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The increasing variety of combinations of different building technology components offers a high potential for energy and cost savings in today's buildings. However, in most cases, this potential is not yet fully exploited due to the lack of intelligent supervisory control systems that are required to manage the complexity of the resulting overall systems. In this article, we present the implementation of a mixed-integer nonlinear model predictive control approach as a smart realtime building energy management system. The presented methodology is based on a forward-looking optimization of the overall energy costs. It takes into account energy demand forecasts and varying electricity market prices. We achieve real-time capability of the controller by applying a decomposition approach, which approximates the optimal solution of the underlying mixed-integer optimal control problem by convexification and rounding of the relaxed solution. The quality of the suboptimal solution is evaluated by comparison with the globally optimal solution obtained by the dynamic programming method. Based on a real-world scenario, we demonstrate that utilization of the real-time capable mixedinteger nonlinear model predictive control approach in a building control system leads to savings of 16% in the total operating costs and 13% in primary energy compared to the state-of-the-art control strategy without any loss of comfort for the residents.
We present a feedback-corrected optimal scheduling approach to reduce the demand of electrical energy of batch processes, exemplified at the sand preparation in foundry. The main energy driver in the exemplary foundry is the idle time of the batch-wise working sand mixers. In this novel approach, we use linear integer programming to minimize the demand of energy of the sand mixers by scheduling the batches in real-time. For the optimization we use a physical model of the sand preparation, which takes dwell-times of the processes as dead-time systems into account. In this paper, we present the steps to make the optimal scheduling approach applicable for the production process. The application at the real production plant proves the performance of the suggested approach. Compared to the conventional control, the feedback-corrected optimal scheduling approach leads to an reduction in energy consumption of approximately 6.5 % without modifying the process or the aggregates.
A new type of rotary compressor, called “rotary-chamber compressor”, consists of two interlocking rotors with 4 wings each, that perform non-uniform rotary movements. Both rotors have the same direction of rotation, while one rotor is accelerating, the other rotor is retarding. After surpassing a specific mark, the sequence changes and the leading rotor begins to retard and vice versa. Due to the resulting relative phase difference, the volume between the two wings is changing periodically, which allows pulsating working chambers. The technology was first introduced by its founder Jürgen Schukey in 1987. Since then, no further development on this machine is known to us except our own. In this contribution, a study on the kinematics of the rotary-chamber-compressor is presented. Initial studies have shown that changes in the kinematics of the rotors will have a direct influence on the thermodynamical variables, which, if optimized, can lead to an increased performance of the machine. Therefore, a mathematical model has been developed to obtain the performance parameters from different kinematic concepts by using numerical CFD analysis. Furthermore, additional optimization possibilities will be listed and discussed.
This paper presents a novel approach for modelling the energy consumption of the coupled parallel moulding sand mixers of a foundry as an optimal control problem. The minimization of energy consumption is optimized by scheduling the mixing processes in a linear integer programming scheme. The sand flow through the foundry’s sand preparation is characterized by a physical model. This model considers the sand demand of the moulding machine as disturbance, the stored sand masses in the mixer hoppers and machine hoppers, respectively. The novel approach of handling dwell-times for dosing, mixing and transport processes using dead-time systems and constraint pushing allows the application of a linear model. The formulation of the optimal control problem aims at real-time application as model predictive control at the production plant. Initial application results indicate an improvement in energy consumption of approximately 8%.