Petrochemical Trace Element Analysis by ICP-OES

Presence of trace metals in the refining process could cause various problems including equipment corrosion, catalyst poisoning, and engine deposition. Combustion of fuels can lead to emission of air contaminants including:

• Lead, arsenic, and iron poison catalysts
• Sodium, which causes superficial fusion on fire brick
• Vanadium compounds, which can cause refractory damage in refinery furnaces
• Mercury, which can contaminate air and threaten human health
• Volatile organometallic compounds, which can contaminate distillates and cause instability of equipment

Some examples of the trace elemental analysis in the petrochemical industry are:

• High volatile naphtha
• Middle distillates kerosene
• Lubricating oil
• Diesel additive pentanol

Along with atomic absorption spectrometry (AAS) and ICP-MS, ICP-OES is commonly used for trace metal analysis. Why use ICP-OES?

• Compared to traditional AAS, ICP-OES has higher sensitivity (GFAA not included), a wide dynamic range, no molecular interferences, and has multi-element detection capability in a single analysis. ICP-OES therefore is advantageous in providing fast analysis and high throughput capability.
• Compared to ICP-MS, ICP-OES has a much greater tolerance for high-matrix samples and is the more economic choice. With the low detection limits at ppb levels, ICP-OES meets the requirement of different petrochemical analysis applications.

ICP-OES presents several challenges when analyzing organic solvent samples

1. Organic sample overload
   Due to vapor pressure and viscosity, organic samples have better transport efficiency to the plasma compared to aqueous samples. Therefore, organic samples tend to “overload” the plasma and cause instability and even failure of the plasma. Organic sample overload can be solved in several ways:
   • Use a V-groove nebulizer and baffled spray chamber to reduce plasma loading and prevent instability. In addition, samples with high volatility (i.e., samples with a vapor pressure above 30mm of mercury at room temperature) can be introduced with a cooled spray chamber to further reduce plasma loading.
   • A less preferable way to reduce a sample’s volatility is to dilute the sample with a less volatile solvent such as xylene or kerosene. However, this may lower the sensitivity.
   • Lastly, a low-flow nebulizer at room temperature can be applied to reduce the plasma loading. This also reduces the system’s sensitivity and takes much more time for sample introduction. For viscous samples, the sensitivity is also decreased because less sample may reach the plasma. To avoid inaccurate quantification all the samples in a given set, they should all have similar physical properties.
2. Carbon emission and deposition
   Carbon emission and deposition in the torch causes interferences with detection of some elements like Na and K, which have emission wavelengths in the visible range. As a result, high background from carbon emission overlaps with the real value for Na and K. These steps can help to solve the challenge of carbon emission and deposition:
   • Understand the sample volatility and chemical properties
   • Use a cooled spray chamber for more volatile organic solvents
   • Set proper plasma conditions by optimizing the nebulizer type and gas flow
   • Add oxygen to the auxiliary gas to minimize background emissions for axial view plasmas
3. Difficult analysis
   The combination of instrument hardware and software needs to make analysis easy. For example, fast element wavelength selection in the software, availability of hardware and reagents for organic solvent sample introduction, fast sample introduction, and data analysis and comparison can all be part of the challenges for operators whose goal is to simplify the process and improve productivity, especially for routine laboratories with thousands of samples per week. To make sure analysis is easy
   • Use flexible hardware options and configurations combined with easy to use software for selection of wavelengths, method development, and data analysis that significantly reduces manual labor and saves operator time.