Analytical instrumentation
Petroleum laboratories are testing for product quality and safety for transport through physical testing.
However, analyzing specific petrochemical qualities in laboratories requires resources that increase costs and harm the environment due to high energy consumption, repeated experimental trials, and the use of consumables.
This paper proclaims that a digital twin is an alternative solution to testing petroleum products.
Through continuous data iterations in a virtual simulation, a digital twin facilitates early detection of process and deviation, preventing redundancy in experiments.
Since a digital twin is energy-efficient, it can benefit the environment and operational costs.
Overall, a digital twin provides a strong framework that reduces expenses and sustains the environment.
The petroleum industry is striving for a cleaner environment.
Although petroleum is a nonrenewable resource, it is widely used globally.
For example, petroleum is essential for transportation and generating electricity and heat.
However, petroleum releases two greenhouse gases, carbon dioxide (CO2) and methane (CH4), which harm the environment’s air, contributing to climate change.
Environmentalists are figuring out ways to minimize the usage of CH4 and CO2 when testing petroleum products, since it is essential to reduce oil spills that can contaminate the environment [1].
Most oil industries use physical testing through manufacturing instruments to test whether oil is being extracted and transported safely.
However, companies must spend more money on resources to manufacture instruments, and these instruments end up becoming waste for the environment.
An effective alternative would be a digital test for dealing with petrochemicals. This results in the solution of using digital twin instrumentation.
Digital twin instrumentation is an emerging technology used in laboratories, first invented in Apollo 13 from NASA.
However, it was a failed experiment, but it was later repurposed for success in the Mars Rover exploration [1].
Currently, digital twins are emerging in the petroleum industry since physical testing can cause difficulty in testing petroleum.
Digital twin enables early detection of processing errors in petroleum laboratories to optimize the perfect product for petrochemicals in a way that has a lower operational cost and more sustainable compared to physical testing models [1].
Overall, digital twin instrumentation is a better alternative solution for testing petroleum compared to physical testing.
Physical petroleum instrumentation has a major restraint that digital twins lack: it is more expensive.
For example, using hydrocarbon petroleum fluids, gas chromatography is required to test the manufacturing process.
GC-MS is a type of gas chromatography that is an expensive method and requires tandem instruments and autosamplers to ensure data reproducibility which features column carrier gases and large electricity consumption [2,3].
A large amount of consumption and the use of a multitude of resources create long-term costs, as there are high electricity demand and routine maintenance of tandem instruments and autosamplers.
In contrast, a digital twin does not inherently ostracize the demand for physical testing.
However, this advantage ensures the frequency and redundancy.
Digital instrumentation is cost-reducing because it lowers the consumption of resources and high-demanding energy.
It provides a digital simulation for petroleum.
From a cost perspective, the proposed digital twin features a digital thread component which must be updated initially and then continuously through its lifecycle since it allows the system models to indicate the initial manufacturing or as-manufactured deviations from the design intent after physical testing has been processed [4].
Continuous virtual trials detect inefficiencies early and process the discrepancies that are evaluated in the testing process.
This results in fewer expenses due to no iterative trial and error process that physical testing requires.
Similarly, the economic impact becomes prominent when considering the limitations of conventional digital prototypes.
For example, the utilized product model validation method consists of using existing data of tool machining milling time and remaining machining times for unused production of tool life; for machining processes with big data conditions the model allows a way to predict the usage performance of products and equipment [4].
Early data prediction provides a cost-effective approach because it enables maintenance and usage performance to be improved.
Similarly, a digital twin for fault monitoring also follows a data-driven method that predicts the product, ensuring the dependability and fault tolerance of many systems – specifically in photovoltaic ones – which use mathematical analysis, simulation study, and experimental validation [5].
This allows for a more cost-effective approach because early detection of petroleum does not require replacing parts, performing maintenance, or implementing abrupt shutdowns.
This essentially lowers long-term operational expenses since it is a shorter process.
For example, AAS (Asset Administration Shell) is a petrochemical tool that is being studied by a digital twin.
AAS are element instances which can utilize multiple data specification templates through the HasDataSpecification relationship, providing the flexibility to meet complex data requirements [5].
It has also embedded data specifications composed of pairs that include an external global reference to a data specification along with the embedded content of that specification within the AAS [5].
AAS framework for digital twin is very cost-effective because it minimizes the complexity, which reduces the long-term expense.
Clearly, using digital twin models can provide a lower operational cost like AAS, which allows a more efficient process.
