@incollection{GaidaWolfBaecketal.2014, author = {Daniel Gaida and Christian Wolf and Thomas B{\"a}ck and Michael Bongards}, title = {Multi-objective nonlinear model predictive substrate feed control of a biogas plant}, series = {Kompendium der Forschungsgemeinschaft :metabolon 2012-2014}, address = {Gummersbach}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-6622}, year = {2014}, abstract = {In this paper a closed-loop substrate feed control for agricultural biogas plants is proposed. In this case, multi-objective nonlinear model predictive control is used to control composition and amount of substrate feed to optimise the economic feasibility of a biogas plant whilst assuring process stability. The control algorithm relies on a detailed biogas plant simulation model using the Anaerobic Digestion Model No. 1. The optimal control problem is solved using the state-of-the-art multi-objective optimization method SMS-EGO. Control performance is evaluated by means of a set point tracking problem in a noisy environment. Results show, that the proposed control scheme is able to keep the produced electrical energy close to a set point with an RMSE of 0.9 \%, thus maintaining optimal biogas plant operation.}, language = {en} }