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As fossil fuel power plant owners and operators go through the force transition, virtual generation provides the opportunity to carry out while transforming, balancing the force trilemma of delivering a safe, sustainable, and just force formula at each and every level of the process.
That said, any virtual generation deployed will result in operation and maintenance benefits (OR
However, with the solution, managers OR
Starting at the asset level, when the wisdom of industry and appliances is combined with AI/ML (artificial intelligence/machine learning) generation, it can be a difficult tool to unlock performance, fuel consumption and availability, and reduce heat rates and emissions. Whether it is implemented to a fuel or steam force plant, this generation gives concrete effects on O
Optimization of the combustion of fuel turbines. Typically, fuel turbines require seasonal adjustment, configuration or mapping of flame temperatures, and fuel distributions to perform certain reliable operations that meet emissions as weather conditions change during the season. requires a failure, which affects availability. In addition, manual seasonal adjustment is only effective for the exact situations in which it was performed and does not allow the fuel turbine to respond well to adjustments in ambient temperature or fuel homes between configurations.
By employing AI/ML technologies to frequently optimize combustion in closed-loop control rather than manual seasonal adjustments, aeroderivative fuel turbine operators can reap the following benefits:
■ Reduction from 0. 5% to 1% in CO2 emissions / fuel consumption / heating rate.
■ Up to 14% reduction in CO emissions.
■ Up to 12% reduction in NOx emissions.
■ Improved availability of any manual configuration or related downtime.
Ai-enabled tuning software that is implemented in a supervisory control system, which is completely constrained through safety-critical programming of the control system, can safely use device learning to locate temperatures of ideal flame and the ideal fuel splits frequently and autonomously for optimal combustion. is true when critical variables such as environmental conditions and fuel quality change.
Two case studies of aeroderivative power plants illustrate the types of benefits this generation can offer. In the first case study, a state-of-the-art combined cycle power plant was subject to a fluctuating composition of herbal fuel that caused emissions and operability problems. and required a common technical intervention, resulting in downtime. Occasionally, remote tuners were asked to make manual changes to avoid upper acoustic relief or eruption, and for NOx issues during baseload or CO operations during low-load operations. With the implementation of AI- based on combustion optimization, the following effects were achieved:
■ CO emissions have been reduced by 14% by operating with a low-density composition that tends to accumulate CO emissions.
■ NOx emissions have been reduced by operating by 12% with a high-density composition that has a tendency to accumulate NOx emissions.
■ Annual or seasonal adjustment times went from 4 to zero, avoiding 12 days of inactivity.
■ After installation, the best acoustic moments of the site were reduced from six to zero in a period of 12 months.
The current case examined concerned a power plant that suffered emissions to the point of exhausting NOx credits one summer, preventing any new generation for the rest of the year. The months of July, August and September accounted for a third of the overall typical generation for this site, which added to the criticality of the problem. After implementing a virtual solution to frequently optimize closed-loop combustion, the following benefits were obtained:
■ NOx emissions were reduced by 10%, eliminating the need for a combustion overhaul that would have cost $2 million and resulted in a 12-week outage.
■ The software allowed the site to generate electric power in its high-demand season and beyond without exceeding NOx credits, generating $300,000 more in profits than the previous season.
■ Annual or seasonal adjustment times went from two to zero, while avoiding six days of downtime.
■ After installation, he did not delight in the best acoustic events.
Traditional program-based systems for steam plants restrict the ability to optimize combustion for heat rate and emissions as the boiler degrades, which can lead to unplanned downtime due to tube breaks due to excessive soot blowing. A number of virtual responses have been developed to address these constraints; however, open-loop responses still leave room for operator inconsistencies, and model-based prediction has restricted effectiveness across the operating range. This can prevent you from achieving the desired efficiency.
