scholarly journals A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6852
Author(s):  
Grant Buster ◽  
Paul Siratovich ◽  
Nicole Taverna ◽  
Michael Rossol ◽  
Jon Weers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
High Reliability ◽  
Geothermal Systems ◽  
Low Carbon ◽  
Modeling Framework ◽  
Operational Optimization ◽  
Modeling Software ◽  
Geothermal Power ◽  
Nominal Performance

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

scholarly journals Complete Data Inventory of a Geothermal Power Plant for Robust Cradle-to-Grave Life Cycle Assessment Results

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2839 ◽  
Author(s):  
Lorenzo Tosti ◽  
Nicola Ferrara ◽  
Riccardo Basosi ◽  
Maria Laura Parisi
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Power Plants ◽  
Electric Energy ◽  
Impact Category ◽  
Gas Content ◽  
Primary Data ◽  
Geothermal Systems ◽  
High Concentration ◽  
Geothermal Power

Technologies to produce electric energy from renewable geothermal source are gaining increasing attention, due to their ability to provide a stable output suitable for baseload production. Performing life cycle assessment (LCA) of geothermal systems has become essential to evaluate their environmental performance. However, so far, no documented nor reliable information has been made available for developing robust LCA studies. This work provides a comprehensive inventory of the Italian Bagnore geothermal power plants system. The inventory is based exclusively on primary data, accounting for every life cycle stage of the system. Data quality was assessed by means of a pedigree matrix. The calculated LCA results showed, with an overall low level of uncertainty (2–3%), that the commissioning and operational phases accounted for more than 95% of the environmental profile. Direct emissions to atmosphere were shown to be the major environmental impact, particularly those released during the operational phase (84%). The environmental performances comparison with the average Italian electricity mix showed that the balance is always in favor of geothermal energy production, except in the climate change impact category. The overall outcome confirms the importance, for flash technology employing fluid with a high concentration of gas content, of using good quality primary data to obtain robust results.

Low Cycle Fatigue: Probability and Statistical Modeling of Fatigue Life

2014 ◽  
Author(s):  
D. Gary Harlow
Keyword(s):  
Distribution Function ◽  
Fatigue Life ◽  
Damage Accumulation ◽  
Power Plants ◽  
Structural Reliability ◽  
Low Cycle Fatigue ◽  
High Reliability ◽  
Cycle Fatigue ◽  
Low Carbon ◽  
Side Body

Low cycle fatigue (LCF) induces damage accumulation in structural components used in various applications. LCF typically describes conditions for which plastic strains are larger than elastic strains. In order to certify and qualify a structural component, manufactured from a given material, that requires high reliability for operation and safety, fundamental material properties should be experimentally investigated and validated. The traditional strain–life approach serves as the underlying experimental method for most LCF investigations. Building upon that background, the purpose of this paper is to investigate the statistical variability and appropriately model that variability for life in LCF. Specifically, the variability associated with the median behavior in a strain–life graph for data is examined. The ensuing analyses are based on data for a cold-rolled, low carbon, extra deep drawing steel; ASTM A969 which is appropriate for applications where extremely severe drawing or forming is envisioned. It is frequently used in the automotive industry for components such as inner door components and side body components. For substantiation of the proposed modeling techniques, data for 9Cr-1Mo steel is also investigated. Such steel is frequently used in the construction of power plants and other structures that experience operating temperatures in excess of 500°C. The commonly used universal slopes approach for fatigue life modeling for which the strain–life computation employs the standard Coffin–Manson relationship is compared to a statistical methodology using a distribution function frequently used in structural reliability. The proposed distribution function for characterizing the fatigue life is a generalized Weibull distribution function that empirically incorporates load history and damage accumulation.

Predicting Power Plant Equipment Life Using Machine Learning

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Martin Gascon ◽  
Nikhil Kumar ◽  
Rana Ghosh
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
Thermal Strain ◽  
Operating Regime ◽  
Remaining Useful Life ◽  
Machine Learning Techniques ◽  
Power Plant Equipment ◽  
Plant Operations ◽  
Modeling Software

Abstract There are new challenges for plant operators due to the increased share of renewable energy. Plant operators must maintain high reliability and high profits while plants are being required to be more flexible to compensate for the variable generation addition of these renewables into the grid. Plant operators must deal with the thermal strain and the wear-and-tear of such operations. Various models have been proposed in the literature. However, no work has been reported on the development of a robust prediction model. The aim of this study was to determine which machine learning algorithm gives the best estimation of boiler component remaining useful life using plant operations. The flexible operation for all units was estimated using the Intertek hourly MW analysis and damage modeling software Loads Model™. We used several plant features as predictors (such as equipment manufacturer, operating regime, and ramp rates). We tested five different machine learning techniques and found that gradient boost is the best approach to predict the reduction in life span of the plant with over 90% precision.

