Prediction of chemical oxygen demand (COD) with total organic carbon (TOC) to eliminate the interferences of high concentration of chloride ion in oilfield wastewaters

ABSTRACT

Chemical oxygen demand (COD) is one of the most commonly used indexes for monitoring water quality. However, the accuracy of the conventional COD test is significantly affected by the high concentration of chloride ion in water samples. In addition, the conventional COD test results in the production of hazardous wastes including mercury and hexavalent chromium. This study proposed the prediction of COD with total organic carbon (TOC) for monitoring high-chloride wastewater by comparing their relationship using simulated wastewaters and actual oilfield wastewaters from two wastewater treatment stations at Xinjiang oilfield, China. The theoretical analysis and experimental results showed that there are significant linear correlations between the COD and TOC in simulated wastewaters and actual oilfield wastewaters. High correlation coefficients (R) of 0.9777 and 0.9710 between COD and TOC were obtained for oilfield wastewaters from Xinjiang 81# and 91# stations, respectively. By using the established conversion models, the COD values in the two groups of mixed oilfield wastewaters from Xinjiang 81# and 91# stations were successfully predicted, with relative errors <±5%. It was demonstrated that the proposed method could be promisingly used as an alternative for the green determination of COD values in high-chloride oilfield wastewaters while eliminating the hazardous wastes and chloride interferences.

KEYWORDS:

• Chemical oxygen demand
• total organic carbon
• high-chlorine wastewater
• chloride interference
• green analytical chemistry

Acknowledgments

The authors would like to acknowledge the financial supports from the Hubei Provincial Natural Science Foundation of China (Grant No. 2018CFB165), the Open Fund of the HSE Key Laboratory of CNPC (Grant No. 2016D-5006-08), and the Doctoral Scientific Research Startup Foundation of Yangtze University (Grant No. 801090010134).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Hubei Provincial Natural Science Foundation of China [2018CFB165]; Open Fund of the HSE Key Laboratory of CNPC [2016D-5006-08]; Doctoral Scientific Research Startup Foundation of Yangtze University [801090010134].