Using a combination of predictive styling and AI/ML generation in a closed-loop formula that skews the set things to change the air and fuel set and activate enthusiasts when an area of the boiler needs to be cleaned can provide emissions, heat rate and availability benefits, including:
■ Up to 0. 5% reduction in CO2 emissions/fuel consumption/heat rate.
■ Up to 15% reduction in NOx emissions.
■ 10% to 25% availability increased by reducing leakage in the boiler tubes by blowing soot.
In addition, for selective catalytic relief units, or SCRs, deploying a solution that optimizes soot blowing and combustion improves SCR power and reduces operating load. Improves the functionality and power of SCR ammonia by improving stability. and uniformity of the base load or short combustion process, while reducing the amount of NOx number one to be reduced through ammonia injection. By offering a lower and more balanced profile, the generation improves SCR removal power, reduces ammonia input and has the decrease in ammonia slippage.
A closed-loop solution achieves the “best zone” and increases adoption, as there is no upper learning curve on how to use the solution. to 0. 61%, fuel savings of more than $1. 7 million year-consistent and CO2 relief of 38,000 tons/year.
In addition to operationalizing assets with virtual technology, operators are opportunities in the O
Smart functionality. Many fossil fuel plants operate at lower grades than their original design, forcing them to cycle more and spend more hours operating well below their optimal power levels. This makes it difficult to assess functionality deficiencies, risks to reliability and availability, and recoup investment from maintenance activities.
The heat rate, a measure of plant efficiency, is a moving target that adjusts with the seasonal load profile, operating modes, environmental situations, and apparatus fitness. The heat rate cannot be measured directly in the plant and therefore, without a virtual dual (Figure 1) to compare functionality, it can be difficult to know whether a replacement in the heat rate is due to a replacement in operating situations or to a replacement in the physical condition and degradation of the apparatus. Only with the clarity of the source and the magnitude of the replacement can maximum effective use of resources be implemented to have the greatest impact on recoverable degradation and ensure optimal functionality over time.
Therefore, classic thermal functionality equipment and processes, which have been in use for many years, are temporarily too bulky to meet the mandatory speed and flexibility. In the absence of a complex functionality solution, many factories operate below their optimal power levels. In addition, when asset strategy, functionality and reliability monitoring, and maintenance workflows are not fully integrated, manual transfers save you formula optimization and result in higher costs, higher fuel consumption, and higher GHG emissions.
If the virtual solution is not complete to respond to those problems, groups are not empowered to make the most productive decision. It is not enough to monitor: groups want visibility into economic assumptions and scenarios. In addition, the analysis of the solution is as intelligent as the virtual dual and, finally, the software only continues to give price if it is configured as it should be after operational changes.
By combining a flexible, easy-to-use thermal functionality application with analytical strength to provide a holistic problem-solving solution to keep fossil fuel plants at optimal efficiency, virtual generation can help reduce emissions, heat rate, and operation and maintenance costs. Such a solution includes:
■ Advanced thermal research and modeling across the load range, with practical recommendations.
■ Analysis for economic forecasts, carbon functionality signals (KPIs) and hypothetical simulations.
■ Knowledge flows fully incorporated into the paint control procedure throughout the factory or company, from asset strategy, monitoring and diagnostics to maintenance and knowledge control.
With a comprehensive tracking and diagnostics application, functionality engineers receive temporary alerts about minor but significant adjustments to operational functions so they can properly assess and mitigate the issue. In the case of a condenser air leak (Figure 2), early detection from complex thermal analyses, combined with the ability to assess long-term functionality taking into account economic impacts, can allow the equipment to intervene without delay in the event of an interruption or wait, based on expected prices and benefits.
Also, if we decide that the update is not enough to warrant an immediate termination, the application continues to monitor and if there is a tipping point and functionality degradation accelerates, the team is immediately alerted and can do it again. Evaluate your decision. When this type of solution is implemented, detecting and resolving an unmatched capacitor factor can lead to significant O&M results, both in terms of load and carbon footprint.
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