Determination of Oil Content in Oily Sludge Based on Extraction and Smartphone Image Colorimetry

doi:10.12422/j.issn.1006-396X.2023.04.002

Abstract

Abstract:

The traditional method for determining the oil content of oily sludge was improved, and the extraction smartphone image colorimetry was established by combining the image colorimetry with the pretreatment method of hot solvent extraction. The effects of RGB value, different color channels, shutter time, sensitivity, white balance and other parameters on the determination of oil content were investigated. Under the premise that the light source was tungsten iodide lamp and the front camera of Samsung A8s mobile phone was used, the best test conditions were finally determined: white balance value was 2 800 K, sensitivity value was 100, shutter time was 1 / 2 000 s. The linear correlation coefficients of the method under the optimal test conditions were more than 0.99 under the optimal test conditions, and the linear range was similar to that of the spectrophotometric method, with the detection limit of 0.06% and the lower limit of 0.24%. The recoveries of 8 groups of parallel standard samples were 96%~103%, and the relative standard deviation was 2.26%, which indicated that the accuracy of this method was relatively high. Compared with the traditional method, this method is more simple and rapid, and its accuracy can meet the needs of conventional oil content measurement, which is expected to realise the rapid on?site determination of oil content of large?scale oil?containing sludge in plants.

Key words: Oily sludge, Picture colorimetry, Digital image, Quantitative determination

摘要：

对传统含油污泥含油率测定方法进行改进，将图片比色法与热溶剂抽提的前处理方法相结合，建立了抽提萃取?智能手机图片比色法，并考察了RGB颜色通道、曝光时间、感光度、白平衡值等参数对含油率测定结果的影响。以碘钨灯为光源，使用三星A8s手机前置摄像头拍摄，确定了最佳测试条件：曝光时间为1/2 000 s，感光度为100，白平衡值为2 800 K。在最佳测试条件下该方法的线性相关系数大于0.99，线性范围与分光光度法相近，检出限为0.06%，测定下限为0.24%。8组平行标准样品测试的加标回收率为96%~103%，相对标准偏差为2.26%，说明该方法准确度相对较高。该方法比传统含油污泥含油率测定方法更加简单快速，准确度满足常规含油率测定需求，有望实现工厂大规模含油污泥含油率的现场快速测定。

关键词: 含油污泥, 图片比色法, 数字图像, 定量测定

CLC Number:

TQ09

Enlin Feng, Xian Wu, Weijie Zou, Lihua Li. Determination of Oil Content in Oily Sludge Based on Extraction and Smartphone Image Colorimetry[J]. Journal of Petrochemical Universities, 2023, 36(4): 13-19.

冯恩临, 吴限, 邹尉婕, 李丽华. 基于抽提萃取⁃智能手机图片比色法的含油污泥含油率测定[J]. 石油化工高等学校学报, 2023, 36(4): 13-19.

Figures/Tables 6

Fig.1 Relationship between gray value of color channel and oil content

Fig.2 Relationship between R，G，B channel gray value and oil content when shutter time is 1 / 4 000,1 / 2 000 and 1 / 1 000 s

Fig.3 Relationship between R，G，B channel gray value and oil content of samples with sensitivity of 50, 100 and 200

Fig.4 Relationship between gray value and oil content of R，G，B channel under 2 800, 4 000, 5 000 and 6 500 K white balance values

Table 1 Experimental data of recovery rate of oil samples

样品编号	G通道灰度值		含油率/%		加标回收率/%
样品编号	加标前	加标后	加标前	加标后	加标回收率/%
1	217.34	73.17	1.63	7.85	103
2	214.89	77.51	1.73	7.66	98
3	215.15	79.51	1.72	7.58	97
4	215.75	81.34	1.70	7.50	96
5	214.89	75.68	1.73	7.74	99
6	215.43	74.94	1.71	7.77	100
7	216.43	74.99	1.67	7.85	101
8	216.53	75.40	1.66	7.75	101

Table 2 Comparison of oil content results of actual samples tested by different methods

样品编号	含油率/%
样品编号	抽提重量法	抽提分光光度法	抽提萃取⁃智能手机图片比色法
1	9.17	3.56	3.23
2	8.72	2.33	3.65
3	9.34	3.03	3.62
4	7.34	2.13	2.40
5	6.73	2.51	2.55
6	6.51	2.56	2.41
7	5.35	1.19	1.29
8	5.32	1.64	1.42
9	4.10	1.84	1.53
10	3.20	1.79	0.41

