Full-Scale Digesters: Model Predictive Control with Online Kinetic Parameter Identification Strategy

This work presents a nonlinear model predictive control scheme with a novel structure of observers aiming to create a methodology that allows feasible implementations in industrial anaerobic reactors. In this way, a new step-by-step procedure scheme has been proposed and tested by solving two specific drawbacks reported in the literature responsible for the inefficiencies of those systems in real environments. Firstly, the implementation of control structures based on modeling depends on microorganisms’ concentration measurements; the technology that achieves this is not cost-effective nor viable. Secondly, the reaction rates cannot be considered static because, in the extended anaerobic digestion model (EAM2), the large fluctuation of parameters is unavoidable. To face these two drawbacks, the concentration of acidogens and methanogens, and the values of the two reaction rates considered have been estimated by a structure of two observers using data collected by sensors. After 90 days of operation, the error in convergence was lower than 5% for both observers. Four model predictive controller (MPC) configurations are used to test all the previous information trying to maximize the volume of methane and demonstrate a satisfactory operation in a wide range of scenarios. The results demonstrate an increase in efficiency, ranging from 17.4% to 24.4%, using as a reference an open loop configuration. Finally, the operational robustness of the MPC is compared with simulations performed by traditional alternatives used in industry, the proportional-integral derivative (PID) controllers, where some simple operational scenarios to manage for an MPC are longer sufficient to disrupt a normal operation in a PID controller. For this controller, the simulation shows an error close to the 100% of the reference valueUniversidad de Sevilla G9104541

Full-Scale Digesters: Model Predictive Control with Online Kinetic Parameter Identification Strategy

This work presents a nonlinear model predictive control scheme with a novel structure of observers aiming to create a methodology that allows feasible implementations in industrial anaerobic reactors. In this way, a new step-by-step procedure scheme has been proposed and tested by solving two specific drawbacks reported in the literature responsible for the inefficiencies of those systems in real environments. Firstly, the implementation of control structures based on modeling depends on microorganisms’ concentration measurements; the technology that achieves this is not cost-effective nor viable. Secondly, the reaction rates cannot be considered static because, in the extended anaerobic digestion model (EAM2), the large fluctuation of parameters is unavoidable. To face these two drawbacks, the concentration of acidogens and methanogens, and the values of the two reaction rates considered have been estimated by a structure of two observers using data collected by sensors. After 90 days of operation, the error in convergence was lower than 5% for both observers. Four model predictive controller (MPC) configurations are used to test all the previous information trying to maximize the volume of methane and demonstrate a satisfactory operation in a wide range of scenarios. The results demonstrate an increase in efficiency, ranging from 17.4% to 24.4%, using as a reference an open loop configuration. Finally, the operational robustness of the MPC is compared with simulations performed by traditional alternatives used in industry, the proportional-integral-derivative (PID) controllers, where some simple operational scenarios to manage for an MPC are longer sufficient to disrupt a normal operation in a PID controller. For this controller, the simulation shows an error close to the 100% of the reference valu