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Selection of building thermal insulation materials using robust optimization

  • BUILDING COMPONENTS AND BUILDINGS
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

Life cycle assessment (LCA) is commonly used to analyze the environmental profile of a material and product. For example, the selection of building insulation is crucial to ensure that energy and financial conservation goals can be met. Most of the current methods used in material selection are deterministic in nature, which ignores or neglects the effects caused by uncertainties present in various factors, including how the thermal insulation properties of insulation may change with time. In addition, during the use phase, uncertainty in operating conditions may also affect the performance of the selected material, affecting the overall environmental and economic performance. In practice, when material usage is in large quantities, uncertainty may have significant impacts. This article proposes a novel optimization model-based approach in selecting suitable building insulations using LCA results and economic consideration together with its uncertainties.

Methods

This work presents an optimization-based approach to select building thermal insulation materials in the presence of data uncertainties. Firstly, we developed a deterministic model, incorporating life cycle assessment and costing to evaluate various environmental impacts and costs of the different materials. Next, by considering the different uncertainties in the data used for the environmental and cost assessments, a robust optimization approach is proposed to derive the second model. A novel solution algorithm is then developed to obtain model solutions efficiently.

Results and discussion

Computational studies based on a high-rise apartment in Shanghai were performed to test the applicability and performance of the proposed solution. The results demonstrate that the proposed robust optimization model is effective in mitigating the data uncertainties in the material selection optimization and outperforms the deterministic model significantly in terms of probability of achieving cost and environmental impact requirements under uncertainty. In addition, from our case studies, it was identified that parametric uncertainties and payback period exert great influence in decision-making.

Conclusions

The work which had expanded the basic deterministic model to include multiple aspects rather than a single objective, and taking into account of parametric uncertainties to mitigate the risks of uncertainties in real applications allows building engineers to select appropriate building material(s) based on both environmental and economic consideration. Furthermore, this work also proposes a novel solution approach to address a wide range of intractable nonlinear optimization problems.

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Abbreviations

\( \mathbbm{I} \) :

set of exterior walls. \( \mathbbm{I}=\left\{1,,,\dots,,, I\right\},i\in \mathbbm{I}. \)

\( \mathbbm{J} \) :

set of insulation materials. \( \mathbbm{J}=\left\{1,,,\dots,,, J\right\},j\in \mathbbm{J}. \)

ℕ :

set of environmental impacts. ℕ = {1, …, N}, n ∈ ℕ.

a i :

srea of wall i. [m2]

c j :

the cost of 1 m3 insulation material j. [rmb]

k i, j :

the thermal resistivity of material j on wall i. [mK/W]

p j, n :

the impact n of 1 m3 material j. [kg-eq/m3]

q n :

the impact n resulted from the generation of 1 kWh electricity. [kg-eq/kWh]

r i :

the thermal resistance of the wall i excluding insulation materials. [m2K/W]

t :

time. [year]

T 1 :

recovery time. [year]

T 2 :

length of the use phase. [year]

u i :

heating and cooling coefficient of wall i. [sK]

v :

average price of 1 kWh electricity. [rmb]

x i, j :

the thickness of insulation material j used on wall i. [m]

Γ ∈ ℝ :

budget of uncertainty

References

  • Administration CM (2017) Shanghai annually average temperature. from http://www.cma.gov.cn/. Accessed 11 Apr 2017

  • Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math Oper Res 23(4):769–805

    Article  Google Scholar 

  • Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

    Article  Google Scholar 

  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53(3):464–501

    Article  Google Scholar 

  • Breuker MS, Braun JE (1998) Common faults and their impacts for rooftop air conditioners. HVAC&R Res 4(3):303–318

    Article  Google Scholar 

  • Chassein A, Goerigk M (2017) Minmax regrets combinatorial optimization problems with ellipsoidal uncertainty sets. Eur J Oper Res 258(1):58–69

    Article  Google Scholar 

  • Çomakl K, Yüksel B (2003) Optimum insulation thickness of external walls for energy saving. Appl Therm Eng 23(4):473–479

    Article  Google Scholar 

  • Djuric N, Novakovic V, Holst J, Mitrovic Z (2007) Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools. Energ Build 39(4):471–477

