September 2021

Special Focus: Refining Technology

Enterprise-wide energy efficiency fleet monitoring tool

In response to challenges related to global warming, climate change and greenhouse gas emissions, numerous countries are adopting regulatory measures requiring industry to further improve energy efficiency.

Trivedi, K., ExxonMobil Global Projects; Nehete, S., ExxonMobil Chemical Operations Pvt. Ltd.; Gunter, J., ExxonMobil Fuels & Lubricants Co.

In response to challenges related to global warming, climate change and greenhouse gas emissions, numerous countries are adopting regulatory measures requiring industry to further improve energy efficiency. The authors’ company has a long tradition of effectively improving energy efficiency and mitigating emissions, both for internal operational efficiency and to support external drivers within the industry—such as the API Compendium challenge—as well as managing the risk of climate change through emissions reduction goals for 2025, which are projected to be consistent with the goals of the Paris Agreement. The company continues to invest in lower-emissions technologies, such as carbon capture and advanced biofuels, which are necessary for society to achieve its ambition for net zero emissions by 2050.1,2

Over several decades, substantial work has been done from an energy management perspective. This has enabled the company to become one of the most energy efficient international refining companies in the world and one of the most energy efficient refining companies operating in the U.S. The company has achieved a 10% improvement in energy efficiency across its global refining and chemicals operations following an effort launched in 20003, showcasing a robust set of processes to improve energy efficiency and mitigate emissions, including programs focused on reducing methane emissions, flaring and venting. These processes include, where appropriate, setting tailored objectives at the business, site and equipment level, and then stewarding progress toward meeting those objectives. This rigorous approach is effective to promote efficiencies and reduce greenhouse gas emissions in operations, while striving to achieve industry-leading performance.4

However, with regulations to manage climate change risk emerging around the world, it is becoming increasingly important to develop smart ways to identify further energy efficiency improvement opportunities in existing facilities. Such initiatives support the company’s aim for industry-leading greenhouse gas performance across its businesses by 2030.1

Energy fleets, such as fired heaters and boilers, are the primary energy consumers for refineries and petrochemical facilities. Overall facility energy efficiency improves with the improved energy performance of energy fleets. Therefore, it is important to routinely track the performance of energy-consuming equipment. This helps to identify maintenance requirements in a timely manner and improve energy efficiency.

Need for enterprise-wide monitoring tools

For an organization like the authors’ company, plant performance monitoring activities consume significant resources. For example, the global fleet of fired heaters within the authors’ company exceeds 700. Adding other equipment that impact the energy footprint to the fleet will result in thousands of pieces of equipment that must be monitored. It is important to deploy smart and efficient digital solutions to minimize resource requirements without affecting the quality of the monitoring. In fact, digital monitoring has helped the company to increase its overall productivity by replacing closed site level monitoring systems with open, interoperable systems. This has resulted in easier information sharing among relevant stakeholders. Such a transformation makes engineers more creative in managing fleets and unlocks many unidentified opportunities. With improved transparency, such transformation facilitates the process to monetize and prioritize opportunities at the corporate level.

Another major advantage of such an approach is that it helps to deploy a consistent process as uniform engineering toolsets are used across the entire fleet family. This also results in creating an internal benchmarking database that can be used to monetize and prioritize opportunities on a global scale.

Additionally, since an entire fleet can be on one platform, individual fleet performance can be easily rolled to corporate level key performance indicators (KPIs) that can be tracked over the period. Having such KPIs helps the corporation to develop a strategy around its energy performance.

Fleet performance presented on one visualization platform also acts as a “social proof” and psychologically motivates people to match the best demonstrated behavior. The concept of social proof is widely discussed in psychology. Cialdini5 coined the term to represent a phenomenon wherein people copy the actions of others to undertake behavior in a given situation. A classic example of decreasing household energy use would be to not only provide the individual’s electric consumption but also the electricity consumed by the neighbors.

Providing a common platform for sharing data will promote a healthy competitive environment and motivate all to make energy efficiency improvements.


The entire fleet development work process can be broken down into eight major steps, as shown in FIG. 1. The entire work process begins with the identification of the critical energy fleet population, followed by the development of a technical functional specification that acts as a backbone for the entire tool development process. This considers defining key energy performance indicators, establishing calculation methodologies, target setting and energy gap calculations.

FIG. 1. Elements of a fleet development framework.

Once tool mechanics are developed, it is important to arrange the millions of data points generated by the tool into visuals for easier interpretation. The last and most critical step in achieving success is to gain the trust of business partners and embed the tool into normal work processes. This sets up the tool for long-term success.


