A methodology is presented to solve computer-aided molecular design (CAMD) and process design model problems under consideration of fluid property uncertainty. The uncertainties of the group contribution (GC) property prediction models are quantified for which asymptotic approximation of the covariance of parameter estimation errors is performed following a regression analysis. A Monte Carlo sampling technique generates GC factor samples within the respective uncertainties, which are evaluated separately as constraints to the CAMD optimization problem. The methodology is applied to identify working fluid candidates for an organic Rankine cycle used as waste heat recovery system in a marine diesel engine. CAMD under property uncertainties allows (1) identifying robust and more reliable molecules with respect to property uncertainties (conservative approach) and (2) enhancing the search space in order to find potentially globally optimal working fluids (optimistic approach). Suitable Hydrofluoroolefins (HFO) have been identified as potential working fluids for waste heat recovery.

提出了一种在考虑流体性质不确定性的情况下解决计算机辅助分子设计（CAMD）和过程设计模型问题的方法。量化了组贡献（GC）属性预测模型的不确定性，为此，在回归分析之后对参数估计误差的协方差进行渐近逼近。蒙特卡洛采样技术会在各个不确定性范围内生成GC因子样本，这些样本将作为CAMD优化问题的约束条件进行单独评估。该方法学可用于识别有机朗肯循环的工作流体候选物，以用作船用柴油机的余热回收系统。在属性不确定性下的CAMD允许（1）就属性不确定性确定稳健且更可靠的分子（保守方法），以及（2）扩大搜索空间以发现潜在的全局最佳工作流体（优化方法）。合适的氢氟烯烃（HFO）已被确定为废热回收的潜在工作流体。