    Article  Google Scholar 

  • Dombaycı ÖA (2007) The environmental impact of optimum insulation thickness for external walls of buildings. Build Environ 42(11):3855–3859

    Article  Google Scholar 

  • Ee AW, Shaik SM, Khoo HH (2015) Development and application of a combined approach for inherent safety and environmental (caisen) assessment. Process Saf Environ Prot 96:138–148

    Article  CAS  Google Scholar 

  • GB (1987) GBJ86: domestic building design standard. GB

  • Gustafsson S-I (2000) Optimization of insulation measures on existing buildings. Energ Build 33(1):49–55

    Article  Google Scholar 

  • Hu M, Weir JD, Wu T (2012) Decentralized operation strategies for an integrated building energy system using a memetic algorithm. Eur J Oper Res 217(1):185–197

    Article  Google Scholar 

  • Jiménez-González C, Constable DJ, Ponder CS (2012) Evaluating the “greenness” of chemical processes and products in the pharmaceutical industry - a green metrics primer. Chem Soc Rev 41(4):1485–1498

    Article  Google Scholar 

  • Kaynakli O (2012) A review of the economical and optimum thermal insulation thickness for building applications. Renew Sust Energ Rev 16(1):415–425

    Article  Google Scholar 

  • Korjenic A, Petránek V, Zach J, Hroudová J (2011) Development and performance evaluation of natural thermal-insulation materials composed of renewable resources. Energ Build 43(9):2518–2523

    Article  Google Scholar 

  • Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11(1):161–179

    Article  Google Scholar 

  • Organization IS (2006) ISO 14044: Environmental Management, Life Cycle Assessment, Requirements and Guidelines. ISO

  • Rezaee R, Brown J, Augenbroe G, Kim J (2015) Assessment of uncertainty and confidence in building design exploration. AI EDAM 29:429–441

    Google Scholar 

  • Scholtes S (2004) Nonconvex structures in nonlinear programming. Oper Res 52(3):368–383

    Article  Google Scholar 

  • Siegel J, Walker I, Sherman M (2002) Dirty air conditioners: energy implications of coil fouling. In proceedings of the 2002 ACEEE summer study on energy efficiency buildings 1:287–300

  • Soleimanian A, Jajaei GS (2013) Robust nonlinear optimization with conic representable uncertainty set. Eur J Oper Res 228(2):337–344

    Article  Google Scholar 

  • Soyster AL (1973) Technical note - convex programming with set-inclusive constraints and applications to inexact linear programming. Oper Res 21(5):1154–1157

    Article  Google Scholar 

  • Tsai W-H, Yang C-H, Chang J-C, Lee H-L (2014) An activity-based costing decision model for life cycle assessment in green building projects. Eur J Oper Res 238(2):607–619

    Article  Google Scholar 

  • Wang R, Work D (2014) Application of robust optimization in matrix-based lci for decision making under uncertainty. Int J Life Cycle Assess 19(5):1110–1118

    Article  Google Scholar 

  • Wang L, Mathew P, Pang X (2012) Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building. Energ Build 53:152–158

    Article  Google Scholar 

  • Yang C, Li H, Rezgui Y, Petri I, YuceB. Chen B, Jayan B (2014) High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energ Build 76:92–101

    Article  Google Scholar 

Download references

Funding

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and the Energy Studies Institute, National University of Singapore.

Author information

Authors and Affiliations

  1. Department of Industrial Systems Engineering and Management, National University of Singapore, 1 Engineering Drive 2, Singapore, Singapore

    Menghua Sun, William B. Haskell & Tsan Sheng Ng

  2. Environmental Research Institute, National University of Singapore, 1 Create Way, Singapore, Singapore

    Alvin W. L. Ee

  3. School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore, Singapore

    Harn Wei Kua

Authors
  1. Menghua Sun
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  2. William B. Haskell
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  3. Tsan Sheng Ng
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  4. Alvin W. L. Ee
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  5. Harn Wei Kua
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Corresponding author

Correspondence to Tsan Sheng Ng.

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Communicated by: Edeltraud Guenther

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Sun, M., Haskell, W.B., Ng, T. et al. Selection of building thermal insulation materials using robust optimization. Int J Life Cycle Assess 25, 443–455 (2020). https://doi.org/10.1007/s11367-019-01711-w

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