Utilization of acid tars

Frolov, A. F.; Denisova, T. L.; Aminov, A. N.

doi:10.1007/bf00719343

Cited by 2 publications

(2 citation statements)

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“…The bulk density results are within the range reported in other works (e.g. [4,6,33]). The relatively high density of acid tar has been attributed in part to its sulfuric acid content (density 1.96 g/cm 3 ) [13].…”

Section: Physical Propertiessupporting

confidence: 86%

Characterization of acid tars

Leonard

Stegemann

Roy

2010

Journal of Hazardous Materials

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Section: Physical Propertiessupporting

confidence: 86%

Characterization of acid tars

Leonard

Stegemann

Roy

2010

Journal of Hazardous Materials

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“…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%

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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Utilization of acid tars

Cited by 2 publications

References 1 publication

Characterization of acid tars

Characterization of acid tars

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

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