Article
Reference
Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks
JAXA-ROZEN, Marc, PRATIWI, Astu Sam, TRUTNEVYTE, Evelina
JAXA-ROZEN, Marc, PRATIWI, Astu Sam, TRUTNEVYTE, Evelina. Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks. International Journal of Life Cycle Assessment , 2021
DOI : 10.1007/s11367-021-01921-1
Available at:
http://archive-ouverte.unige.ch/unige:151614
Disclaimer: layout of this document may differ from the published version.
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Online Resource 2: Supplementary case study results
Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks
Marc Jaxa-Rozen
1*, Astu Sam Pratiwi
1, Evelina Trutnevyte
11
Renewable Energy Systems, Institute for Environmental Sciences (ISE), Section of Earth and Environmental Sciences, University of Geneva, Switzerland
* corresponding author (Uni Carl Vogt, Boulevard Carl Vogt 66, CH-1211 Geneva 4, Switzerland; +41 22 379040; [email protected])
Contents:
Table S1
Figures S1-S22
Table S1: Parameter ranges tested for each design alternative. Parameters in bold are retained after sensitivity screening; parameters excluded after screening are set to the average value of their range.
Parameters Unit
Design alternatives Networked
heat pumps Central heat
pump Direct
heating Average weight of mud per m of well kg/m 0.04 - 20 0.04 - 54 45 - 54
Circulation pump efficiency 0.7 - 0.85 0.7 - 0.85 0.7 - 0.85
Depth of slim hole/exploration well m 20 - 150 50 - 250 150 - 250
Diesel required by machinery per m of well MJ/m 204.8 - 307.2 204.8 - 307.2 204.8 - 307.2 Distribution pipe length inside substation m 100 - 2000 100 - 2000 100 - 2000 Drilling electricity consumption kWh/m 360 - 445 360 - 445 360 - 445 Drilling rig mobilization distance km 100 - 300 100 - 300 100 - 300
Drilling rig weight ton 80 - 120 90 - 600 400 - 800
Duration of slim hole/exploration pump test day 10 - 30 10 - 30 10 - 30
Exploration well count Integer value 1 - 6 1 - 6 1 - 6
Geothermal flow rate L/s 10 - 80 20 - 80 20 - 80
Geothermal gradient °C/m 0.02 - 0.035 0.02 - 0.035 0.02 - 0.035
Geothermal main well count Integer value 2 - 6 2 - 6 2 - 6
Geothermal production temperature °C 10 - 55 20 - 55 78 - 120
Heat exchanger lifetime year 8 - 12 8 - 12 8 - 12
Heat exchanger recovered fraction - 0.3 - 0.7 0.3 - 0.7 0.3 - 0.7
Heat pump COP multiplier 1/ideal COP 0.3 - 0.6 0.3 - 0.6 -
Heat pump lifetime year 10 - 20 10 - 20 -
Heat pump recovered fraction - 0.3 - 0.7 0.3 - 0.7 0.3 - 0.7
Machinery weight ton 30 - 50 30 - 50 30 - 50
Other equipment lifetime year 5 - 15 5 - 15 5 - 15
Piping length between wells (HDPE) m 250 - 750 250 - 750 250 - 750
Production tube diameter inch 6 - 8 6 - 8 6 - 8
Pumping pressure maintained at surface bar 1 - 11 1 - 11 1 - 25
Refrigerant leak fraction - 0.02 - 0.05 0.02 - 0.05 -
Refrigerant recovery efficiency - 0.5 0.5 0.5
Submersible pump count Integer value 1 - 6 1 - 6 1 - 6
Submersible pump efficiency - 0.45 - 0.6 0.45 - 0.6 0.45 - 0.6
Submersible pump lifetime year 7 - 14 7 - 14 7 - 14
Submersible pump recovered fraction - 0.3 - 0.7 0.3 - 0.7 0.3 - 0.7
Surface diameter of the well m 0.445 - 1 0.445 - 0.61 0.445 - 0.61
Surface equipment transport distance km 30 - 1000 30 - 1000 30 - 1000 Transport distance for other drilling equipment km 300 - 700 300 - 700 300 - 700
Transport distance to dispose cuttings km 5 - 12 5 - 12 5 - 12
Transport distance to dispose the material at
the end-of-life km 20 - 50 20 - 50 20 - 50
Well production index - 2 - 10 2 - 10 2 - 10
Well testing duration (main wells) day 10 - 60 30 - 90 30 - 90
Figure S1: Steps of the preliminary screening analysis used to reduce the number of parameters for each design alternative of geothermal heating networks.
Figure S2: Probability plot for the obtained distribution of water consumption (blue markers), relative to a reference distribution (red line). Top rows: normal reference distribution; bottom rows: log-normal reference distribution. We fit reference distributions using the
scipy.stats Python package, and find that the water consumption impacts deviate from these distributions.
Figure S3: Distribution of relative regret between design alternatives for each impact, using Latin hypercube ensembles (n=12,000).
For each boxplot, the mean is denoted by a black marker, and the median by a black line. Boxes show the interquartile range; Boxes show the interquartile range; whiskers are extended to the full range of the data.
Figure S4: Correlation between impacts (Pearson’s r), using Latin hypercube ensembles (n=12,000). Left panel: networked heat pump alternative; middle panel: central heat pump alternative; right panel: direct heating alternative.
