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

机译：复杂能源过程系统的概率性能评估–自持卫生系统

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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.
机译：开发了一种概率建模方法，并将其应用于调查创新型卫生系统“纳米膜厕所”（NMT）的能源和环境绩效。该系统通过先进的能源和水回收岛来处理人类排泄物，以解决当前和未来的卫生需求。由于系统输入材料的复杂设计和固有特性，因此存在许多随机变量，这些变量可能会严重影响系统的性能。本研究中采用的非侵入式概率方法将有限数量的确定性热力学过程仿真与人工神经网络（ANN）逼近模型和蒙特卡洛仿真（MCS）结合起来，以评估系统不确定性对NMT预测性能的影响系统。过程性能指标的联合概率分布表明，斯特林发动机（SE）的功率输出范围为61.5–73 W，置信区间（CI）为95％。此外，NMT系统极有可能（具有95％的CI）在15.8至35 W之间实现正净功率输出；一项敏感性研究表明，系统功率性能主要受SE加热器温度的影响。对NMT设计的环境性能进行的调查（包括水回收和CO2 / NOx排放）表明，与传统系统相比，它具有显着的环境效益。概率分析的结果可以更好地指导系统设计和运营策略的未来改进，并且该概率评估框架也可以应用于类似的复杂工程系统。

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