464623 Optimal Control of a LiBr/Water Absorption Chiller

Monday, November 14, 2016: 8:48 AM
Mason (Hilton San Francisco Union Square)
Alejandro A. Sabbagh, Chemical Engineering, Universidad de los Andes, Bogota, Colombia and Jorge M. Gomez, Chemical Engineering, Universidad de los Andes, Bogotá, Colombia

A LiBr/water absorption refrigeration system driven by solar energy is studied to find its optimal control profile subject to disturbances of the temperature and atmospheric conditions during the day. This work is developed in two phases, first a computational mathematical model was developed to accomplish the optimal control of the chiller, which was then used in a second phase to build a prototype.

A single-stage LiBr/water absorption chiller is made up of four main components: a generator, an absorber, an evaporator and a condenser, which can be modeled as a heat exchanger. The circulation of the solution is assured by a solution pump, and a solution heat exchanger is used to internally recover thermal energy.

Recent works [1-3] had presented dynamic models to evaluate the long-term behavior of a single-effect LiBr/water absorption chiller and also to follow its daily operational profile with good precision. These models may be used to study the interaction of the absorption unit with the other components of a solar-assisted cooling plant, in aim of optimizing both the layout of the system and the control logic of the chiller.

The use of dynamic optimization for analyzing the control possibilities allows to identify the optimal process behavior and select the best control strategy based on quantitative criteria. This implies finding the variable profiles that minimize the objective function in a system subject to disturbances [4]. From the mathematical model or system restrictions a total of 254 variables define the process and a total of 245 equations model the single stage absorption cooling cycle.

The process is modeled by the Differential-Algebraic Equation (DAE) system that represents de dynamics behavior of the system. Then, the optimal control problem is solved by discretizing the time domain, which converts it into a Non-Linear Programming (NLP) problem.

Work [5, 6] results shows that the optimal control strategy is able to optimize the overall performance of the thermal system, achieving a reduction in energy consumption between 0.73 and 2.55% compared with traditional control strategies. With this work we seek to reduce energy consumption for an absorption cycle by applying an optimal control strategy as it has been accomplished for other thermal systems.

Considering that the most inefficient energy consumption systems, identified in countries like Colombia, come from using equipment for cooling and space conditioning [7] and, besides, that during the summer season the energy generation is insufficient to satisfy the demand, the absorption refrigeration system becomes as an opportunity to solve this problem in a more definitive way.

The Non-Linear Programming (NLP) problem is implemented in Gams® and solved with the solver CONOPT.

Reference

[1] G. Evola, N. Pierres, F. Boudehenn, P. Papillon.

[2] P. Kohlenbach, F. Ziegler, “A dynamic simulation model for transient absorption chiller performanc. Part I: The model” International Journal of Refrigeration, pp. 217-225. 2008

[3] P. Kohlenbach, F. Ziegler, “A dynamic simulation model for transient absorption chiller performanc. Part II: Numerical results and experimental verification” International Journal of Refrigeration, pp. 217-225. 2008

[4] J. Cuthrell, L. Biegler, “On the optimization of differential-algebraic process system” Annual AIChE Meeting, Miami, Fl, 1986.

[5] Z. Ma, S. Wang, “Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm” Applied Energy, pp. 198-211, 2011

[6] T. Chow, G. Zhang, Z. Lin, C. Song, "Gobal optimization of absorption chiller system by genetic algorithm and neural network" Energy and Buildings, pp. 103-109, 2002.

[7] “Plan Energetico Nacional Colombia: Ideario Energético 2050”, Unidad de Planeación Minero Energetica, 2015


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