Energy and parametric analysis of solar absorption cooling systems for an office building: a case study

ABSTRACT

Solar cooling via absorption is a promising and rapidly evolving technology in the field of renewable energies. It offers potential advantages in reducing reliance on fossil fuels and addressing environmental concerns such as pollution and global warming. However, the design of solar cooling systems often neglects the interdependencies among parameters, leading to outcomes that may deviate from actual performance. To address this issue, this study used TRNSYS in order to conduct a simulation of a solar absorption cooling system Followed by detailed statistical analysis by, utilizing “the Taguchi method” and “Response surface methodology” to systematically analyze the effects of key parameters. The analysis is carried out to meet a peak cooling demand of 25,000 kJ/hr for an office building located in Souk Ahras City (36° 17’11’“N, 7° 57” 4’’ E). The parameters investigated include collector area (A), collector slope (S), hot water tank volume (V), and water flow rate in the collector (F), with the aim of determining optimal values for improved system efficiency and cost-effectiveness. The optimized values obtained were as follows: Area = 10.46 m², Slope = 30°, Volume = 4 m³, and Flow = 780 kg/hr. Remarkably, a maximum solar fraction of SF = 0.76 was achieved, providing empirical evidence supporting the hypothesis that accounting for the interdependent influences among parameters is crucial in designing solar cooling systems to achieve desired objectives. These findings contribute to a more objective understanding of the design considerations for efficient and economically viable solar cooling systems.

KEYWORDS:

• Solar cooling
• absorption cooling system
• Souk Ahras city
• TRNSYS
• Taguchi method

Nomenclature

$\mathit{a}$	=	regression coefficients (-)
$\mathit{A}$	=	solar collector area (m2)
${\mathit{a}}_1,{\mathit{a}}_2$	=	heat loss coefficients (W/m2.°K)
${\mathit{A}}_{\mathit{i}}$	=	surface transfer area (m2)
$\mathit{CL}{\mathit{F}}_{\mathit{h}}$	=	cooling Load Factor for given hour (-)
$\mathit{CO}{\mathit{P}}_{\mathit{rated}}$	=	denotes the rated coefficient of performance of the chiller (-)
${\mathit{C}}_{\mathit{s}}$	=	soil’s specific heat (J/kg.°K)
$\mathit{C}{\mathit{p}}_{\mathit{chw}}$	=	specific heat capacity of chilled water (J/kg.°K)
$\mathit{C}{\mathit{p}}_{\mathit{hw}}$	=	specific heat capacity of hot water (J/kg.°K)
${\mathit{C}}_{\mathit{pf}}$	=	Specific heat of the fluid (J/kg.°K)
${\mathit{C}}_{\mathit{rated}}$	=	rated cooling capacity of the device (kJ/hr)
$\mathit{eff}$	=	motor Efficiency (-)
$\mathit{F}$	=	fluid flow through solar collector (kg/hr)
${\mathit{f}}_{\mathit{dei}}$	=	current fraction at which the chiller operates (-)
${\mathit{F}}_{\mathit{p}}$	=	part load operating factor for motor type (-)
${\mathit{F}}_{\mathit{s}}$	=	Service Allowance Factor (-)
${\mathit{F}}_{\mathit{u}}$	=	diversity factor of maximum design for each hour (-)
$\mathit{G}$	=	available global solar radiation on the collector plane (W/m2)
$H_p$	=	rated electrical horsepower of equipment motor (J/hr)
$\mathit{K}$	=	soil’s thermal conductivity (W/m.°K)
$\mathit{n}$	=	number of cases (-)
$\mathit{m}\mathit{chw}$	=	mass flow rate of chilled water (kg/hr)
${\mathit{m}}_{\mathit{fluid}}$	=	fluid mass (kg)
$\mathit{mhw}$	=	mass flow rate of hot water (kg/hr)
${\mathit{M}}_{\mathit{i}}$	=	the fluid mass at the node i.(kg)
${\mathit{m}}_{\mathit{l}}$	=	mass flow rate of the load side (kg/hr)
$m_s$	=	mass flow rate from the heat source side (kg/hr)
${\mathit{M}}_{\mathit{w}}$	=	mass of water converted to steam (kg)
${\mathit{N}}_{\mathit{p}}$	=	number of people (-)
${\mathit{Q}}_{\mathit{aux}}$	=	energy obtained from the auxiliary boiler (J/hr)
${\mathit{Q}}_{\mathit{el}}$	=	latent heat gain from equipment. (J/hr)
${\mathit{Q}}_{\mathit{coll}}$	=	energy collected by the solar collector (J/hr)
${\mathit{Q}}_{\mathit{chw}}$	=	heat energy of absorption and condensation rejected by the absorption chiller (J/hr)
${\mathit{Q}}_{\mathit{em}}$	=	sensible heat gain from electric motor (J/hr)
${\mathit{Q}}_{\mathit{er}}$	=	sensible heat gain from electric resistance (J/hr)
${\mathit{Q}}_{\mathit{hw}}$	=	energy of hot water entering the chiller (J/hr)
${\mathit{q}}_{\mathit{l}}$	=	latent heat gain per person for the degree or type of activity in the space (J/hr)
${\mathit{Q}}_{\mathit{lig}}$	=	sensible heat gain from lights (J/hr)
${\mathit{Q}}_{\mathit{need}}$	=	energy required to heat the liquid from entering condition to setpoint temperature (J/hr)
${\mathit{Q}}_{\mathit{pl}}$	=	latent heat gain from people (J/hr)
${\mathit{Q}}_{\mathit{ps}}$	=	sensible Heat Gain from people (J/hr)
${\mathit{Q}}_{\mathit{remove}}$	=	cooling capacity required to remove heat from the building (J/hr)
${\mathit{q}}_{\mathit{s}}$	=	sensible heat gain per person for the degree or type of activity in the space (J/hr)
$\mathit{S}$	=	solar collector slope (°)
${\mathit{T}}_{\mathit{chw}.\mathit{in}}$	=	temperature of chilled water entering the evaporator (°K)
${\mathit{T}}_{\mathit{a}}$	=	ambient temperature(°K)
${\mathit{T}}_{\mathit{amp}}$	=	surface amplitude temperature (°K)
${\mathit{T}}_{\mathit{chw}.\mathit{out}}$	=	temperature of chilled water leaving the evaporator (°K)
${\mathit{T}}_{\mathit{g}}$	=	temperature of the undisturbed ground (°K)
${\mathit{T}}_{\mathit{hw}.\mathit{in}}$	=	temperature of the water at its inlet (°K)
${\mathit{T}}_{\mathit{hw}.\mathit{out}}$	=	temperature of the water at its outlet (°K)
${\mathit{T}}_{\mathit{i}}$	=	node temperature (°K)
${\mathit{T}}_{\mathit{in}}$	=	temperature of liquid entering the boiler (°K)
${\mathit{T}}_{\mathit{mean}}$	=	annual average soil temperature at different depths and times (°K)
${\mathit{t}}_{\mathit{now}}$	=	represents the time of year in hours (hr)
${\mathit{T}}_{\mathit{set}}$	=	boiler setpoint temperature (°K)
${\mathit{t}}_{\mathit{shift}}$	=	the period of the soil temperature cycle (°K)
$\mathit{U}$	=	overall losses from the solar tank to the environment (J/hr.m2.°K)
$\mathit{V}$	=	main heat tank volume (m3)
$\mathit{W}$	=	lighting (Equipment) power output (J/hr)
$\mathit{x}$	=	system variables (-)
${\mathit{Z}}_{\mathit{i}}$	=	value of the performance properties (-)

