Sustainable cooling cycles by algorithmically supported design of decentral pump systems

Abstract

Accounting for up to 25% of electricity consumption in industry, pump systems are significant electricity consumers. Designing pump systems is a complex process because of the multitude of parameters and their combinations (pump types, interconnection variants, various load scenarios). Moreover, decentral pumps can be integrated to reduce the required hydraulic power, which further increases the complexity. This complexity can no longer be mastered with conventional planning methods. The aim of this paper is to tackle the complexity of pump systems design using algorithm-based design methods. The design problem is formulated as a mixed-integer nonlinear optimisation program with the objective of minimising the life cycle costs. The solution involves three steps: (i) the placement of decentral pump stations to reduce the hydraulic power, (ii) the preselection of suitable pump types, and (iii) the optimisation of the selection and operation of pumps. To solve the optimisation problem, an exact off-the-shelf solver and a problem-specific heuristic solution method are used. These are compared with conventional design methods. Both the algorithm-based and the conventional design methods are applied to an industrial cooling cycle operated by BASF SE. The results show that due to decentralisation, up to 38% (248 kW) of hydraulic power can be saved. With the help of the algorithm-based design methods, savings can also be achieved with regard to the efficiency and costs: up to 21% of the life-cycle costs can be saved while increasing the net efficiency from 47.2% to 57.8%. Furthermore, the algorithm-based design methods are superior, leading to 15% (approx. 770 000 €) lower life-cycle costs compared to conventional methods. Conventional methods based on the best efficiency point or mean efficiency show the lowest level of performance. The advantages are particularly evident in system variants with a high complexity. The trade-off between investment costs and energy demand is presented transparently using a Pareto front.