In this interview, industry expert Janam Pandya and Tom Dearing explain how process Raman supports real-time refined fuel analysis, helping refineries improve QC, ASTM-based testing, and in-line property monitoring.
First, can you explain why accurate refined fuel property analysis matters in refinery operations?
Janam Pandya: Accurate refined fuel property analysis is critical because refineries operate in a highly regulated environment. Blended fuels are often subject to strict quality requirements, and product quality must be tested using ASTM-certified test methods to ensure compliance.
There is also a strong economic driver. Refinery margins are often tight, so maintaining tighter specifications can help optimize the blending process and reduce unnecessary costs.
If a fuel product is over-blended or adjusted too conservatively, valuable components may be wasted. If it is under-tested or released too slowly, it can affect yield, product release timelines, and operational efficiency.
This is why the industry is shifting from offline laboratory testing toward in-line continuous quality control. Continuous oversight helps engineers make faster decisions, maintain tighter process control, and optimize product quality in real time.
What are the limitations of traditional refined fuel quality control workflows?
Janam Pandya: A significant amount of quality control still happens in offline QC laboratories, with some measurements performed using online techniques. These traditional workflows create several pain points. Offline analysis is slow and often comes with long turnaround times. It also requires frequent sample collection, which adds labor and operational complexity.
Some instruments used in conventional workflows also have high operating costs. Frequent downtime, heavy maintenance, consumables, carrier gases, and calibration requirements can all increase the total cost of ownership. In addition, variability in some conventional measurement techniques can introduce uncertainty.
The lack of direct in-line monitoring also means that process changes may not be detected quickly enough. This delays adjustments, increases variability, and can contribute to off-spec production. From a business perspective, these delays can lead to slower product release, reduced yields, and higher overall operating costs.
How can in-line spectroscopy help address these challenges?
Janam Pandya: In-line spectroscopy offers a way to address many of the limitations of traditional testing. In-line measurements are taken directly in the process line, delivering immediate data without the need for sample collection or conditioning. This allows engineers to monitor product quality continuously rather than waiting for lab results.
The operational benefits are significant: in-line spectroscopy can reduce the overall cost of operation and maintenance while improving yields. Immediate results help engineers make informed decisions more quickly, supporting tighter process control and reducing off-spec production.
For refined fuels, this is especially valuable because quality specifications are strict and process conditions can change. By moving analysis closer to the process, refineries can improve product consistency, reduce delays, and support more efficient blending and release workflows.
How does Raman spectroscopy work for refinery process monitoring?
Janam Pandya: For refinery process monitoring, fiber-optics-based Raman spectrometers are typically used. In this setup, laser light is transmitted from the instrument to the sample through a fiber-optic cable. The cable is connected to optics that are directly inserted into the fuel line.
When the system is operating, the light interacts with the sample. As the hydrocarbon molecules vibrate, Raman-scattered light travels back through the optics and returns to the instrument through the fiber-optic cables. The instrument reads this scattered light and generates a Raman spectrum.
A chemometric model deployed on the instrument then converts the single spectrum into results for different fuel properties. These results are displayed in real time as a function of time.
This means one Raman measurement can provide fast, quantitative and qualitative information on multiple properties without requiring sample conditioning, consumables, or carrier gases.
What makes gasoline quality control a useful case study?
Janam Pandya: Gasoline is a useful case study because it requires several important analytical tests for quality control. These include research octane number (RON); motor octane number (MON); Reid vapor pressure (RVP); benzene; olefins; aromatics; sulfur; distillation profile; and API gravity or gravity.
In a typical refinery workflow, performing all of these tests can require at least four samples, at least three different analyzers, and around two hours of analyst time. This creates significant operational costs through labor, consumables, equipment maintenance, and potential delays.
For RON and MON analysis, octane testing engines are required. These are offline instruments with large footprints that require sample collection, sample preparation, reference fuels, calibration, and ongoing maintenance. Vapor pressure analyzers are used for RVP testing and may require calibration standards, maintenance, and consumables such as purge gases.
Gas chromatography is used for properties such as simulated distillation, aromatics, benzene, and olefins, but GC systems often require sample conditioning, bypass lines, carrier gases, routine calibration, and extensive maintenance.
In contrast, Raman spectroscopy has the potential to supplement these workflows by measuring multiple fuel properties from a single direct in-line measurement.
How can process Raman be integrated into refinery environments?
Janam Pandya: Process Raman can be integrated into refinery environments through a compact analyzer, process probes, flow cells, and fiber-optic connections.
The MarqMetrix All-In-One Process Raman Analyzer is a compact system that can be mounted near process streams or placed in an analyzer shack. It comes factory-calibrated, requires minimal yearly maintenance, and can integrate with existing process control systems via Modbus or OPC communication protocols.
For direct in-line installations, Process BallProbe accessories can be used with a bore-through flange adapter. These probes are available in various diameters, and the Hastelloy construction material can withstand corrosive and oxidative environments up to 6000 psi and from -250 to 300 °C.
