How Digital Technology Assist in Carbon Footprint Reduction?

As fossil fuel plant owners and operators manage the energy transition, digital technology provides the chance to perform while transforming and balancing the energy trilemma of delivering a secure, sustainable, and equitable energy system each step of the way.

That being stated, not every digital technology used will result in benefits for operations and maintenance (O&M). If the relevance of technological flexibility, user experience, and equipment know-how is not addressed from the start, you may not achieve your desired goals. It may be difficult to connect the dots between and across systems if the strategy is not tailored to the teams. Adoption is at risk if the solution is too difficult to use. And if the models are not supported by the developers’ skills, data accuracy is jeopardized.

However, with the appropriate solution, O&M executives are discovering that digital solutions enable them to develop toward a more sustainable future, decreasing their facilities’ carbon footprints without sacrificing cost or dependability. In many situations, they discover that by boosting automation and giving better insight into reliability concerns and performance inadequacies, they may cut their cost of generation, increase reliability and availability, and reduce greenhouse gas (GHG) emissions.

Using Digital Technology to Operationalize the Asset

When industry and equipment expertise is combined with AI/ML (artificial intelligence/machine learning) technology, it may be a strong tool for unlocking performance, improving fuel consumption and availability, and reducing heat rate and emissions. Whether used to a gas or steam plant, such technology produces tangible results in terms of O&M.

Optimization of Gas Turbine Combustion

Gas turbines often require seasonal modification, tuning, or mapping of flame temperatures and fuel splits to maintain consistent and emissions-free operation when weather patterns vary seasonally. This can be a manual operation conducted by an expert on-site, and it frequently necessitates an outage, which reduces availability. Furthermore, manual seasonal tuning is only useful for the specific conditions under which it was accomplished, and it does not allow the gas turbine to adapt efficiently to changes in ambient temperature or fuel properties between tunings.

Aeroderivative gas turbine operators might get the following benefits by using AI/ML technology to continually improve combustion in closed-loop control instead of human seasonal adjustments:

  • CO2 emissions/fuel consumption/heat rate are reduced by 0.5% to 1%.
  • CO emissions can be reduced by up to 14%.
  • NOx emissions can be reduced by up to 12%.
  • Improved availability with no human adjustment required and no related downtime.

AI-enabled tuning software deployed in a supervisory control system and completely bound by the control system’s safety-critical programming may safely employ machine learning to identify the best flame temperatures and fuel splits continuously and autonomously for optimal combustion. This is especially true as crucial factors such as ambient temperature and fuel quality vary.

Case Studies in Gas Turbine Combustion Optimization

Two case studies of aero-derivative power plants demonstrate the sorts of benefits that such technology may provide.

First case study

In the first case study, a peaking combined cycle power plant had a variable natural gas composition, which created emissions and operability difficulties as well as frequent technical intervention, resulting in downtime. Remote tuners were frequently summoned to make manual changes to prevent stage down for excessive acoustics or blowout, as well as to handle NOx or CO concerns at baseload or low-load operations.

The following outcomes were obtained through the use of AI-enabled combustion optimization:

  • When using a low specific gravity composition, which tends to increase CO emissions, CO emissions were decreased by 14%.
  • When running with a high specific gravity composition, which tends to increase NOx emissions, NOx emissions decreased by 12%.
    Yearly or seasonal tuning events were decreased from four to zero, while 12 days of downtime were avoided.
  • During the 12-month period following installation, the site’s high acoustic occurrences decreased from six to zero.

second case study

The second case study was a power plant that battled with emissions to the point that it ran out of NOx credits one summer, effectively shutting down generating for the rest of the year. The months of July, August, and September accounted for one-third of the entire normal generation for this facility, highlighting the problem’s criticality.

Following the deployment of a computerized technology to continually improve combustion in closed-loop, the following benefits were realized:

  • NOx emissions decreased by 10%, avoiding the requirement for a $2 million combustion upgrade that would have resulted in a 12-week outage.
  • The plant was able to generate power without exceeding NOx credits during its high-demand season and beyond, producing $300,000 more than the previous season.
  • Yearly or seasonal tuning events were decreased from two to zero, while six days of downtime were avoided.
    The location did not encounter any high acoustic incidents after the installation.

