Hybrid Intelligent Inverse Optimal Control for Methane Production in an Anaerobic Process

K. J. Gurubel, F. Ornelas-Tellez, E. N. Sanchez and S. Carlos-Hernandez

Abstract
Anaerobic processes are very attractive because of their waste treatment properties and their capacity for transforming waste materials in order to generate methane, which can be used as a renewable energy source. A hybrid intelligent control strategy for an anaerobic process is proposed in this work; the structure of this strategy is as follows: a) a control law calculates dilution rate and bicarbonate addition in order to track a methane production reference trajectory; 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, and c) a Takagi-Sugeno supervisor, which detects the process state, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop. The applicability of the proposed scheme is illustrated via simulations considering a completely stirred tank reactor.

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Keywords
Anaerobic process, methane production, hybrid intelligent control, neural observer, inverse optimal neural control, Takagi-Sugeno supervisor