If a sample conditioning system is already in place, a Flow Cell BallProbe can be used for online analysis. Flow Cell BallProbes are available in different metallurgy and flow-path volumes and can withstand up to 9000 psi. High-quality fiber-optic jumpers of up to 100 m also allow flexible installation with minimal signal loss.
For hazardous locations, the analyzer can be ATEX and IECEx-certified depending on the certification. It can be installed in a safe zone with fiber-optic cables running to the hazardous zone, or placed in a properly rated explosion-proof enclosure for Class I, Division 1, Class I, Division 2, or Zone 2 classified locations.
What is chemometrics, and why is it important for process Raman?
Tom Dearing: Chemometrics is a collection of tools and analysis techniques used to convert spectral data into useful information: the goal is to use that information to make decisions. These tools can be used to clean data, build models, and make predictions.
In process Raman applications, models are built using spectra and analytical data collected from the respective measurements being investigated. For refined fuels, this means Raman spectra are matched with reference data collected using the appropriate ASTM methods. Once the model has been built, it can be deployed on the instrument to make predictions in real time.
The word “model” can sound complicated, but in this context, models are simply a series of mathematical steps that convert the spectra into relevant property information. They allow the Raman analyzer to translate a spectrum into values such as RON, MON, RVP, API gravity, flash point, viscosity, or distillation characteristics.
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What real-world refinery data was collected using in-line Raman?
Tom Dearing: In one real-world deployment, approximately 300 samples were collected at a refinery. These samples included various refined fuels across three main groups: gasoline, jet fuel, and diesel. Offline reference data from ASTM methods was also supplied, allowing models to be developed to predict critical fuel properties.
The spectra showed clear differences between gasoline, jet, and diesel samples. Each fuel type exhibited distinct peaks at different locations, allowing them to be distinguished using the Raman analyzer. In some cases, the spectra also showed enough detail to differentiate subtypes, such as gasoline and premium gasoline.
This level of spectral discrimination is important because it supports model development for individual fuel types and specific fuel properties. The models built from this data showed strong correlation between the in-line Raman spectrometer and the corresponding ASTM reference methods.
What properties were measured for gasoline, diesel, jet fuel, base oils, and lubricants?
Tom Dearing: For gasoline, models were developed for properties such as benzene, RON, MON, and RVP. These models demonstrated high accuracy and strong correlation with the relevant ASTM methods.
For diesel and jet fuels, models were developed for important properties including API, cetane, flash point, and simulated distillation values such as T10, T50, and T90. These measurements are important for product release and for applications such as transmix, where multiple products may move through the same pipeline.
For example, API can help distinguish between different fuel types, while flash point can help show the transition between diesel and jet samples.
Base oils and lubricants were also analyzed. Base oils are the primary raw materials used to produce lubricants, so understanding their properties is essential for predicting final lubricant performance.
Samples from API Group II and API Group III base oils were analyzed, and models were built to simulate distillation, viscosity at 40 °C and 100 °C, pour point, volatility, and aromatics. These models showed strong correlation and accuracy compared with the relevant ASTM methods.
How repeatable were the measurements, and how are models deployed in real time?
Tom Dearing: The repeatability results were excellent. Repeatability standards were measured for the refined fuels, as well as for the API Group II and API Group III base oils. Across the models, the standard deviations and repeatability coefficients were very small, showing that the instrument and measurements were highly reproducible.
For the base oil repeatability standards, the standard deviation in many cases was less than one degree Fahrenheit. This level of repeatability gives users confidence that the instrument's values are both accurate and consistent.
For real-time deployment, the calibration data can be collected using SpecWare, the command-and-control software for detailed calibration data collection. The models then live in RunTime, the software package used on the instrument for real-time prediction. RunTime allows users to build methods that include acquisition parameters, settings, and models for predicting the properties of interest.
Once the predictions have been made, the results can be output through standard communication protocols such as OPC UA. Results can also be delivered as a text file for integration into a LIMS system.
This allows in-line Raman data to be used directly within refinery workflows for real-time decision-making, process control, and product quality monitoring.
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About Janam Pandya 
Janam Pandya holds a PhD and Master’s degree in Food Science from the University of Massachusetts Amherst, plus a Bachelor’s in Food Processing Technology.
At Thermo Fisher Scientific, he has worked in chemistry, spectroscopy applications, and product management, now supporting market growth for the process Raman portfolio across market segments.
About Tom Dearing 
Thomas Dearing holds a PhD in Chemometrics, Chemistry and Modeling from the University of Hull. At Thermo Fisher Scientific, he serves as Product Manager and Senior Staff Scientist, applying expertise in chemometrics, spectroscopic data analysis, data fusion, machine learning, and process Raman to support advanced analytical instrumentation and applications development.
This information has been sourced, reviewed, and adapted from materials provided by Thermo Fisher Scientific – Portable and Handheld Process Raman Spectroscopy.
For more information on this source, please visit Thermo Fisher Scientific – Portable and Handheld Process Raman Spectroscopy.
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