Process synthesis methodologies have traditionally focused on optimizing the economic criteria. However, owing to the complexity of real systems, external factors, such as expected or unexpected events or disturbances, negatively affect the performance of optimal networks. In this context, it becomes essential to consider the effect of such events on the performance of the network in the early design stages. This work presents a comprehensive review of the contributions related to this relevant topic, focusing on two widely utilized indicators: reliability and resilience. Reliability focuses on the probability of system failures due to disturbances, and resilience analyzes the behavior of the system after disturbances and its capacity to recover over time. Relevant contributions are reviewed, which present deterministic and stochastic methods to estimate these indicators, with a special focus on the design phase. Moreover, this work also presents a perspective on using machine learning methods on complex systems datasets as an emerging direction for enhancing the estimation of these properties. This contribution highlights recent advancements in this field and emphasizes the relevance of resilience and reliability as key metrics for developing safer processes with improved operability.

While the treatment of wastewater is an important issue that received significant attention in the past decades, improving the related technologies is only one part of a more complex task. Domestic wastewater is usually transported via the city's sewer system, and in many places, it is combined with rainwater. This means that disturbances, such as heavy rainfall or failures in the pipeline system, can lead to floods of polluted wastewater. Thus, it is important to design such transportation systems to be reliable. This work presents a methodology for generating several potential extensions to retrofit an existing water transportation network and increase its reliability. Reliability and feasibility evaluation is performed via the P-graph framework, after which the nondominated networks are collected. Results of the presented case study show that reliability can be increased 3 times by adding only some of the possible extensions to the network. The methodology proposed analysed 512 plausible retrofitting alternatives, from which 20 are non-dominated networks. This range of alternatives provides designers with insightful information to decrease water pollution and the vulnerability of wastewater systems.