Philipp Pöttgen

Experimental Validation of an Enhanced System Synthesis Approach

Planning the layout and operation of a technical system is a common task for an engineer. Typically, the workflow is divided into consecutive stages: First, the engineer designs the layout of the system, with the help of his experience or of heuristic methods. Secondly, he finds a control strategy which is often optimized by simulation. This usually results in a good operating of an unquestioned system topology. In contrast, we apply Operations Research (OR) methods to find a cost-optimal solution for both stages simultaneously via mixed integer programming (MILP). Technical Operations Research (TOR) allows one to find a provable global optimal solution within the model formulation. However, the modeling error due to the abstraction of physical reality remains unknown. We address this ubiquitous problem of OR methods by comparing our computational results with measurements in a test rig. For a practical test case we compute a topology and control strategy via MILP and verify that the objectives are met up to a deviation of 8.7 %.

Multicriterial Optimization of Technical Systems Considering Multiple Load and Availability Scenarios

Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.

Developing a Control Strategy for Booster Stations under Uncertain Load

Booster stations can fulfill a varying pressure demand with high energy-efficiency, because individual pumps can be deactivated at smaller loads. Although this is a seemingly simple approach, it is not easy to decide precisely when to activate or deactivate pumps. Contemporary activation controls derive the switching points from the current volume flow through the system. However, it is not measured directly for various reasons. Instead, the controller estimates the flow based on other system properties. This causes further uncertainty for the switching decision. In this paper, we present a method to find a robust, yet energy-efficient activation strategy.

Designing a Feedback Control System via Mixed-Integer Programming

Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.

Evaluation of Different Approaches for the Optimization of Layout and Control of Booster Stations

Pumps and pumping systems consume about 12 % of the annual electricity production in Europe. A parallel arrangement of two or more pumps is called “booster station”. Booster stations meet a varying pressure demand with high energy-efficiency by deactivating individual pumps at smaller loads. Hence, one major difference from single pump units to booster stations is the diversity of control options. Due to the non-linear characteristics of the machines and physical laws we are facing a Mixed-Integer Nonlinear Problem (MINLP). The global optimization of a MINLP requires a specialized solver and a huge amount of time. To reduce the complexity of the problem, we evaluate two possible ways: i) We apply piecewise linearization techniques to the problem in order to gain a Mixed Integer Linear Problem (MILP). The advantage is the vast variety of available solvers and a significant reduction of calculation time. The disadvantage of this method is being less accurate. ii) The enumeration of the integer decisions and the fitting of the characteristic curves by algebraic functions enable us to reduce the complexity of the problem to a simple Nonlinear Problem (NLP). This technique allows us to keep the accuracy of the physical laws, but has the big drawback of the enumeration time. The consecutive use of two global optimization solvers combines the advantages of the formerly mentioned approaches, but we lose the guarantee for global optimality. Philipp Pöttgen, and Peter F. Pelz

Multicriterial Design of a Hydrostatic Transmission System Via Mixed-Integer Programming

In times of planned obsolescence the demand for sustainability keeps growing. Ideally, a technical system is highly reliable, without failures and down times due to fast wear of single components. At the same time, maintenance should preferably be limited to pre-defined time intervals. Dispersion of load between multiple components can increase a system’s reliability and thus its availability inbetween maintenance points. However, this also results in higher investment costs and additional efforts due to higher complexity. Given a specific load profile and resulting wear of components, it is often unclear which system structure is the optimal one. Technical Operations Research (TOR) finds an optimal structure balancing availability and effort. We present our approach by designing a hydrostatic transmission system.