2022 / Online: 25 August 2022 (05:54:42 CEST)

This work presents a nonlinear model predictive control scheme that challenges overcoming the obstacles holding back over decades to develop affordable autonomous control and monitoring systems applied in the large-scale industry. Among the numerous proposals in the literature, most do not consider the significant fluctuation of kinetic parameters in the reduced mathematical model ADM2, widely used for control and monitoring purposes. The prevalent cause, on a basis, is the lack of information caused by some dynamics and parameters that cannot be measured in real-time by reliable sensors. In addition, to make matters worse, those systems inherently act with nonlinear nature and have a high sensitiveness to uncontrollable inputs and perturbations. Therefore, to prevent these drawbacks, this work proposes a new methodology that reconstructs the lack of information from the non-measurable dynamics, concentration of bacterias, and the kinetic parameters related to reaction rates. Simulations results demonstrate the effectiveness of the methodology compared with traditional industrial control schemes.

model predictive control; asymptotically observer; kinetic parameter observer; homoge-neous reaction systems; anaerobic digestion

Engineering, Control and Systems Engineering

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