February 2017

Process Optimization

Use the right model to unlock utility system potential

Although we have entered a new era of low energy prices, energy still represents one of the largest, but most easily managed, operating costs in the hydrocarbon industries.

Gómez-Prado, J., Hutton, D., KBC Process Technology Ltd.

Although we have entered a new era of low energy prices, energy still represents one of the largest, but most easily managed, operating costs in the hydrocarbon industries. In oil refining and petrochemicals, the use of steam, fuel and power typically accounts for a significant part of the operating cost. However, the potential economic benefits that follow from correct operation of the utility system are often unclear.

For example, in olefin plants, the cracking furnaces are the primary energy users and typically receive most of the focus in energy performance improvement studies. However, furnaces account for only about 5% of the energy performance gap (FIG. 1). Almost half of the gap is due to inefficiencies in the steam and power system (i.e. utility system). This paper describes the role that utility system modelling can play in closing this large gap.

FIG. 1. Historical breakdown of energy performance gaps at refining and petrochemicals sites.

Utility systems: Generically simple, yet complex

Steam and power systems for refinery and petrochemical sites are conceptually simple (FIG. 2):

  • Boilers generate steam
  • Heat, in the form of steam, is supplied to users in the process plants
  • Power (or shaftwork) is generated in turbines as the steam is converted to the appropriate temperature and pressure levels for consumers
  • Some amount of water treatment is required to ensure that the system keeps running.
FIG. 2. Typical utility system configuration with boilers, turbines, letdown stations and users.

This simplicity of the system is deceptive. It appears that the system is just heating up water and then condensing it again. How is it possible to get it so wrong? On paper, the operation sounds simple, but it is actually complex in operation:

  • Process unit steam demands are continuously changing
  • Different boilers have different efficiencies, as do turbines
  • Equipment can be offline for maintenance
  • Economic drivers change across the day (e.g., power tariffs)
  • Use of different fuels can be dictated by price and emissions limits
  • Operations are pushing for increased reliability and switch between motor and turbine drives.

All of these factors must be addressed within the capacity limits of the system and its individual components. This means that even though a site’s utility system might be considered simple, the operational complexities are such that a model of the utility system will help even experienced staff gain a better understanding of the system’s interactions, and allow them to assess and evaluate responses to situations as they arise.

As shown in FIG. 1, up to 50% of a site’s energy performance gap can be attributed to the utility system. In economic terms, models of utility systems help identify operational savings of 2%–4%; this value reaches 10% in certain cases. When used at the heart of a performance monitoring system, an additional 1%–3% can be achieved. Clearly, if investment is being considered, even greater strides can be made to close the performance gap.

Capturing complexity for different stakeholders

All components of the utility system must be accounted for to capture the system’s interactions, even if the modeling approach for each piece of equipment is simple. Just how simple is defined by answering the key question when building a utility system model: For what is the model going to be used? A number of stakeholders, each with a different role to play in managing the system most effectively, will reference the model:

  • Utilities operations
    • Real-time operational advice on system optimization based on site utility demands, energy tariffs and operating constraints; this measurement can be done offline or online, up to once per hour
    • Training
  • Utilities planning
    • Offline operational scenario planning based on projected site demands and equipment availability
    • This operation enables utilities plant managers to plan for equipment maintenance and defined operating scenarios (feedstock changes, turnarounds, electrical price variations, plant outages, etc.) and to make contingency plans for unplanned outages
  • Site energy management
    • Connected to the data historian, a model can be used for monitoring, tracking performance and determining lost opportunities
    • Using heat and mass balances around the headers, models can help to determine losses or metering issues
    • General troubleshooting by providing engineers with the tools to analyze performance at the system level or equipment level
  • Investment project planning
    • Offline modeling to calculate the real value of projects in terms of imported utilities, as well as the impact on the overall site utility balances
    • Perform what-if analyses
    • Review benefit of quick wins and investment ideas
  • Large project planning
    • To modify or build new utility systems and to size new equipment (based on multiple design cases)
    • To review and optimize configuration decisions (number and type of boilers, gas turbines, steam turbines, etc.)
    • Evaluate suitability of the existing configuration for long-term site development plans (e.g., increase on steam demands, power demands, etc.).