Digital twin frameworks display environmental advantages by providing simulation, early anomaly detection, and monitoring in the petroleum industry.
For example, the control buttons for controlling the cantilever of the road header are designed in the virtual model to realize the simulation of the coal mining site within the virtual environment, which provides a good solution for early warning prevention of special operations and virtual equipment monitoring in special environments [4,6]
Converting to petroleum manufacturing, digital twin can verify hazardous conditions within testing petroleum which can prevent environmental contamination.
Furthermore, a second environmental advantage is simulating raw data with virtual models.
The digital twin depends on dynamic data of the operating state of the real physical system monitored by sensors in real time while the virtual model can generate auxiliary data through simulation [6].
These data showcase the difference in connections of the real physical system.
This represents the association rules between systems, including the connection between physical system and database, the connection between the physical system and virtual model, the connection between the physical system and service system, connections between virtual models and databases, connections between virtual models and service systems, and connections between service systems and databases [4].
This allows the digital twin to detect discrepancies and abnormalities early on due to the integrated monitoring.
As a result, it prevents environmental hazards and increases efficiency when testing virtually.
Moreover, digital twins can be utilized for petrochemicals that impose a high environmental risk.
For instance, in Figure 1, the structure of the prepared AAS represents the digital twin of the ethylene oxide/ethylene glycol (EO/EG) plant
EO recovery unit columns.
The recovery stripping unit is a process unit used to recover EO absorbed in the liquid phase by heating and vaporizing it into the gas phase [7].
In Figure 1, the digital twin of AAS allows safety measures during ethylene sterilization and processes by simulating stripping conditions and recovery before transferring to Figure 2, which is the physical process of constructing AAS [7].
This way, it reduces ethylene contamination in the air, which enhances air quality and performs safer petrochemical practices.
In addition, environmental benefits also apply to CO2 pipeline transport systems when using a digital twin.
For example, A CO2 pipeline transport simulator flow loop can be used for initial model development and debugging enabling evaluation of the system behavior through a simulation before transferring to the physical aspect from Figure 3 into Figure 4 [8].
The model imports streaming data from temperature sensors, vibration sensors, pressure sensors and other subsystems, whereas the digital twin provides constant monitoring of the pipeline transport system [8].
The digital thread component of the proposed digital twins must be updated initially and then continuously, according to Figure 3 [8].
The digital twin requires an initial manufacturing or as-manufactured deviations from the design intent [8].
This incorporates early detection of anomalies that lead to CO2 leakage or spills.
As a result, it reduces the risk of carbon contamination in the atmosphere, which provides a sustainable environment.
Even though it is argued to be relatively better in operating tests for petroleum products, there are still flaws within digital twin instrumentation.
One challenge that is discovered in digital twins is that there are often synchronization issues and model fidelity issues.
For instance, the intrinsic randomness in the physical processes may cause deviations of the digital twin from the real system trajectory [9].
Specific deviations are prominent in petroleum products, which can negatively impact the model accuracy for petroleum.
And these discrepancies can complicate the decision-making process because the prescription update problem has extensively been discussed in the decision-making literature.
In the context of decisions assisted by digital twins, a prescription has to be considered as a decision [9].
For example, the action to update a decision must be evaluated together with the decision of re-obtaining a new prediction because the last can decrease the error and possibly improve the decision since improved predictions can increase expenses [9].
Moreover, there was also a study conducted about the problems that affect digital twins.
According to Figure 5, most people (30% of respondents) voted the most common problem as the high costs of technology implementation including the addition of new devices, preparation of appropriate mathematical models, the complexity of the process of modelling the real object), the adaptation of the infrastructure, personnel costs (hiring new employees), and subsequent costs of repairing errors resulting from the inaccurate operation of the solution [10].
Figure 5 highlights a high implementation cost as the primary challenge faced with digital twins.
For a smaller petroleum laboratory, a digital twin can be restricted with the short-term accessibility.
Consequently, Figure 5 represents the initial implementation cost and does not impact long-term usage since it can nullify over the course of time.
Clearly, there are flaws regarding digital twin models however, it can be resolved over long period of time.
To sum up, a digital twin is shown to be an effective approach when it comes to processing petroleum/petrochemical products in laboratories.
Although physical instrumentation is a significant tool to test petroleum products, it requires a lot of energy consumption, reliance on resources, and redundancy in testing.
Digital models resolve these restrictions by enabling virtual simulation, fault monitoring, and early detection that can prevent safety and environmental risks without spending a lot of money.
However, there is a high initial implementation expense and synchronization issues involved in digital twin models, it will nullify over time.
