Oil-free centrifugal chiller optimal operation

Centrifugal chillers, using variable-speed turbo compressors with magnetic bearings, are becoming very common in Heating, Ventilation and Air Conditioning (HVAC) systems. Such solution guarantees superior energy efficiency, mostly under part load conditions, compared with traditional equipments, and it provides additional advantages such as light weight and a compact package. On the other hand, turbo machinery adds its own complexity to the whole HVAC system and its efficient management is a non-trivial task. In this paper a hybrid optimisation technique is employed to determine optimal operation, under various working conditions, for air-condensed water centrifugal chillers. The proposed method provides optimal solutions using a combination of two algorithms: A random population-based optimiser, the Gravitational Search Algorithm (GSA), followed by the deterministic Levenberg-Marquardt (LM) algorithm. The hybrid method effectively overcomes the problem of high sensitivity to initial conditions of LM technique and a shortcoming of GSA which reduces its searching efficiency when close to the optimum. The hybrid method has been tested in a Matlab®-based simulation environment where the performance of an air-condensed centrifugal chiller is adequately evaluated. Simulation results guarantee high energy efficiency in a wide range of chiller working conditions.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[3]  Petter Nekså,et al.  Oil free turbo-compressors for CO2 refrigeration applications , 2013 .

[4]  Tony J. Dodd,et al.  Why ‘GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity , 2011, Natural Computing.

[5]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[6]  Marco Corradi,et al.  A simplified method to evaluate the seasonal energy performance of water chillers , 2010 .

[7]  Yung-Chung Chang,et al.  Optimal chiller loading by genetic algorithm for reducing energy consumption , 2005 .

[8]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[9]  Alessandro Beghi,et al.  A multi-phase genetic algorithm for the efficient management of multi-chiller systems , 2009, 2009 7th Asian Control Conference.

[10]  Alessandro Beghi,et al.  A PSO-based algorithm for optimal multiple chiller systems operation , 2012 .

[11]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[12]  Marina Fruehauf,et al.  Nonlinear Programming Analysis And Methods , 2016 .

[13]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[14]  R. Conry,et al.  Magnetic Bearings, Variable Speed Centrifugal Compression And Digital Controls Applied In A Small Tonnage Refrigerant Compressor Design , 2002 .