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

Kolios, Athanasios and Jiang, Ying and Somorin, Tosin and Sowale, Ayodeji and Anastasopoulou, Aikaterini and Anthony, Edward J. and Fidalgo, Beatriz and Parker, Alison and McAdam, Ewan and Williams, Leon and Collins, Matt and Tyrrel, Sean (2018) Probabilistic performance assessment of complex energy process systems – the case of a self-sustained sanitation system. Energy Conversion and Management, 163. pp. 74-85. ISSN 0196-8904 (

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A 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.