Energies, Vol. 18, Pages 6047: The Complete Chain Management of Organochlorine in Crude Oil: Sources, Detection, Removal, and Low-Carbon Risk Control Strategies

Energies, Vol. 18, Pages 6047: The Complete Chain Management of Organochlorine in Crude Oil: Sources, Detection, Removal, and Low-Carbon Risk Control Strategies

Energies doi: 10.3390/en18226047

Authors:
Zhihua Chen
Weidong Liu
Yong Shu
Qiang Chen
Keqiang Wei

Organic chlorine (Org-Cl) in crude oil poses continuous operational and environmental risks during production, trading, and refining processes. This article reviews the management of Org-Cl from its origin assumptions to analysis and mitigation measures and proposes a practical closed-loop framework. Quantitative merit value indicators (typical detection limit/quantitative limit, accuracy, and repeatability) and greenness indicators are used to compare standard methods and advanced methods, and to guide the selection of applicable methods. Corresponding technical maturity levels (TRLs) are assigned to mitigation measures (protective beds/adsorption, HDC, and emerging electrochemical/photochemical routes). Technical economic indicators with reference values (relative capital expenditure/operating expenditure levels) are summarized to assist decision-making. The main findings are as follows: (i) Evidence of secondary formation of organic chlorine under distillation-related conditions still relies on the matrix and requires independent verification; (ii) MWDXRF can achieve rapid screening (usually only 5 to 10 min), while CIC/D5808 supports quality balance arbitration; (iii) adsorption can remove a considerable portion of organic chlorine in light fractions under laboratory conditions, while the survival ability of HDC related to crude oil depends on the durability of the catalyst and the tail gas treatment capacity; and (iv) minimum viable implementation (MVI) combined with online total-chlorine monitoring and a physical principle-based digital twin technology can provide auditable closed-loop control. The limitations of this review include partial reliance on laboratory-scale data, inconsistent reports among studies, and the lack of standardized public datasets for model benchmarking. Prioritization should be given to analysis quality control, process durability indicators, and data governance to achieve reliable digital deployment.