References 22

1	国家环境保护总局.GB l8598—2001 危险废物填埋污染控制标准［S］.北京：国家环境保护总局，2001.
2	国家环境保护总局.GB 18484—2001 危险废物焚烧污染控制标准［S］.北京：国家环境保护总局，2001.
3	国家环境保护总局.GB 18597—2001 危险废物贮存污染控制标准［S］.北京：国家环境保护总局，2001.
4	中华人民共和国城乡建设环境保护部.GB 4284—1984 农用污泥中污染物控制标准［S］.北京：国家市场监督管理总局，1984.
5	薛广海，李强，刘庆，等.当前国内外含油污泥处理标准及石油烃检测方法的深度剖析和对比［J］. 石油化工应用， 2019， 38（1）：1⁃6.
	Xue G H， Li Q， Liu Q， et al. In⁃depth analysis and comparison on the standards and testing methods for oil contaminated soil of domestic and international［J］. Petrochemical Applications， 2019， 38（1）：1⁃6.
6	张雷，梁玉艳，王志勇，等.油田含油污泥物性分析［J］. 环境科学与管理， 2011， 36（12）：124⁃127.
	Zhang L， Liang Y Y， Wang Z Y， et al. Properties of oily sludge in oilfield［J］. Environmental Science and Management， 2011， 36 （12）： 124⁃127.
7	中国石油天然气股份有限公司辽河油田分公司.Q/SY LH 0533—2016 油田油泥含油、含水、及杂质测定方法［S］．盘锦：中国石油天然气股份有限公司辽河油田分公司， 2016.
8	Kaoutit H E， Estévez P， García F C， et al. Sub⁃ppm quantification of Hg（Ⅱ） in aqueous media using both the naked eye and digital information from pictures of a colorimetric sensory polymer membrane taken with the digital camera of a conventional mobile phone［J］. Analytical Methods， 2012， 5（1）：54⁃58.
9	Firdaus M L， Aprian A， Meileza N， et al. Smartphone coupled with a paper⁃based colorimetric device for sensitive and portable mercury ion sensing［J］.Chemosensors，2019，7（2）：3⁃5.
10	Wongthanyakram J， Masawat P. Rapid low⁃cost determination of lead（Ⅱ） in cassava by an ipod⁃based digital imaging colorimeter［J］. Analytical Letters， 2019，52（3）：550⁃561.
11	Pohanka M， Zakova J， Sedlacek I. Digital camera⁃based lipase biosensor for the determination of paraoxon［J］. Sensors and Actuators， 2018， 273（11）：610⁃615.
12	Wang Y， Zeinhom M， Yang M， et al. A 3D⁃printed， portable， optical⁃sensing platform for smartphones capable of detecting the herbicide 2，4⁃dichlorophenoxyacetic acid［J］. Analytical Chemistry， 2017， 89（17）： 9339⁃9346.
13	Sicard C， Glen C， Aubie B， et al. Tools for water quality monitoring and mapping using paper⁃based sensors and cell phones［J］. Water Research， 2015， 70（3）：360⁃369.
14	Guo J， Wong J， Cui C， et al. A smartphone⁃readable barcode assay for the detection and quantitation of pesticide residues［J］. Analyst， 2015， 14（3）：15⁃18.
15	Masawat P， Harfield A， Namwong A. An iPhone⁃based digital image colorimeter for detecting tetracycline in milk［J］. Food Chemistry， 2015， 184（8）：23⁃29.
16	Urapen R， Masawat P. Novel method for the determination of tetracycline antibiotics in bovine milk based on digital⁃image⁃based colorimetry［J］. International Dairy Journal， 2015， 44（1）：1⁃5.
17	Lin B， Yu Y， Cao Y， et al. Point⁃of⁃care testing for streptomycin based on aptamer recognizing and digital image colorimetry by smartphone［J］. Biosensors & Bioelectronics， 2017， 100（1）：482⁃489.
18	Porto I， Neto J， Santos L， et al. Determination of ascorbic acid in natural fruit juices using digital image colorimetry［J］. Microchemical Journal， 2019， 149（10）：3⁃4.
19	Franco M， Suarez W T， Santos V. Digital image method smartphone⁃based for furfural determination in sugarcane spirits［J］. Food Analytical Methods， 2016，33（3）：1⁃8.
20	王嘉辉，吴限，李丽华，等.图像RGB数值衍生的欧氏距离用于手机比色法检测石油类物质［J］.分析测试学报，2020，39（7）：881⁃886.
	Wang J H， Wu X， Li L H， et al. Determination of petroleum substances by mobile phone colorimetry based on euclidean distance from image ＲGB value［J］. Journal of Analytical and Testing， 2020，39 （7）： 881⁃886.
21	陈红，唐立模，陈珺，等. 基于RGB图像的泥沙颗粒粒径测试技术研究［J］. 泥沙研究， 2015（1）：25⁃29.
	Chen H， Tang L M， Chen J， et al. Study on measurement technique of sediment particle size base on ＲGB image［J］. Sediment research， 2015（1）： 25⁃29.
22	张云丽，韩宪忠，王克俭. 基于深度颜色特征的灰度直方图玉米品种识别研究［J］. 作物杂志， 2015（1）：156⁃159.
	Zhang Y L， Han X Z， Wang K J. Study on corn variety identification based on depth and color features of gray histogram［J］. Journal of Crop， 2015（1）： 156⁃159.

[1]	Rui Cao, Dongyun Han, Zubin Cao. Study on the Solidification and Forming Mechanism of Oily Sludge [J]. Journal of Petrochemical Universities, 2022, 35(2): 9-14.
[2]	Yan Song,Shao Luhua, Cai Lingyun,et al. Centrifugal Treatment of DeOiled Sludge of Oilfield [J]. Journal of Petrochemical Universities, 2015, 28(4): 27-30.
[3]	GUAN Yue-ming,ZHANG Zhong-zhi,ZHANG Wei-mu,et al. Oily Sludge Degradation by Land-Farming With a Commercial Inocul [J]. Journal of Petrochemical Universities, 2010, 23(4): 44-47.