Given the range of valuable uses that a model can be put to, it is fairly evident that a single model, or at least a single interface, will not meet all of these needs. Therefore, it is crucial that the purpose of the model(s), and the relevant functional requirements, are clearly defined before modeling begins. The worst of all solutions is a single model that is even more complex to use than the utility system itself, with the outcome that the model falls out of use.

FIG. 3 shows a high-level guide to selecting the type of model, and the level of detail, that will provide the functionality required for the various uses described above. As the guidelines suggest, few instances exist where an online optimizer should be the first choice of model, even in situations where optimization is the planned outcome.

FIG. 3. Guidelines for defining utility system model functionality.

An offline “static” model is a simpler (and cheaper) tool that provides an experienced team with the additional insights they need to identify the main drivers and the means of maximizing value from a reasonably complex system. One aspect that is often overlooked when developing a model is that the knowledge and experience of the operations team and engineering support function are often very high. The model is not there to replace them; it exists to free them from the mundane tasks of establishing and calculating balances so that they have time for higher-value tasks and decisions. As shown in FIG. 3, approximately 80% of the savings will be achieved by an experienced team working with an offline model.

It is logical to ask the question of how much additional value can be achieved by the more complex types of models. FIG. 3 shows a steam system assessment associated with the offline model. Once the model is established, this assessment determines the savings that have been captured, as well as estimates the additional value of moving to the next level. At this stage, it is a business case for putting the model online.

Assuming that this second step is implemented and the value of the energy management functionality has been properly demonstrated, a similar assessment is carried out, and a business case is developed for the final step of online optimization. The costs and complexities can then be weighed against the expected value at each stage.

The following case studies illustrate how a well-planned model can provide a high return in complex situations. One case study demonstrates how getting it wrong can be quite counterproductive.

Case Study 1: A poorly specified model

A refining and petrochemicals complex was investing in an integrated utility system after operating separate utility systems for different site areas for a number of years. To support the economic planning phase, an optimizer model was constructed to calculate new utility pricing for the integrated site areas. A member of the engineering team saw the model and requested the inclusion of a number of operational scenarios that would be useful in the commissioning phase. These scenarios were built into the model without a review of the needs and purpose. The consequence was a number of complex case studies that would help the engineer for a period of 2–3 months. The interface was modified to include this functionality, and was of no use to anybody else.

After the commissioning of the new utility system, the operations team saw the model and decided that it would be ideal for operational planning, with a few modifications. A further change in functionality was subsequently built into the model. Three modes of use now existed—two of which were redundant, and all of which required specific coding and interfacing, leaving the operations group to battle with useless features.

This case study is an example of a poorly specified model. It illustrates what can happen if a model is not properly planned for a specific purpose.

Case Study 2: Identification of energy saving opportunities

A utility system model was developed for a complex European petrochemical site, with the potential for generating power from a combination of multistage backpressure steam turbogenerators, steam condensing turbines and a gas turbine. This setup gave the site three choices for electrical power supply:

  1. Generation within the utility plant itself, with the choice of the four steam turbogenerators and the gas turbine, to provide sufficient power to meet the site’s demands
  2. Maximize self-generation and sell excess power to the external power grid
  3. Minimize self-generation and purchase the deficit from the external power grid.

An optimizer model was developed to determine the optimal operation, depending on the power tariff. While this determination was the ultimate purpose of the model development, significant additional value was created during its construction. During the building of the model, several improvements were identified. These could be tested once the model was completed. Improvement ideas included:

  • Increasing the VHP header pressure and decreasing the pressure of the other headers (value: up to $2.5 MM/yr)
  • Rebalancing the system to reduce power generation from the condensing turbine (value: approximately $15 MM/y)
  • Making changes to increase the demineralized water temperature (value: approximately $600 M/y).