Configuring thousands of pieces of equipment into the fleet tool is resource intensive. So, for every fleet family, it is important to identify the critical fleet population based on the Pareto Principal.6 It is often a good idea to assign a priority for every piece of equipment to maintain a right balance between the upfront resource required and the benefits obtained by monitoring. This approach (FIG. 2) enables an organization to focus on the critical equipment first, followed by low-priority equipment when more resources are available.

FIG. 2. Determining initial fleet population.

One simplified approach to define priority is to sort the fleet in the descending order of energy consumption. A threshold energy consumption allows the identification of high-priority items that must be configured during an initial rollout. Minor modifications to the list are often needed depending upon equipment criticality from the process unit perspective. For example, smaller furnaces like the naphtha hydrotreater feed furnace may not be qualified based on the threshold value; however, depending on its criticality in a process unit or a site, an owner-operator may want to include it in the initial rollout.

Define KPI

This step includes the development of the technical approach defining KPIs, universally consistent estimation methodologies and target setting.

KPIs can vary depending on the family of fleet. For example, energy efficiency can serve as a KPI for equipment like furnaces, boilers and gas turbines. Different KPIs, such as thermodynamic efficiency for compressors or potential power that can be generated through steam letdown, etc., can be developed.

Once KPIs are determined, it is important to establish globally consistent methodologies to estimate the same, and to find a balance between accuracy vs. complexity of the techniques to quantify the KPI. Methods that are too complex and more accurate are usually resource intensive, as they require numerous process inputs and make the tool more complicated. Often, a literature survey or in-house research can provide such consistent techniques. For example, furnace efficiency for gas-fired furnaces can be estimated based on stack conditions, such as oxygen concentration and temperature7 (Eqs. 1 and 2):

η = 99 – (0.001123 + 0.0216 EA) / (TstackTa)                            (1)

EA = (0.98 × 21) / (21– O2stack)                                                   (2)


Tstack and Ta = Flue gas temperature measured at stack and ambient temperature in °F
O2stack = Excess oxygen measured at stack in dry mol%

Target setting

The historical performance of the fleet in terms of KPI is valuable information in defining targets. Normally, KPI variability follows the normal variation with the fleet being operated at its best performance at least 10% of the time. A typical variability is shown on the upper left hand of FIG. 3. Minimizing the variation is the first step in improving performance, followed by pushing the mean value closer to a constraint. The energy penalty caused due to a large variability is defined as an operational gap (GAP 1A) that can be closed through operational excellence; whereas GAP 1 is the energy penalty due to the difference between the current and the best demonstrated performance. This can be closed via engineering and maintenance excellence. Finally, GAP 2 represents the gap between the best demonstrated and the best-available technology. This gap closure often involves capital investment.

FIG. 3. Typical variability of performance parameters and how improvement is made.

Complex fleets like gas turbines and furnaces often require understanding the impact of external parameters, such as turndown operation on various targets, and must be incorporated in the algorithms to identify realistic gaps. For example, stack oxygen increases with furnace turndown operations due to its inherent characteristics and operability issues, raising serious safety concerns. Therefore, it is important to model oxygen targets as a function of furnace duty, as shown in FIG. 4.

FIG. 4. Moving stack O2 target.

Opportunity definition

It is important to show the gap between target and actual performance in terms of financial opportunity. The opportunity value helps to drive performance excellence. Engineering gaps—such as the potential energy savings and carbon dioxide (CO2) savings if the fleet can be operated at its target—can be easily converted to an economical opportunity using the site fuel, power and CO2 prices.

Data collection

This is the most challenging aspect of the entire process. A robust tool requires information, including instrumentation tags, equipment design information and piping and instrumentation diagrams (P&IDs). Developing a pictorial list of required instrumentations, as shown in FIG. 5, facilitates the data collection request. At times, the information might have limited availability, especially if the facility was built several decades ago. Such sites often lack sufficient instruments that are critical for KPI calculations. However, a corporate level initiative to build such systems motivates local business partners to install the desired instrumentation using cost-effective methods (e.g., wireless instruments). In a few cases, it is often required to independently infer the missing information.

FIG. 5. Instrument tags required for monitoring a fired heater.

Data analysis and visualization

As a part of tool development, it is important to sanitize the process information using data analytics tools. Data sanitization includes, but is not limited to, screening poor measurements, identifying the equipment running status, etc. Such an approach often provides robust KPI numbers. Inferior data input will skew the corporate level performance. Several calculation engines offered by technology companies can be used to develop process models based on functional specification.

To turn data points into actionable information, it is important to arrange these millions of data points using an adequate visualization platform. A “visual story” based on plant information can be developed that will support improvement opportunities. Various types of visuals should be considered, depending on the corporation’s requirement for action. The resulting actions taken by the operators/engineers will determine the success of the tool.