Optimization of Steam Plant Boilers

Traditional, schedule-based steam plant control systems limit the ability to optimize combustion for heat rate and emissions as the boiler degrades, potentially resulting in unanticipated downtime due to tube ruptures caused by excessive soot blowing. To overcome these constraints, several digital solutions have been developed; nevertheless, open-looped methods still provide the potential for operator inconsistency, and model-based predictive control has limited efficacy across the operating range. This can prevent the intended efficiency from being realized.

Using a closed-loop system that biases the control setpoints to adjust air-fuel mixing and activate blowers when a section of the boiler needs to be cleaned might bring benefits in terms of emissions, heat rate, and availability, including:

  • CO2 emissions/fuel consumption/heat rate can be reduced by up to 0.5%.
  • NOx emissions can be reduced by up to 15%.
  • Improved availability of 10% to 25% due to a reduction in boiler tube leaks caused by soot blowing.

Furthermore, for units that use selective catalytic reduction, or SCR, adopting a system that optimizes soot blowing and combustion control increases SCR efficacy while decreasing operating costs. It improves the SCR’s performance and ammonia efficiency by enhancing the combustion process’s stability and uniformity at baseload or transient operation, while simultaneously lowering the quantity of primary NOx to be reduced by ammonia injection.

The method enhances SCR removal efficiency, decreases ammonia usage, and has the ability to reduce ammonia slip by delivering a lower and more balanced profile. Because there is no steep learning curve in using a closed-loop system, it is easier to achieve the “best zone” and improve acceptance. This method allowed heat rate increases of 0.55% to 0.61%, fuel savings of more than $1.7 million per year, and CO2 reductions of 38,000 tons per year for a three-unit coal-fired steam plant.

Using Digital Technology to Improve the Process

Operators are discovering possibilities in O&M that may affect their carbon footprint and cost to generate by improving procedures and workflows, in addition to operationalizing assets with digital technologies.

By linking the data, system, and workflow silos that hide latent potential, digital solutions give insight and promote operational improvements and efficiency. To link siloed procedures with advanced analytics, one method in O&M is to integrate an advanced thermal performance tool into an asset performance management framework. Turbine control systems cover excessive fuel emissions, fuel control, and vibration monitoring. GE turbine control system parts include IS200EISBH1A, IS200ERBPG1A, etc.

Intelligent Productivity

Many fossil fuel facilities are operating at levels lower than their original design, causing them to cycle more and operate for longer periods of time at levels much below their optimum efficiency levels. This makes assessing performance gaps, risks to dependability and availability, and return on investment from maintenance efforts challenging.

Heat rate is a measure of plant efficiency that varies with seasonal load profile, operating modes, ambient conditions, and equipment health. Because heat rate is not immediately quantifiable in the plant, it might be difficult to determine whether a change in heat rate is due to a change in operating circumstances or a change in equipment health and deterioration without a digital twin. Only when the source and extent of the change are known can the most effective use of resources be allocated to have the most influence on recoverable deterioration and assure optimal performance throughout time.

Digital technology may help minimize emissions, heat rate, and O&M expenses by combining a flexible, user-friendly thermal performance application with the analytic capability to give a comprehensive problem-solving solution to keep fossil fuel facilities at peak efficiency. Advanced Thermal modeling and analytics throughout the whole load range, with actionable suggestions, should be included in such a system.

Analytics for economic forecasting, carbon key performance indicators (KPIs), and what-if scenarios. Fully integrated data streams into the work management process across the plant or organization, from asset strategy and monitoring to maintenance and knowledge management.


With a complete monitoring and diagnostics tool, performance engineers may be quickly notified of slight but substantial changes in operational capabilities, allowing them to examine and remediate the problem. In the case of a condenser air leak, early detection from advanced thermal analytics, combined with the ability to forecast performance into the future while accounting for economic impacts, can allow the team to decide whether to call in an outage to address immediately or wait, depending on the expected costs and benefits.

Furthermore, if it is judged that the change is not significant enough to justify an immediate outage, the application continues to monitor—and if an inflection point occurs and performance degradation increases, the team is promptly notified and can reconsider its choice. When this type of technology is used, detecting and resolving a single condenser issue can result in considerable O&M savings in terms of both cost and carbon footprint.

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