Abbreviations

ANOVA	=	analysis of variance
S/N	=	signal-to-noise (dB)
COP	=	coefficient of Performance
ETC	=	evacuated tube collector
FPC	=	flat plate collector
TRNSYS	=	transient system simulation
MATLAB	=	matrix laboratory
MIWO	=	mineral wool
PTC	=	parabolic trough collector
SACS	=	solar absorption cooling system
SF	=	Solar fraction
TMY	=	typical meteorological year
Greek symbols	=
$\mathit{\alpha }$	=	thermal diffusivity of the soil (m2/s)
$\mathrm{\Delta }\mathit{T}$	=	difference between the inlet fluid temperature and ambient temperature (°K)
$\mathit{\eta }$	=	collector’s efficiency (-)
${\mathit{\eta }}_0$	=	optical or zero loss efficiency (-)
$\mathit{\rho }$	=	soil density (kg/m3)
$\mathit{\phi }$heat required to convert 0.45kg of water to steam (J/hr)	=

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Brahim Bacha

Brahim Bacha is a PhD student at the University of Souk-Ahras (Souk-Ahras, Algeria). He is a student researcher at the Physics Laboratory of matter and radiation working in the topic of optimization and developing solar air conditioning systems in civilian areas. His broader research interests include Renewable energy systems, Sustainable energy and environmental problems. Currently, Brahim is collaborating in a research project studying Mechanisms for developing energy quality in the city of Souk Ahras.

Nor Rebah

Nor Rebah earned her PhD from the University of Tlemcen in 2011 and her Habilitation to Lead Researches from Biskra University in 2016. Since 2024, she has served as a Professor at Souk Ahras University, where she leads the Materials and Renewable Energy team within the Laboratory of Physics of Matter and Radiation (LPMR). She specializes in the modeling of solar radiation, utilizing advanced techniques to predict and analyze solar radiation patterns. Moreover, her work extends to the optimization of solar hybrid systems. She is also involved in the development of nanomaterials tailored for solar applications.

Nouredine Sengouga

Nouredine Sengouga has obtained his PhD in Physical Electronics from Lancaster University (UK). He is a Professor at Biskra University (Algeria). He has taught several subjects such as: solar cells, semiconductor devices, basic electronics, basic physics. His research interests are solar cells and other semiconductor devices. He has supervised tens of theses and produced more than 100 publications and conference presentations and a reviewer for several journals.

Michel Aillerie

Michel Aillerie obtained a PhD in 1991 and the Habilitation to Lead Researches in 2001 at the Université Paul Verlaine of Metz, currently Université de Lorraine. He is Professor since 2005 and makes his research in the city of Metz at the Laboratoire Materiaux Optiques et Photonique, LMOPS, join laboratory of the Université de Lorraine and CentraleSupelec, France. His interests and activities concern two main themes. The first one concerns the Characterization of functional non-linear optical properties of materials for optoelectronic applications. The second one concerns the development and optimization of energy production and management systems in a sustainable development approach.