A model predictive optimal control system for the practical automatic start-up of anaerobic digesters

The optimal automatic start-up of anaerobic digesters has remained an elusive problem over the years to be solved at the lowest possible costs, including that of process monitoring. In this work, a non-linear model predictive control (NMPC) system was developed, under two proposed configurations, for the optimal start-up of anaerobic digesters treating soluble non-recalcitrant substrates. The minimum set of low cost practical control variables (CVs) selected for process start-up include (i) the effluent quality as acetate COD, (ii) the level of aceticlastic methanogenic biomass in the reactor, and (iii) the methane production rate (only for one of the NMPC configurations). The manipulated variables (MVs) consist of the volumetric inflow rates of the organic substrate, dilution water, and of a possible concentrated alkali addition. To be able to apply the above selected CVs (technically and economically feasible to measure/ estimate), a simplified tailored AD model was specifically designed as the prediction model, integral part of the NMPC system. The NMPC system developed was evaluated for a case scenario consisting of the automatic start-up of a high rate AD reactor treating a readily biodegradable carbohydrate based substrate. The AD plant was virtually represented by the complex Anaerobic Digestion Model No. 1. Compared to other manual start-up strategies, the two configurations of the NMPC developed appeared to reach the target methane production rate faster (39 and 18 days for the NMPC versus 70-75 days for the manual strategies) together with an overall superior CV set-point tracking error performance. Interestingly, the two configurations of the NMPC developed appear to propose two very different, almost opposite, start-up feeding strategies to both eventually start-up the reactor successfully with no process destabilizations throughout. A number of practical scenarios were also considered to evaluate the NMPC configurations for robustness and any possible improvements. These tests indicate that the NMPC objective function formulation is a key factor of the success and robustness exhibited during startup. (C) 2020 Elsevier Ltd. All rights reserved.