Probabilistic performance assessment of complex energy process systems - The case of a self-sustained sanitation system

Simple item page
dc.contributor.authorKolios, Athanasios
dc.contributor.authorJiang, Ying
dc.contributor.authorSomorin, Tosin
dc.contributor.authorSowale, Ayodeji
dc.contributor.authorAnastasopoulou, Aikaterini
dc.contributor.authorAnthony, Edward J.
dc.contributor.authorFidalgo, Beatriz
dc.contributor.authorParker, Alison
dc.contributor.authorMcAdam, Ewan
dc.contributor.authorWilliams, Leon
dc.contributor.authorCollins, Matt
dc.contributor.authorTyrrel, Sean
dc.date.accessioned2018-03-13T10:20:46Z
dc.date.available2018-03-13T10:20:46Z
dc.date.issued2018-02-22
dc.description.abstractA probabilistic modelling approach was developed and applied to investigate the energy and environmental performance of an innovative sanitation system, the “Nano-membrane Toilet” (NMT). The system treats human excreta via an advanced energy and water recovery island with the aim of addressing current and future sanitation demands. Due to the complex design and inherent characteristics of the system’s input material, there are a number of stochastic variables which may significantly affect the system’s performance. The non-intrusive probabilistic approach adopted in this study combines a finite number of deterministic thermodynamic process simulations with an artificial neural network (ANN) approximation model and Monte Carlo simulations (MCS) to assess the effect of system uncertainties on the predicted performance of the NMT system. The joint probability distributions of the process performance indicators suggest a Stirling Engine (SE) power output in the range of 61.5–73 W with a high confidence interval (CI) of 95%. In addition, there is high probability (with 95% CI) that the NMT system can achieve positive net power output between 15.8 and 35 W. A sensitivity study reveals the system power performance is mostly affected by SE heater temperature. Investigation into the environmental performance of the NMT design, including water recovery and CO2/NOx emissions, suggests significant environmental benefits compared to conventional systems. Results of the probabilistic analysis can better inform future improvements on the system design and operational strategy and this probabilistic assessment framework can also be applied to similar complex engineering systems.en_UK
dc.identifier.citationKolios A, Jiang Y, Somorin T, et al., (2018) Probabilistic performance assessment of complex energy process systems – The case of a self-sustained sanitation system. Energy Conversion and Management, Volume 163, May 2018, pp. 74-85en_UK
dc.identifier.cris19766275
dc.identifier.issn0196-8904
dc.identifier.urihttp://dx.doi.org/10.1016/j.enconman.2018.02.046
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13078
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectProbabilistic performance assessmenten_UK
dc.subjectArtificial neural networken_UK
dc.subjectNano membrane toileten_UK
dc.subjectReinvent the toilet challengeen_UK
dc.subjectEnergy recoveryen_UK
dc.titleProbabilistic performance assessment of complex energy process systems - The case of a self-sustained sanitation systemen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Probabilistic_performance_assessment_of_complex_energy-2018.pdf
Size:
1.12 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)