INTEGRATED HYBRID INTELLIGENT CONTROL SCHEME FOR METHANE PRODUCTION IN AN ANAEROBIC PROCESS

摘要

An integrated hybrid intelligent control strategy for an anaerobic digestion process is proposed in order to maximize methane production. The structure of this strategy is as follows: a) a control law calculates dilution rate and bicarbonate addition in order to track reference trajectories; this control law is based on speed-gradient inverse optimal neural control, b) a nonlinear discrete-time recurrent high order neural observer is used to estimate biomass concentration, substrate degradation and inorganic carbon, c) a Takagi-Sugeno supervisor controller detects the biomass activity, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop, d) a Takagi-Sugeno supervisor calculates reference trajectories for the system states and methane production at different operating points of the process, and finally e) fuzzy gain scheduling for the dilution rate control law interpolates the respective gains in order to control methane production at different operating points. The applicability of the proposed scheme is illustrated via simulations considering a completely stirred tank reactor.