RSM and Artificial Neural Networking based production optimization of sustainable Cotton bio-lubricant and evaluation of its lubricity & tribological properties

Depletion of mineral reservoirs along with health and environmental concerns have led to a greater focus on bio-lubricants. The purpose this study was analyze optimize the reaction conditions transesterification process for cotton biolubricant synthesis by using Response Surface Methodology (RSM). In RSM, Rotatable central composite design selected examine effect input factors yield bio-lubricant during process. ANOVA analysis showed that temperature most significant factor followed time, pressure catalyst-concentration. Optimum obtained RSM maximum TMP tri-ester (cotton bio-lubricant) about 37.52% were 144 °C temperature, 10 h 25 mbar pressure, 0.8% predicted results successfully validated experimentally artificial neural networking. About 90%–94% seed oil after purification its physiochemical, lubricity tribological properties evaluated found comparable ISO VG-46 SAE-40 lubricant. Hence, cottonseed is potential source industry.