Several dashboards can be tailored to meet the requirements of the broad segment of users. Monitoring engineers, subject matter experts (SMEs) and corporate level managers are the potential users of the dashboards prepared by the tool. Such dashboards enable spotting several improvement opportunities at various levels, and the right teams can be engaged to capture those opportunities.

A dashboard displaying information for all sites, such as the one shown in FIG. 6, promotes the social proof principle, initiating dialogue between various sites for expertise and knowledge transfer. Open dialogues between sites improve the knowledge base and best practices that are essential building blocks for the improvement. FIG. 6 shows various performance dashboards for global/regional fleet KPIs, as well as site and equipment level performance, whereas FIG. 7 shows how the dashboards can be used to identify opportunities at the equipment level. Equipment for each site can be arranged in descending order of the value of the opportunity. Alternatively, the opportunities can be viewed with a focus on the dominant cause. This helps in organizing the right teams to close the identified gaps.

FIG. 6. Example dashboard.
FIG. 7. Opportunity identification.


This step is critical for a successful rollout of the corporate-wide tool, ensuring that relevant stakeholders are aligned and support the tool. Typically, the validation exercise requires several engagements between project engineers and the right stakeholders (site engineers, site management, corporate level energy analysts, etc.).

Depending on the corporation’s governance structure, existing work processes such as a technical quality assurance procedure may need to be leveraged. Projects associated with closing the identified gaps should undergo an independent project review or a cold eye review. This step ensures that business partners have trust in the new system, and accept the opportunity values calculated by the tool. Trust is a key factor that can drive actions based on the tool’s recommendations.


The focus in this final step is to embed the use of the tool on a routine basis. This will enable long-term success in extracting the most value out of the tool. This includes aligning roles and responsibilities among various departments and assigning appropriate work-process modifications to streamline the use of the tool. Integrating the tool into existing equipment monitoring work processes and energy stewardship practices will enhance the performance of the corporate-wide initiative. Depending on the corporation’s governance structure, other integration opportunities must be identified to leverage them in enhancing the performance of the corporate fleet.

Data architecture

For successful digitalization, seamless data flow and connectivity are essential, especially where billions of data points are involved. This includes corporate level IT infrastructure to collect and store the information in central historians and establishing connectivity to provide secure access to the data for the entire organization. Information such as process measurements from each process unit gets historized in the data historian system. Data is accessed by the fleet tool through custom communication channels. It is important to have a powerful calculation engine that can process such information at scheduled periodic intervals, generating results and historizing to the data historian or a local database (FIG. 8). Such a database enables easy linkage with visualization software or even a web portal, creating a graphical user interface that is easy to access and use.

FIG. 8. Data architecture.


The authors’ company widely employs the fleet monitoring tool for a variety of energy fleets, including furnaces, boilers, gas turbines, waste heat recovery steam generators, steam systems and heat exchangers. Thousands of energy consuming and producing equipment are monitored daily, benefitting the corporation. The prime advantage of such a tool for the authors’ company is to drive transparency and impel action to steadily improve energy efficiency. For example, with the furnace fleet tool rollout, the authors’ company has realized improved operational and maintenance discipline at sites.

The tool has identified maintenance opportunities, such as the repair of dampers and seal air leaks and the performance of regular CO breakthrough tests for turndown operations. It has also helped in identifying capital projects such as air preheater replacement. For a few items, sites are able to justify investments on an expedited basis as the associated losses are transparent to management. This has helped improve overall corporate level fleet performance.

FIG. 9 shows the air preheater upgrade project performance. The project was justified as the team was able to get historical performance from the tool and visualize the issues associated with existing technology—the technology was deemed to be outdated. By using this approach, a one-time investment in upgrading technology and equipment for the air preheater is justified that avoids the associated debit due to frequent breakdowns. HP

FIG. 9. Tool enables justification of air preheat replacement.


The authors would like to acknowledge the contributions of the site engineers, fired heater subject matter experts, Krishnan Chunangad, Camron Solomon, Edward Kubis, Keen Seng, Nick Smith, Phil Melancon, Bill Hicks, Nur Hidayah Md Nasir, et al., for providing the help and support in developing the tool.


  1. ExxonMobil, “ExxonMobil sustainability report,” 2020, online:
  2. ExxonMobil, “ExxonMobil announces emission reduction plans; expects to meet 2020 goals,” 2020, online:
  3. ExxonMobil, “ExxonMobil announces greenhouse gas reduction measures,” 2019, online:;.
  4. ExxonMobil, “Energy and carbon summary,” 2021, online:
  5. Cialdini, R., Influence: How and why people agree to things, Morrow, 1984.
  6. Kiran, D. R., Total quality management: Key concepts and case studies, Elsevier, 2017. .
  7. Ganapathy, online:

The Authors

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