Planning a pump system is a difficult task. Many different load profiles (scenarios) are given and
have to be supported by the system. You can choose from a wide range of pumps and fittings to
include them in the system. Many configurations of the chosen components have to be considered
for this task. Therefore, an exponential number of possible systems can be imagined, more than a
system designer can all think of. From this set of possible systems, it is hard to find the optimal
system with minimal power consumption, acquisition costs or maintenance costs.
Partners of this project developed a mathematical optimization program to compute this optimal
pump system [1]. But such an optimization program is hard to read and to understand.
Therefore, a domain specific language was designed to generate this optimization program. The
language includes among others pump specifications, a selection of components to be used, their
connections and different load profiles. The generated program is then solved by external software
tools and evaluated to show the optimal system in a simulation model. The generated optimization
program is analyzed to achieve user friendly feedback about feasibility of a system with the specific
requirements.
This paper presents some features of the language to proof that it is possible to specify a pumping
system optimization scenario in a readable manner. Pumps are described by their characteristics,
connection types and their costs. Characteristics are automatically linearized for a detailed model
of the available operation points of the pump.
If it is not possible to configure a system based on the given constraints, an error report will be
generated indicating the problem in the original specification. For example, there would be an error
message if the required pressure cannot be achieved by only using the given pumps.
Another approach of this work is to evaluate the results of the optimization program solver. The
result of the solver is a set of variables with their values. From this information a simulation model
next to a layout of the cost optimal pump system are generated.
In summary this work makes the power of mathematical optimization methods usable for a wide
range of users. It is easy to plan an energy optimal system with the presented tool which supports
all requirements. Compiler technologies can analyze the planned system and give the user a readable feedback for his work. Also it is possible to generate faster solvable optimization programs
than normal crafted programs
