Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

Abstract

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 min while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time.

Solving Some Graph Problems in Composite 3D Printing Using Spreadsheet Modeling

“…11 and 12) are linear; therefore, after transferring the model to the Solver dialog box, setting the optimization criterion to minimum and choosing the simplex search method, we almost immediately obtain the shortest path {1_9, 9_10, 10_5, 5_7, 7_8} with length L = 14 (Figure 10). 3) based on (10) and (11). 3) based on ( 10) and (11).…”

“…Computer graph algorithms are used in many applications such as routing and scheduling problems, recommendation systems, social networks, and data mining [8]. In physics, graphs are used to model the behavior of physical systems such as molecules, crystals, and networks [9,10]. Graph theory is used in biology to model and analyze biological networks such as gene regulatory networks, protein interaction networks, food webs, in the study of epidemiology, and the spread of infectious diseases [11].…”