Group analysis of acid tars

Kolmakov, G. A.; Занозина, В. Ф.; Khmeleva, M. V.; Okhlopkov, A. S.; Гришин, Д. Ф.; Зорин, А. Д.

doi:10.1134/s0965544106010038

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…In another work [20], it was reported that fresh acid tars contains mainly sulfuric and sulfonic acid, while acid tars that have been dumped for several years contained predominantly weak carboxylic acids. A group analysis of acid tars [17] using various stages of solvent extraction and column chromatography also revealed that the studied sample contained considerable amounts of asphaltenes, carbenes, carboids, and hydrocarbons, and low amounts of resins and other impurities. The hydrocarbon fraction was further separated into alkylbenzene, PAHs, and naphtheno-aromatic hydrocarbons; however, the individual compounds in these fractions were not characterized.…”

Section: Composition Of Acid Tarsmentioning

confidence: 96%

Characterization of acid tars

Leonard

Stegemann

Roy

2010

Journal of Hazardous Materials

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Section: Composition Of Acid Tarsmentioning

confidence: 96%

Characterization of acid tars

Leonard

Stegemann

Roy

2010

Journal of Hazardous Materials

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“…Common eluents used are pentane, benzene, ethyl acetate and methanol to end up with aliphatic, aromatics, esters and polar organic compounds [15]. Hexane, benzene and ethanol are solvents which are used to extract different groups of organic compounds from acid tar so that the analysis become easy since different groups differ in their solubility for every solvent [16].…”

Section: Techniques For Characterising Acid Tarsmentioning

confidence: 99%

Characterization and utilization of acid tar waste from crude benzol processing for environmental sustainability

Danha¹,

Chihobo²,

Musademba³

et al. 2014

IOSRJESTFT

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This paper reviews the utilization of acid tars with the overall aim of reducing or eradicating the environmental problems associated with acid tar landfill disposal at a local crude benzol refinery in Zimbabwe .The review focused on disposal, characterization, composition and utilization of acid tars. Peer reviewed journal and conference papers from 1980 to the current year were considered. The papers reviewed were mainly from petroleum processing since the acidic nature of the tar can be assumed to be similar to that of crude benzol processing and the databases used were Science direct, Hinari and Agora .The effects of the disposal of acid tar in lagoon were reviewed. The utilisation of acid tar waste is dependent on the chemical composition hence work on the characterization of acid tars was reviewed. The use G.C-MS was common in the characterization of acid tars as compared to other analytical techniques. Some of the utilization processes which were reviewed included among others the conversion of acid tar to asphaltenes, production of pavement binders, energy utilisation, acid tar co-processing, membrane technology, ion exchangers and carbon adsorbents technique. The utilization of acid tar waste hinges on effective treatment methods owing to its complex chemical composition.

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“…The composition of the acid tars stored in the lagoons varies with the period of production, the product treated with sulfuric acid, and the age of the tars [12][13][14][15]. Acid tars have a variable composition between sites and even within one lagoon [15].…”

Section: Introductionmentioning

confidence: 99%

“…Milne [3], Frolov et al [12], Nieuwenhuis [17], and especially Kolmakov et al [5,13] and other [18,19] reviewed various methods for the processing of acid tars into useful products but concluded that none of the approaches are satisfactory and thus, it is necessary to apply an effective remediation method of acid tars lagoons [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…The characterization of untreated and treated acid tars in each site is conventionally accomplished through expensive sampling and testing of numerous borehole samples using highly sensitive and specific analytical procedures involving extraction and subsequent gravimetric or chromatographic techniques [4,13,28]. These procedures are laborious, expensive, time-consuming, and inadequate when high spatial and temporal resolutions of petroleum hydrocarbon content are required [15].…”

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confidence: 99%

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Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning

Tita,

Onutu,

Doicin

2024

Applied Sciences

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Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons—TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R2 correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.

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Group analysis of acid tars

Cited by 5 publications

References 3 publications

Characterization of acid tars

Characterization of acid tars

Characterization and utilization of acid tar waste from crude benzol processing for environmental sustainability

Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning

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