Interactive brochure on Omnivise Performance
In the competitive power-generation market, power utilities are focused on optimizing performance while minimizing operational costs to deliver low-cost, high-quality energy to their customers.
However, there needs to be a balance in the trade-offs between performance, operational costs and risks.
When it comes to fossil generation, performance optimization is directly related to operations cost. Using a Thermal Digital Twin based on data from the DCS for advanced control concepts without major changes to mechanical equipment is one of the options to increase your plants profitability.
With Omnivise Performance you can adapt plant operations to your current requirements.
GT Auto Tuner is an AI-based solution for gas turbines that uses a digital twin to optimize the turbine inlet temperature and emissions with the help of reinforcement learning.
The turbine inlet temperature is estimated accurately by performing online energy balance in real time. Goal of the improved turbine inlet temperature control is to compensate the power degradation (performance recovery) in combination with a continuous real time optimization of emissions and combustor dynamics.
DEWA extends usage of GT Auto Tuner
A new package of enhanced energy services from Siemens Energy will supply Dubai Electricity and Water Authority’s (DEWA) Jebel Ali L2 power and water station with the latest advancements in power plant service, maintenance, and controls. Under terms of the new long-term service agreement, Siemens Energy will supply an intelligent controller for each of the four SGT5-4000F gas turbines, the latest SPPA-T3000 control system, services for generators, as well as added upgrades for outage reduction and operational flexibility.
Changes in ambient conditions and exhaust back pressure can result in different combustion visible in modified dynamics and emission levels.
Patented reinforcement learning (RL) methods extract existent dependencies into a neural network based RL control policy that is continuously adjusting the combustion process.
Field experience demonstrated the potential to lower NOx emissions and minimize the need for seasonal tuning based on the combination of AI and improved turbine inlet temperature estimation.
- Power degradation recovery
- Increased part load efficiency
- Further reduction of seasonal tuning
- Reduced combustion dynamics and emissions: Possible ammonia reduction through improved emissions control as well as ~ 10% lower NOₓ output
- Close loop control utilizing an AI control policy
The world‘s first methods for data-efficient reinforcement learning on collected dataVolkmar Sterzing and Steffen Udluft of Siemens Energy Corporate Technology developed a process that uses just a few recorded data to teach machines to regulate complex processes on their own. The continuous fine-tuning of the combustion valves has optimized gas turbine operations in terms of emissions and wear by constantly searching for the best solution in real time.
The Stability/Emissions Functionality autonomously introduces refinements to the fuel distribution and outlet temperature control in order to drive emissions and combustion dynamics towards optimal values in both steady state and transient operation.
It allows real time combustion tuning by fuel staging optimization and continuously optimized fuel fraction control for the given load.
This autonomous combustion control helps to significantly reduce NOₓ emissions.
- Continuous emissions and dynamics tuning, intended to eliminate seasonal manual tuning
- In case of installed SCR (catalyst): continuous emissions control reduces use of ammonia used to meet stack NOₓ guarantees
- For SGT6-5000F/6000G: Continuous tuning may result in reduced occurrences of high dynamics/flashback
- Ability to better handle fuels with varying gas composition
The Performance Functionality allows real time gas turbine performance modelling to improve engine performance by compensating detrimental effects to temperature control set point (OTC).
This partial degradation recovery functionality integrates physics-based Real Time Thermal Modeling (RTTM), a software library calculating turbine inlet temperature (TIT) in real time on site. Adjustments are made autonomously at baseload to maintain the RTTM-calculated TIT to the predefined TIT set point.
- Ability to control engine to a specific turbine inlet temperature set point, leading to greater accuracy in engine control
- Ability to provide partial degradation recovery by increasing turbine inlet temperature above its degraded state (limited to frame specific)
- Ability to increase efficiency of the GT due to increased turbine inlet temperature
GT Auto Tuner is available for the following gas turbine models:
GT Auto Tuner can also support SGT6-6000 gas turbines.