Abstract
Computer-Aided Process Engineering (CAPE) has transformed how we analyze, design, and optimize energy processes. Yet, even advanced models rest on uncertain ground: their reliability depends on how well future operating environments are described-environments that are dynamic, complex, and deeply uncertain. In practice, uncertainty is often reduced to local parameter variations, driven by limited data, computational burden, and overconservative robust formulations. This narrow treatment creates a false sense of confidence: Designs that perform well in theory often fail in real-world operation. In a century marked by economic, climatic, and technological volatility, designing under uncertainty is no longer optional; it is essential.We have developed approaches that place uncertainty at the core of energy process modeling and design. This paper provides an overview of these methods and how uncertainty can be explicitly represented, quantified, and embedded into the design process.We present approaches to characterize uncertainties under data scarcity, including imprecise probabilities to distinguish between aleatory and epistemic uncertainty. To propagate uncertainty through computationally intensive models, we introduce surrogate-assisted techniques that exploit structural sparsity, enabling the analysis of systems with large numbers of uncertain inputs (100+) while mitigating the curse of dimensionality. These methods are integrated into optimization frameworks that target expected performance, robustness, and, for the first time, antifragility-systems that can benefit from variability rather than merely withstand it. We illustrate these approaches across applications ranging from detailed process models to system-level energy analyses, advancing a shift in CAPE toward designs suited for an unpredictable world.