Case Study 3: Analyzing investment decisions

FIG. 4. Simplified investment roadmap developed for an ethylene complex in Asia.

A utility system model was built for an ethylene complex in Asia to evaluate the impact of energy-saving projects on the utility system. The model was used to determine the true value of the projects—individually and in combination—in terms of imported utilities. The results were presented in an investment roadmap (FIG. 4), which illustrates two key investment decisions:

  • The model was used to illustrate how the decision on self-generated power hinged on whether or not the plant was prepared to shut down a boiler. If the boiler could not be shut down, then it would be more economically attractive to generate power. However, if the boiler could be shut down, then additional, economically viable projects could be implemented to provide greater overall savings.
  • The site was considering building a pipeline to import high-pressure steam. However, the model showed that rapidly implementing operational changes and low-cost projects in increments increased the payback of the new pipeline to the point where it became unattractive.

Case Study 4: Complex-wide utility system optimization

A refinery and neighboring petrochemical plant that were sharing utilities commissioned a utility system model to determine the overall optimal operation for the two sites, without compromising either site’s data security. The complexity of the individual sites was high, with multiple turbogenerators and switchable turbine-motor pairs. To add to the complexity, there was a complex power tariff structure that changed according to time of day, day of the week, and time of the year. Non-standard operating modes were also present, with significant fluctuation in operations.

FIG. 5. Overview of optimizer site interactions for Case Study 3.

An optimum operating mode is unlikely to be found without a model of the system, especially since each site did not wish to share its data with the other site. To tackle this problem, two steam system optimizers were developed and linked via a third “master” model (FIG. 5). This outline allowed local optima for each site to be determined, as well as the overall optimum for the complex. Additionally, the use of the master model ensured that sensitive operating data for each site could be protected.

The results showed attractive savings, as well as key observations regarding operation of the two sites:

  • The reported operational savings were 2%–4% of the energy bill for the utility system
  • The sites needed to establish work processes for sharing the benefits, since the overall optimum could mean that one site was required to operate sub-optimally
  • The sites gained deeper insight into their individual operations, as well as how changes in their operations affected the neighboring plant.


Utility system models can be designed and built for a multitude of purposes. The key to successful implementation is to decide on the purpose, and then to develop the functionality accordingly.

Experience has shown that up to 80% of the potential benefits can be captured by an experienced operations/engineering team working with an offline model. At the simplest level, the model is a means of developing heat and mass balances of the utility system. More significantly, it allows engineers to identify the main levers to make a step change in operational performance. Evidence indicates that this action can reduce operating costs by 2%–4%. Furthermore, it can serve as a tool for evaluating case studies in support of operational planning, or new capital investments, in which potential savings can increase to 10% or higher.

Since significant value can be achieved with a relatively simple model, a strong business case is required for additional model functionality. In general, installing a model online and using it for performance monitoring can lead to an additional 1%–3% reduction in cost. This cost reduction is mainly achieved by sustaining savings realized with quick wins, due to a more proactive approach from site personnel.

Similarly, a business case for an online optimizer must also be made. Generally, an optimizer can be expected to increase savings by an additional 1%–2%, but in a limited set of circumstances. The real value of an optimizer is realized when frequent and significant variations to steam demand/generation occur, or in the case of complex power tariffs. In many cases, the expense of an online optimizer can be difficult to justify if the operations and engineering teams are using an offline model to its full potential.

For all types of models, the potential savings discussed in this work are only achievable if the models are part of an effective energy management system; a model will not make the savings by itself (unless it is online and closed-loop, which is a separate discussion).

Any piece of technology requires people to use it, and it must have a defined means of use. Such a system must include an appropriate definition of roles and responsibilities for the operations and engineering teams, along with effective work processes and procedures, to ensure that opportunities for improvement can be identified, implemented and sustained. HP

The Authors

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