SPG Dry Cooling uses advanced analytics from SAS to analyze the performance of its air-cooled condensers, enabling power plants to run more efficiently, improve maintenance planning and better forecast energy production
An air-cooled condenser is an essential part of a thermal power plant. It condenses the steam at the end of a turbine and returns the condensate to the boiler, completing the steam cycle. The performance of this installation determines the energy production.
Headquartered in Brussels, Belgium, SPG Dry Cooling is a major manufacturer of air-cooled condensers, with installations at power plants around the world. Usually huge volumes of water are required to condense steam, but that is not the case with dry cooling. This method is particularly favored in areas that have a low water supply and high water costs or are subject to environmental restrictions.
To predict and optimize the performance of its condensers and the associated steam cycle, SPG Dry Cooling turned to SAS advanced analytics.
Analytics enables us to give our customers advice on optimization and the asset health of their components, allowing them to run their power plants more efficiently. This means we are no longer seen as a mere supplier of equipment but as a long-term partner. Frédéric Anthone Aftermarket Manager SPG Dry Cooling
An air-cooled condenser is part of a steam cycle. First, water is turned into steam inside a boiler. This steam powers the turbine that generates electricity. The steam then flows from the turbine exhaust into the air-cooled condenser where condensation occurs – ventilators blow cooler air on the steam heat exchanger. Finally, the condensate returns to the boiler in a closed loop. As the steam at the end of the turbine is at a low pressure, the air-cooled condenser works at a pressure close to a vacuum, and non-condensable gases are removed continuously.
The performance of the air-cooled condenser determines the amount of energy produced, but the installation is subject to several factors, such as ambient temperature and wind. When it is cold outside, steam condenses much better, and the energy production will increase. Strong lateral winds, however, act as an obstacle for the ventilators and affect the overall capacity.
For the end users of SPG Dry Cooling’s products, it would be advantageous to forecast the performance of the installation in all circumstances. Fortunately, lots of parameter data captured by IoT sensors is available for analysis. And that’s where SAS enters the picture.
Predicting performance and optimizing operations
SPG Dry Cooling requested SAS’ assistance to build a digital twin of the installation. “Scalability is an important reason why we wanted an experienced partner,” says Frédéric Anthone, Aftermarket Manager at SPG Dry Cooling. “The advanced analytics capabilities of SAS are beyond dispute. The solution can be implemented on all types of dry cooling installations, which will lead to huge amounts of data and result in more precise predictions.”
“We have a much better knowledge of our installations,” adds Christophe Deleplanque, Vice President of Innovation at SPG Dry Cooling. “We used to rely on our experience and theoretical data, but there are too many parameters to take into account. Advanced analytics allows for more detailed analyses of our equipment, which enables us to optimize the scale and the operations for our customers.”
In an ideal scenario, more than 4,000 air-cooled condensers worldwide could be connected and share data for analysis. The predictive power of this solution has multiple benefits, giving power plants different ways to enhance their operations, meet demand and ultimately satisfy their customers.
According to Anthone, advanced analytics is helping SPG Dry Cooling achieve three key goals:
- Increase the efficiency of power plants.
- Help power plants avoid unplanned outages and achieve better maintenance planning.
- Enable forecasting of power plant capacity.
Predicting when a system needs maintenance is extremely beneficial for power plants. Cleaning an air-cooled condenser requires a significant amount of water and is very costly in areas with a low water supply. But the energy output also increases after the cleaning process. As analytical models offer better insights about performance, operators can explore the limits of the air-cooled condenser installations and postpone maintenance until needed. Optimized maintenance leads to increased reliability and cost savings.
Additionally, power plant operators can better gauge the output of their installation 24 hours in advance. Not only do power suppliers have a much better idea of the amount of electricity they can bring to the market, this capability also gives them the possibility to optimize the net plant heat rate.
“Analytics enables us to give our customers advice on optimization and the asset health of their components, allowing them to run their power plants more efficiently,” Anthone says. “This means we are no longer seen as a mere supplier of equipment but as a long-term partner.”
SPG Dry Cooling – Facts & Figures
offices around the world
Enhanced communication and future opportunities
SPG Dry Cooling also benefits from these forecasts. As the manufacturer usually develops condensers for the constructors of power plants – not directly for the operators – feedback about the lifetime performance of installations has been scarce. Now engineers at SPG Dry Cooling receive valuable information to improve future air-cooled condenser designs.
The company takes pride in its innovative equipment and services, numerous patents and product developments. With analytics as an integral component of its operations, SPG Dry Cooling looks forward to new opportunities for advancement.
“The analytics solution provided by SAS opens the door to a wide range of possibilities, including building on our large assets fleet to continuously optimize our air-cooled condenser solutions, even in the most extreme operational conditions,” Deleplanque says.
“The big challenge was the communication between thermal engineers and data scientists,” Deleplanque continues. “For the latter, the source of data doesn’t really make a difference. Data may come from banks, pharmaceutical companies or, in our case, air-cooled condensers. However, we wanted to offer our customers real added value. So we needed something that goes beyond pattern recognition. SAS has the right people to understand these processes. On top of that, we haven’t reached the limits of advanced analytics – there is still much more potential in our SAS solution.”
본 문서에 나오는 결과는 본 문서에 설명된 특정 상황, 비즈니스 모델, 데이터 입력 및 컴퓨팅 환경에 적합하게 되어 있습니다. 각 SAS 고객의 경험은 고유한 것으로, 비즈니스 및 기술적 변수에 따라 달라집니다. 따라서 모든 서술은 비전형적인 것이라는 점을 고려해야 합니다. 실제 절약, 결과 및 성능 특성은 개별 고객의 구성 및 조건에 따라 달라질 수 있습니다. SAS는 모든 고객이 비슷한 결과를 달성할 수 있다고 보증하거나 진술하지 않습니다. SAS 제품과 서비스에 대한 유일한 보증은 해당 제품 및 서비스에 대한 서면 계약의 보증서에 명시되어 있습니다. 본 문서의 어떠한 내용도 추가 보증을 구성하는 것으로 해석될 수 없습니다. 고객은 SAS 소프트웨어의 성공적인 구현에 따라 합의된 계약적 교환 또는 프로젝트 성공 요약의 일환으로 성공 사례를 SAS와 공유했습니다. 브랜드 및 제품 명칭은 각 기업의 상표입니다.