City University of Hong Kong Department Name

 

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System optimization studies

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The best solution for determining the optimal control for a given system is to have a detailed model of the complete process that operates in parallel with the actual system. An optimization algorithm, such as one from the evolutionary algorithms (EA), is then applied to this model in order to determine the optimal control. EA¡¯s are search and optimization techniques based on the principles of natural evolution. There are four main streams in evolutionary algorithms namely Genetic Algorithms (GA), Genetic Programming (GP), Evolution Strategies (ES) and Evolutionary Programming (EP). The one most commonly used is GA, with binary encoded parameters. It allows a population composed of many individuals to evolve the following two simple concepts of natural evolution: ¡°survival of the fittest¡± and ¡°reproduction¡±. A new concept of integrating neural network and genetic algorithm in the optimal control of chiller system was introduced. Based on a commercial absorption unit, neural network was used to model the system characteristics and genetic algorithm was used as a global optimization tool. Other optimization techniques for reaching the best design options or operating parameters were also explored for various systems.

 Optimization process based on GA and ANN

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 Outputs

 COP  coefficient of performance

 Qf   fuel consumption rate

 Pcool   cooling water pump electric power

 Pchiller     chilled water pump electric power

 Inputs

 Qc     cooling load

 m2    mass flow rate of chilled water supply

 m3    mass flow rate of cooling water return

 T2      temperature of chilled water supply

 T3      temperature of cooling water return

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ANN model of an absorption chiller system for power consumption

Results of fitness values against building-mix distribution at a district cooling site

 

Related publications :

 

Journal articles:

Chow TT, Zhang GQ, Lin Z, Song CL. Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34(1), 2002, 103-109.

Fong KF, Hanby VI, Chow TT. Optimization of MVAC systems for energy management by evolutionary algorithm. Facilities, 21(10), 2003, 223-232.

Chow TT, Chan ALS, Song CL. Building mix optimization in district cooling system implementation. Applied Energy, Vol.77(1), 2004, 1-13.

Fong KF, Hanby VI, Chow TT. HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38(3), 2006, 220-231.

Chan ALS, Hanby VI, Chow TT. Optimization of distribution piping network in district cooling system using genetic algorithm with local search. Energy Conversion and Management, 48(10), 2007, 2622-2629.

Fong KF, Chow TT, Hanby VI. Development of optimal design of solar water heating system by using evolutionary algorithm. ASME Journal of Solar Energy Engineering, 129(4), November 2007, 499-501.

Fong, KF, Hanby VI, Chow TT. A robust evolutionary algorithm for HVAC engineering optimization. HVAC&R Research, 14(5), 2008, 683-705.

Fong KF, Hanby VI, Chow TT. System optimization for HVAC energy management using the robust evolutionary algorithm. Applied Thermal Engineering, 29(11-12), 2009, 2327-2334.

Fong KF, Chow TT, Lin Z, Chan LS. Simulation-optimization of solar-assisted desiccant cooling system for subtropical Hong Kong. Applied Thermal Engineering, 30(2-3), 2010, 220-228.

Fong KF, Yuen SY, Chow CK, Leung SW. Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods. Applied Energy, 87(11), 2010, 3494-3506.

Zhang X, Fong KF and Yuen KSY. A novel artificial bee colony algorithm for HVAC optimization problems. HVAC&R Research, 19(6), 2013, 715-731.

Sun YJ, Huang GS, Li ZW, Wang SW. Multiplexed optimization of complex AC systems. Building and Environment, 65, 2013, 99-108.

Conference papers:

Chow TT, Lin Z, Song CL, Zhang GQ. Applying neural network and genetic algorithm in chiller system optimization. Proceedings of Building Simulation 2001, the 7th IBPSA International Conference, Rio de Janeiro, Brazil, August 2001, pp.1059-1065.

Fong KF, Chow TT, Chan LS, Ma WL, Fong CK. A preliminary study of optimization of pipe route design of district cooling system. Proceedings of IAQVEC 2001, the 4th International Conference on IAQ, Ventilation & Energy Conservation in Buildings, Changsha, China, Oct 2001, Vol.2, pp.1031-1039.

Fong KF, Hanby VI, Chow TT. Optimal energy management of HVAC systems by using evolutionary algorithm. Proceedings of CIB World Building Congress 2004, Toronto, Canada, May 2004. (CD ROM)

Fong KF, Chow TT, Hanby VI. Development of optimal design of solar water heating system by using evolutionary algorithm. Proceedings of ISEC2005, 2005 Solar World Congress, Orlando, USA, Aug 2005. (CD ROM)

Fong KF, Hanby VI, Chow TT. Application of evolution strategies for HVAC optimization problem. Proceedings of SB05Tokyo “Action for Sustainability”, the 2005 World Sustainable Building Conference in Tokyo, Japan, Sep 2005. (CD ROM)

Fong KF, Hanby VI, Chow TT. Effective paradigm of evolutionary algorithm for system design of centralized solar water heating for high-rise residential building. Proceedings of Renewable Energy 2006 International Conference and Exhibition, Chiba, Japan, Oct 2006. (CD-ROM)

Chan ALS, Hanby VI, Chow TT. Application of genetic algorithm with local search in optimal piping network design of a district cooling system. Proceedings of IAQVEC 2007, the 6th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings, Sendai, Japan, Oct 2007. (CD-ROM)

Fong KF, Chow TT. Comparative study of different paradigms of evolutionary algorithm in the context of system optimization for solar desiccant cooling. Proceedings of eSim 2008, Quebec City, Canada, May 2008. (CD-ROM)

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