Figure S5: Convergence of Sobol total indices for the networked heat pump design alternative. Each colored line represents a different input parameter; Figure S8 reports the indices computed for n=100,000 scenarios. Shaded envelopes show 95% confidence interval
with 100 bootstrap resamples.
Figure S6: Convergence of Sobol second-order indices for the networked heat pump design alternative. Each colored line represents a different pairwise interaction; Figure S16 reports the indices computed for n=100,000 scenarios. Shaded envelopes show 95%
confidence interval with 100 bootstrap resamples.
Figure S7: Spearman rank correlations between sensitivity indices estimated for each impact, for the networked heat pump alternative. Left panel: Sobol total indices (ST); right panel: PAWN median indices. In both matrices, the diagonal shows the rank
correlation between ST indices and PAWN median indices for each impact.
Figure S8: Sobol first-order (S1) and total (ST) indices for the networked heat pump alternative (n=100,000 scenarios).
Figure S9: Impact of geothermal gradient on the variance of water consumption, for the networked heat pump alternative. The dashed line indicates unconditional variance in the ensemble of 12,000 Latin hypercube scenarios sampled for uncertainty analysis.
The black line shows conditional variance using 10 conditioning intervals for the value of the geothermal gradient, in the same ensemble; we use the SAFE Toolbox Python package for this computation. The dotted line shows the unconditional variance obtained after fixing the geothermal gradient to its minimum bound (0.02), and recomputing an ensemble of 12,000 Latin hypercube scenarios.
Figure S10: Relative variable importances for the central heat pump design alternative, grouped by environmental impact (subplots) and set of sensitivity indices (subplot columns). For each environmental impact, the vector of sensitivity indices is rescaled to [0,1], in
each method separately.
Figure S11: Spearman rank correlations between sensitivity indices estimated for each impact, for the central heat pump alternative.
Left panel: Sobol total indices (ST); right panel: PAWN median indices. In both matrices, the diagonal shows the rank correlation between ST indices and PAWN median indices for each impact.
Figure S12: Sobol first-order (S1) and total (ST) indices for the central heat pump alternative (n=100,000 scenarios).
Figure S13: Relative variable importances for the direct heating alternative, grouped by environmental impact (subplots) and set of sensitivity indices (subplot columns). For each environmental impact, the vector of sensitivity indices is rescaled to [0,1], in each
method separately.
Figure S14: Spearman rank correlations between sensitivity indices estimated for each impact, for the direct heating alternative. Left panel: Sobol total indices (ST); right panel: PAWN median indices. In both matrices, the diagonal shows the rank correlation between
ST indices and PAWN median indices for each impact.
Figure S15: Sobol first-order (S1) and total (ST) indices for the direct heating alternative (n=100,000 scenarios).
Figure S16a: Second-order Sobol indices for the networked heat pump alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S16b: Second-order Sobol indices for the networked heat pump alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S17a: Second-order Sobol indices for the central heat pump alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S17b: Second-order Sobol indices for the central heat pump alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S18a: Second-order Sobol indices for the direct heating alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S18b: Second-order Sobol indices for the direct heating alternative, representing the contribution towards variance of pairwise interactions between inputs. Values are computed over 100 bootstrap resamples with n=100,000 scenarios.
Figure S19: Subset of scenarios of interest for the central heat pump alternative (in blue), and full ensemble of scenarios sampled with Latin hypercube for this alternative (in gray; n=12,000). Impacts are shown using parallel coordinates, with each line representing one scenario. For each impact, outcomes are normalized to [0,1] across the full ensemble. A Gaussian kernel density estimate presents the
distribution of outcomes for each impact, in the scenarios of interest (in dark blue) and in the full ensemble of scenarios for the central heat pump alternative (in black).
Figure S20: Left column: clusters identified using spectral clustering in the subset of scenarios of interest, for the central heat pump alternative. Impacts are re-normalized to [0,1] in the subset of scenarios of interest. Right column: combinations of uncertainty
ranges associated with each cluster, identified using PRIM. The normalized uncertainty ranges show the full range of input uncertainties sampled in the Latin hypercube ensemble; the gray lines show the restricted range of each parameter found to be
significant (p<0.05) for each cluster. Estimated p-values for the significance of each restriction are shown in parenthesis.
Figure S21: Subset of scenarios of interest for the direct heating alternative (in blue), and full ensemble of scenarios sampled with Latin hypercube for this alternative (in gray; n=12,000). Impacts are shown using parallel coordinates, with each line representing one scenario. For each impact, outcomes are normalized to [0,1] across the full ensemble. A Gaussian kernel density estimate presents the distribution of outcomes for each impact, in the scenarios of interest (in dark blue) and in the full ensemble of scenarios for the direct
heating alternative (in black).
Figure S22: Left column: clusters identified using spectral clustering in the subset of scenarios of interest, for the direct heating alternative. Impacts are re-normalized to [0,1] in the subset of scenarios of interest. Right column: combinations of uncertainty ranges associated with each cluster, identified using PRIM. The normalized uncertainty ranges show the full range of input uncertainties sampled in the Latin hypercube ensemble; the gray lines show the restricted range of each parameter found to be
significant (p<0.05) for each cluster. Estimated p-values for the significance of each restriction are shown in parenthesis.