Overall, digital twin provides a sustainable way to test petrochemical/petroleum products in laboratories and this will improve the long-term impact on the economy and environment.
Dr Raj Shah is a Director at Koehler Instrument Company in Holtsville, New York, where he has served for over three decades, contributing to the advancement of petroleum, fuels, lubricants, and analytical instrumentation technologies worldwide.
Over the course of his distinguished career in the energy and chemical engineering industries, he has become widely recognized for both his technical leadership and sustained service to global professional societies.
Dr. Shah is an elected Fellow by his peers at ASTM International, the Institute of Chemical Engineers (IChemE), the Chartered Management Institute (CMI), the Society of Tribologists and Lubrication Engineers (STLE), the American Institute of Chemists (AIC), the National Lubricating Grease Institute (NLGI), the Institute of Measurement and Control (InstMC), the American Oil Chemists’ Society (AOCS), the Institute of Physics (IOP), The Energy Institute (EI), and The Royal Society of Chemistry (RSC).
These fellowships reflect his multidisciplinary impact across chemical engineering, tribology, measurement science, energy technology, and applied chemistry. He is also the recipient of the prestigious ASTM Eagle Award and ASTM’s highest honor, the Award of Merit (Fellow), recognizing more than 30 years of leadership and contribution to Committee D02 on Petroleum Products, Liquid Fuels, and Lubricants.
He recently co-edited the bestseller, Fuels and Lubricants Handbook: Technology, Performance, Properties, and Testing, a major reference work for the industry.
Dr. Shah has now authored and co-authored over 750 technical publications, conference papers, and industry articles, and continues to be an active contributor to the scientific and engineering literature.
Further information regarding his work and recognitions can be found at https://shorturl.at/I7000.
Dr. Shah earned his doctorate in Chemical Engineering from The Pennsylvania State University and is a Fellow of The Chartered Management Institute, London.
He is a Chartered Scientist (CSci) with the Science Council, a Chartered Chemist (CChem) with the Royal Society of Chemistry, a Chartered Engineer (CEng) with the Engineering Council, UK, and a Chartered Petroleum Engineer (CPEng) with the Energy Institute. He was recently granted the honorific distinction of “Eminent Engineer” by Tau Beta Pi, the oldest and largest engineering honor society in the United States, an honor reserved for engineers demonstrating exceptional professional achievement and character.
Actively engaged in academia and mentorship, Dr. Shah serves on the Advisory Boards of Farmingdale State College (Mechanical Technology and Engineering Management), Auburn University (Tribology and Lubrication Science), and the State University of New York at Stony Brook (Chemical Engineering and Materials Science & Engineering).
He is also an Adjunct Professor in the Department of Materials Science and Chemical Engineering at Stony Brook University.
Throughout his career, he has remained deeply committed to advancing engineering education, standards development, and technical excellence within the global energy community.
More information on Dr. Shah can be found at https://shorturl.at/yYl85.
Zara Rahman is part of a thriving internship program at Koehler Instrument Company in Holtsville, NY working closely with Dr. Raj Shah.
Gavin Thomas is part of a thriving internship program at Koehler Instrument Company in Holtsville, NY and is a recent graduate of the Chemical and Molecular Engineering program at Stony Brook University.
He also works as a process engineer at Mill-Max in Oyster Bay, NY where he becomes hands-on with various production processes to ultimately improve safety, efficiency, and cost-effectiveness.
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[7] Kaya, F., Ezgi Şanlı, Özlem Albayrak, Perin Ünal, & Pinar Kirci. (2025). Asset Administration Shell Tool Comparison: A Case Study with Real Digital Twins Used in Petrochemical Industry. Sensors, 25(7), 1978–1978. https://doi.org/10.3390/s25071978
[8] Sleiti, A. K., Al-Ammari, W. A., Vesely, L., & Kapat, J. S. (2022). Carbon Dioxide Transport Pipeline Systems: Overview of Technical Characteristics, Safety, Integrity and Cost, and Potential Application of Digital Twin. Journal of Energy Resources Technology, 144(9). https://doi.org/10.1115/1.4053348
[9] Tan, B., & Matta, A. (2023). The digital twin synchronization problem: Framework, formulations, and analysis. IISE Transactions, 56(6), 652–665. https://doi.org/10.1080/24725854.2023.2253869
[10] Gulewicz, M. (2022). Digital twin technology — awareness, implementation problems and benefits. Engineering Management in Production and Services, 14(1), 63–77. https://doi.org/10.2478/emj-2022-0006
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