Framework for Design under Uncertainty Including Inherent Safety, Environmental Assessment, and Economic Performance of Chemical Processes

Abstract

Typically, analysis and design of chemical processes is carried out considering average values of inherently uncertain data that pertain to key design and process variables that shape its performance. In this work, a methodology that includes uncertainty in process safety, an item commonly addressed after the final design has been achieved, along with environmental and economic metrics of the process at the design stage is presented. The approach is based on the use of Monte Carlo simulation techniques to explicitly include uncertainty in the input variables and propagate it through the model. The proposed approach generates a range of performance outcomes through distribution profiles that can be probabilistically characterized, and zones of downside risks and upside opportunities can be identified. A case study that shows the application of the proposed methodology based on the production of ethylene from natural gas was conducted. The results show that although the safest design presented a lower return on investment (ROI), the probability of this scenario to materialize was relatively low, suggesting that a more desirable ROI value for that design would be likely. © 2019 American Chemical Society.