A neuro-based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial-and-error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.
The process of hydrocarbons cracking is carried out in the presence of heatresistant alloys Fe-Ni-Cr, which HP40 alloy (25Cr-35Ni) has the most applications among olefin plants. Since these alloys naturally tend to form coke, the industry has always tried to reduce the coke formation by reducing the catalytic properties of the coils. In this research, the effect of dimethyl disulphide (DMDS) concentration (200-900 ppm) on the HP40 alloy of industrial coils at the presulphidation stage is evaluated. In the presulphidation stage, the alloy surface is in contact with sulphur in the absence of hydrocarbons, and this affects the amount of coke formation in the cracking process. Also, the surface composition and morphology of coke are identified using EDX and SEM analysis. These results showed that at the 500 ppm concentration of DMDS, coke deposition is minimized. Additionally, our findings indicated that coke morphology has not changed under different presulphidation conditions, and coke is still a filament type, but the size of the filaments has changed. Moreover, the study of HP40 composition in both preoxidized and presulphide stages shows that presulphidation reduces the amount of Fe and Ni in the coke layer significantly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.