Aqueous corrosion testing and neural network modeling to simulate corrosion of supercritical CO2 pipelines in the carbon capture and storage cycle

• Main Authors: S Sim, MK Cavanaugh, P Corrigan, IS Cole, Nick Birbilis
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2013
• Subjects: Engineering; Chemical engineering; Civil engineering; Materials engineering; Science & Technology; Technology; Materials Science; Multidisciplinary; Metallurgy & Metallurgical Engineering; carbonic acid; carbon capture and storage; carbon dioxide; corrosion; neural network; pipeline; sulfuric; supercritical; STAINLESS-STEEL; DIOXIDE; BEHAVIOR; LIQUID; TRANSMISSION; SURFACE; DESIGN; PHASE; CCS; Carbonic acid
• Online Access: http://hdl.handle.net/10779/DRO/DU:24472810.v2; https://figshare.com/articles/journal_contribution/Aqueous_corrosion_testing_and_neural_network_modeling_to_simulate_corrosion_of_supercritical_CO2_pipelines_in_the_carbon_capture_and_storage_cycle/24472810

A database was constructed from tests in aqueous electrolytes simulating the damage that may occur to ferrous transport pipelines in the carbon capture and storage (CCS) process. Temperature and concentrations of carbonic acid (H2CO3), sulfuric acid (H2SO4), hydrochloric acid (HCl), nitric acid (HNO3), sodium nitrate (NaNO3), sodium sulfate (Na2SO4), and sodium chloride (NaCl) were varied; the potentiodynamic polarization response, along with physical damage from exposure, was measured. Sensitivity analysis was conducted via generation of fuzzy curves, and a neural network model also was developed. A correlation between corrosion current (icorr) and exposure tests (measured in the form of weight and thickness loss) was observed; however, the key outcome of the work is the presentation of a model that captures corrosion rate as a function of environments relevant to (CCS) pipeline, revealing the extent of the threat and the variables of interest.