Abstract: A hybrid model for predicting corrosion in a system integrates a physics-based model developed using laboratory data and a machine-learning model developed using in-field data. Said hybrid model may be used, for example, in methods by: determining a physics-based measurement of corrosion using a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab-based measurements; determining a machine learning-based measurement of corrosion using a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field-based measurements; and applying an ensemble method to the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to yield an estimated measure of corrosion of the substrate. The hybrid model may be applied to corrosion mechanisms that occur in, for example, hydrocarbon transportation systems, hydrocarbon production systems, hydrocarbon refining systems, and alkylation systems.

Abstract: A method of determining a risk state for a complex industrial process includes graphically depicting a process flow diagram on a graphical user interface with a computer system, the process flow diagram including one or more side streams of the complex industrial process, monitoring one or more process parameters of the one or more side streams, and calculating a risk state for each side stream with the computer system based on the one or more process parameters. The risk state of each side stream is then graphically depicted on the process flow diagram by assigning a probability indicator to each side stream based on the risk state calculated by the computer system, wherein the probability indicator comprises a graphical output recognizable by a user and corresponding to a predetermined scale of failure probability.