From dynamic simulation to optimal design and control of adsorption energy systems

Worldwide heating and cooling demand will rise significantly over the next decades. Adsorption energy systems, namely adsorption chillers and heat pumps, have the potential to provide parts of this demand environmentally friendly by employing solar heat or waste heat. Designing adsorption energy systems is challenging due to the following reasons: (1) intrinsic dynamics, (2) multi-objectiveness, (3) large variety in design parameters, (4) strong influence of control, and (5) a large impact of input parameters such as temperatures. In many studies, these effects have been investigated separately by conducting sensitivity analyses. To explore also the interactions between design, control, and input parameters, a simultaneous optimisation approach is presented and exemplified in this thesis. Key to simultaneous optimisation are fast simulation models which capture the effects of all optimisation parameters. To quickly model new advanced adsorption energy systems, an object-oriented, dynamic-model library is developed in the programming language Modelica. To conduct a simultaneous optimisation of design and control, a multi-objective optimal control problem is formulated. This optimal control problem includes the system dynamics which are captured in the dynamic process model. Additionally, point and path constraints are formulated, ensuring cyclic steady state. The multiobjectiveness is taken care of by employing the -constraint method. The resulting single-objective optimal control problem is solved by the efficient multiple shooting algorithm MUSCOD II. The developed optimisation framework is used to rigorously analyse the interaction between adsorber-bed design, component sizing, thermodynamic cycle, and control. The framework is also used to investigate the effect of the objective function and of varying input temperatures on design and control. The multi-objective optimisation results are used to discuss the trade-off between efficiency and power density by employing the Pareto frontier. Finally, simulation models and optimisation framework are used to set up a model predictive control (MPC). The MPC allows to identify and adjust the optimal control parameters based on varying input conditions. Based on these results, new heat-flow based heuristics are developed, which lead to a Pareto-optimal operation of one-bedadsorption chillers. Both MPC and heuristics are applied experimentally to a one-bed chiller

Pareto-optimal performance of one-bed adsorption chillers by easy-to-implement heat-flow-based control

The control strategy strongly influences the performance of adsorption chillers: both efficiency and cooling power depend on phase times for adsorption and desorption. For a given cooling power, operating points with maximum efficiency are Pareto-optimal and desired in practice. However, finding the corresponding phase times for adsorption and desorption is difficult since these times vary with system characteristics and inlet conditions. In this paper, we mathematically derive a control strategy which is based only on heat flow measurements in the evaporator and the condenser of the adsorption chiller. The derived control strategy finds the Pareto-optimal adsorption and desorption phase times for a given cooling load. The control strategy is easy to implement since it only requires easily available temperature and volume flow measurements in the secondary fluid circuits. The derived control strategy is tested in (1) a simulation study and (2) an experimental study. In the simulation study, we show that the control strategy leads to (near) Pareto-optimal operation in cyclic steady-state and quickly responses to step-changes in the inlet conditions. In the experimental study, we demonstrate the ease of implementation and we show optimal operation for continuously varying inlet conditions

Benchmarking commercial adsorbents for drying air in a packed bed

Adsorbents are widely used as desiccant materials to dry air. The performance of an adsorbent strongly depends on the fit of its properties to the process conditions, such as humidity or temperature. Thus, it is crucial to characterize adsorbents at the process conditions of the selected drying application. For experimental characterization of adsorbents, three indicators are selected that reflect important objectives: (1) working capacity reflects the necessary amount of adsorbent, (2) pressure drop across the adsorbent bed reflects the necessary auxiliary energy for ventilation and (3) dehumidification rate reflects the process duration. In this paper, we evaluate 12 commercially available adsorbents (SG 125, SG 127, SG 127H, SG 125B, SG 127B, ProSorb, ArtSorb, SG E  (2–4 mm), SG E  (3–6 mm), SG M, Zeolite 13X, AQSOA Z02) for conditions derived from an adsorption dishwasher application. Specifically, we employ an adsorption temperature of , a desorption temperature of and a relative humidity of . The results show that there is a trade-off between dehumidification rate and pressure drop across the bed with Pareto-optimal performance of SG M, SG 127 and SG E  (3–6 mm). Furthermore, the results show that there is a trade-off between dehumidification rate and working capacity with Pareto-optimal performance of SG 127B, SG E  (3–6 mm) and AQSOA Z02. Thus, promising adsorbents for drying air are identified