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This paper presents a series of flow and temperature measurements on the principal heat network of :metabolon in Lindlar, Germany. These measurements intend to show the behaviour of the system on specific production areas of :metabolon for future monitoring and optimisation purposes. Such measurements allow the analysis of the system’s heat flow through the network, which showed that losses exist, some areas. The results demonstrate successfully that the temperature and flow changes deserve more detailed and fixed monitoring in specific areas to help the user decide the optimum measuring point.
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.