Comparison of RSM and ANFIS modeling techniques in corrosion inhibition studies of Aspilia Africana leaf extract on mild steel and aluminium metal in acidic medium

• DOI: 10.1016/j.apsadv.2022.100316
• Publication Date: 2022-10-12T09:18:43Z
• Authors: Loveth Nwanneka E...; Paschal Enyinnaya...; Chijioke Elijah Onu; Nonye Jennifer Ohale
• Keywords: Response surface methodology; Genetic algorithm; Industrial electrochemistry; Characterization; TA401-492; 0202 electrical engineering, electronic engineering, information engineering; Hydrochloric acid; Artificial neuro fuzzy inference systems; 02 engineering and technology; Materials of engineering and construction. Mechanics of materials; TP250-261

In this work, the predictive capabilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling aluminum (Al) mild steel corrosion inhibition by Aspilia Africana (A. Africana) were comparatively analyzed. Phytochemical Fourier Transform Infrared Spectroscopy (FTIR) characterization A. leaf extract indicated that inhibitor possessed high value flavonoids, Tannins dominant functional groups necessary for promoting sustainable inhibition. While statistical parameters verified applicability RSM ANFIS techniques Al steel, error indices illustrated dominance (R2 = 0.9917) 0.9905) predicting efficiency respectively. Analysis variance (ANOVA) showed acid concentration (F-value 191.23) was most influential process parameter process, while presented an F-value 160.5 to maintain its superior position among other factors coupled genetic algorithm optimization (ANFIS-GA) validated be 80% at HCl conc. 0.7 M, 0.59 g/L immersion time 6.2 h. Similarly, optimized using RSM-GA 77.3% (HCl 0.5 0.37 4.8 h). Post electrochemical studies demonstrated close agreement with obtained gravimetric technique. Furthermore, polarization acted as a mixed type species.