AI-driven predictive testing for microbially influenced corrosion in coated materials

Overview

This innovative solution utilizes an AI-driven predictive model integrated with hybrid simulation methods to effectively assess and predict the susceptibility of coated materials to microbially influenced corrosion (MIC). By simulating microbial biofilm dynamics and environmental factors such as temperature, seasonality, and salinity, the model replicates real-world MIC processes. It differentiates between microbial-induced corrosion and abiotic degradation, providing precise, quantifiable data on coating performance. This information is crucial for targeted improvements in MIC resistance, enhancing the longevity and durability of coatings in various industrial applications.

Technical specifications

Key features:

• AI-driven predictive modeling: Combines AI with hybrid simulations to capture microbial biofilm dynamics and environmental conditions.
• Differentiation capabilities: Accurately distinguishes microbial-induced corrosion from abiotic degradation.
• Simulation of real-world conditions: Incorporates temperature, salinity, and microbial species like sulfate-reducing and iron-oxidizing bacteria.
• Advanced testing methods: Utilizes techniques like Electrochemical Impedance Spectroscopy (EIS) and AI-aided image analysis for data collection.
• Model refinement: Iteratively validated and refined using real-world industrial data for enhanced accuracy.

Technology readiness level

This technology is currently at Technology Readiness Level 3, indicating that it has been demonstrated in a laboratory setting. Future validation will involve controlled biofilm cultures and accelerated corrosion tests under varied environmental conditions to further optimize and validate the model with real-world data.