Determining the optimum cyclic operation of adsorption chillers by a direct method for periodic optimal control

Gräber, Manuel; Kirches, Christian; Bock, Hans Georg; Schlöder, Johannes P.; Tegethoff, Wilhelm; Köhler, Jürgen

doi:10.1016/j.ijrefrig.2010.12.021

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…Many thermodynamic models can be found in the literature aiming at developing systems that would provide prediction of performance at different work conditions, inter alia in Chua et al (2004), Gräber et al (2011), or Thu et al (2013). An obvious advantage of adsorption chillers in respect to devices based on absorption is a possibility of supplying energy with heat carrier at lower temperature, thereby creating greater opportunity to make use of low-temperature heat sources (also waste heat).…”

Section: Introductionmentioning

confidence: 99%

Operation of an Adsorption Chiller in Different Cycle Time Conditions

Szyc

Nowak

2014

Chemical and Process Engineering

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This paper is devoted to application of adsorption process for cooling power generation in a cooling devices. Construction and working principle of a water-silica gel adsorption chiller has been presented and the basic refrigeration cycle has been discussed. The article outlines behavior of a single-stage adsorption system influenced by changes in cycle time. The effect of cycle time and inlet chilled water temperatures on the main system performance parameters has been analysed.

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Section: Introductionmentioning

confidence: 99%

Operation of an Adsorption Chiller in Different Cycle Time Conditions

Szyc

Nowak

2014

Chemical and Process Engineering

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“…Gräber et al (2011) developed a two-bed adsorption chiller based on the lumped parameter approach. The dynamic system model was later used for periodic optimal control study to determine the optimal cycle times.…”

Section: Previous Work On Modelica-based Dynamic Hvac Modelsmentioning

confidence: 99%

Recent advances in dynamic modeling of HVAC equipment. Part 2: Modelica-based modeling

Li¹,

Li²,

Seem³

et al. 2014

HVAC&R Research

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2014) Recent advances in dynamic modeling of HVAC equipment. Part 2: Modelica-based modeling, HVAC&R Research, 20:1, 150-161,This article is the second in the two-part series examining recent advances in the dynamic modeling of HVAC systems, which can be challenging due to the multi-physical and multi-scale processes of such systems. Part I reviewed the progress in dynamic modeling of major types of HVAC equipment. Part II presents a detailed review of dynamic modeling of HVAC equipment using Modelica, an object oriented modeling platform that has demonstrated potential in addressing HVAC equipment challenges. The Modelica-based modeling platform offers some desirable features, such as object-oriented and acausal modeling, that could significantly facilitate modeling efforts and reduce the time of model development. Currently, there are many numerically robust and efficient integrated simulation environments that support the Modelica-based modeling platform, such as Dymola, SimulationX, and MapleSim. This article reviews historical and recent modeling and simulation tools, and later examines previous work on the Modelica-based dynamic modeling of HVAC equipment. It also presents current issues and possible future directions of dynamic modeling for HVAC equipment.

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“…We take interest in computing optimal control trajectories for nonlinear dynamical systems with parametric uncertainties arising from intrinsic variability or incomplete knowledge of system properties. Such uncertain optimal control problems appear in a variety of settings, including automotive control and control of chemical or biological processes . Relevant to this work, they can be modeled probabilistically based on the assumption that structural information about a priori uncertainty is at hand and is given in form of probability distributions or moment information.…”

Section: Introductionmentioning

confidence: 99%

“…Such uncertain optimal control problems appear in a variety of settings, including automotive control 1 and control of chemical or biological processes. 2,3 Relevant to this work, they can be modeled probabilistically based on the assumption that structural information about a priori uncertainty is at hand and is given in form of probability distributions or moment information. This naturally leads to a stochastic problem formulation with random input uncertainty that is propagated through the dynamic process model and influences the distribution of the solution in a nontrivial way.…”

Section: Introductionmentioning

confidence: 99%

The polynomial chaos approach for reachable set propagation with application to chance‐constrained nonlinear optimal control under parametric uncertainties

Bergner

Kirches

2017

Optim Control Appl Methods

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Summary Optimal control problems with parametric uncertainties frequently arise in control practice and can be addressed by means of a probabilistic robustification using the concept of chance constraints. In this article, we develop an efficient method based on the polynomial chaos expansion to compute nonlinear propagations of the reachable sets of all uncertain states and show how it can be used to approximate nonlinear and joint chance constraints. The strength of the obtained estimator in guaranteeing a satisfaction level is supported by providing an a priori error estimate with exponential convergence in case of sufficiently smooth solutions. The proposed approach is readily implemented in existing state‐of‐the‐art direct methods to optimal control and is evaluated for 2 real‐world nonlinear uncertain optimal control problems. The achieved level of robustness in terms of constraint satisfaction is verified by extensive Monte Carlo sampling.

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Determining the optimum cyclic operation of adsorption chillers by a direct method for periodic optimal control

Cited by 18 publications

References 20 publications

Operation of an Adsorption Chiller in Different Cycle Time Conditions

Operation of an Adsorption Chiller in Different Cycle Time Conditions

Recent advances in dynamic modeling of HVAC equipment. Part 2: Modelica-based modeling

The polynomial chaos approach for reachable set propagation with application to chance‐constrained nonlinear optimal control under parametric uncertainties

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