scholarly journals Microstructural and chemical variation in silica-rich precipitates at the Hellisheiði geothermal power plant

2014 ◽  
Vol 78 (6) ◽  
pp. 1381-1389 ◽  
Author(s):  
D. B. Meier ◽  
E. Gunnlaugsson ◽  
I. Gunnarsson ◽  
B. Jamtveit ◽  
C. L. Peacock ◽  
...  
Keyword(s):  
Amorphous Silica ◽  
Power Plants ◽  
Common Factor ◽  
Study Data ◽  
Fluid Composition ◽  
Silica Layer ◽  
Geothermal Systems ◽  
Pipe Surface ◽  
Geothermal Fluids ◽  
Geothermal Power

Precipitation of amorphous silica (SiO2) in geothermal power plants, although a common factor limiting the efficiency of geothermal energy production, is poorly understood and no universally applicable mitigation strategy to prevent or reduce precipitation is available. This is primarily due to the lack of understanding of the precipitation mechanism of amorphous silica in geothermal systems.In the present study data are presented about microstructures and compositions of precipitates formed on scaling plates inserted at five different locations in the pipelines at the Hellisheiði power station (SW-Iceland). Precipitates on these plates formed over 6 to 8 weeks of immersion in hot (120 or 60ºC), fast-flowing and silica-supersaturated geothermal fluids (~800 ppm of SiO2). Although the composition of the precipitates is fairly homogeneous, with silica being the dominant component and Fe sulfides as a less common phase, the microstructures of the precipitates are highly variable and dependent on the location within the geothermal pipelines. The silica precipitates have grown through aggregation and precipitation of silica particles that precipitated homogeneously in the geothermal fluid. Five main factors were identified that may control the precipitation of silica: (1) temperature, (2) fluid composition, (3) fluid-flow regime, (4) distance along the flow path, and (5) immersion time.On all scaling plates, a corrosion layer was found underlying the silica precipitates indicating that, once formed, the presence of a silica layer probably protects the steel pipe surface against further corrosion. Yet silica precipitates influence the flow of the geothermal fluids and therefore can limit the efficiency of geothermal power stations.

ALLEGHENY LUDLUM AL 29-4C

Alloy Digest ◽  
1993 ◽  
Vol 42 (11) ◽  
Keyword(s):  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Heat Exchangers ◽  
Power Plants ◽  
High Strength ◽  
Corrosion Resistant ◽  
Resistant Alloy ◽  
Corrosion Resistant Alloy ◽  
Geothermal Power

Abstract AL 29-4C is a highly corrosion resistant alloy with a relatively high strength. This combination allows the use of lighter gage tubes, and has led to its use in the brine heat exchangers of geothermal power plants. This datasheet provides information on composition, physical properties, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming and joining. Filing Code: SS-554. Producer or source: Allegheny Ludlum Corporation.

"Recent Patents on the Triboelectrostatic Separation of Fly Ash"

2019 ◽  
Vol 13 ◽  
Author(s):  
Haisheng Li ◽  
Wenping Wang ◽  
Yinghua Chen ◽  
Xinxi Zhang ◽  
Chaoyong Li
Keyword(s):  
Fly Ash ◽  
Power Plants ◽  
High Efficiency ◽  
Separation Efficiency ◽  
High Reliability ◽  
Operating Conditions ◽  
Security Requirements ◽  
Unburned Carbon ◽  
Efficient Operation ◽  
Triboelectrostatic Separation

Background: The fly ash produced by coal-fired power plants is an industrial waste. The environmental pollution problems caused by fly ash have been widely of public environmental concern. As a waste of recoverable resources, it can be used in the field of building materials, agricultural fertilizers, environmental materials, new materials, etc. Unburned carbon content in fly ash has an influence on the performance of resource reuse products. Therefore, it is the key to remove unburned carbon from fly ash. As a physical method, triboelectrostatic separation technology has been widely used because of obvious advantages, such as high-efficiency, simple process, high reliability, without water resources consumption and secondary pollution. Objective: The related patents of fly ash triboelectrostatic separation had been reviewed. The structural characteristics and working principle of these patents are analyzed in detail. The results can provide some meaningful references for the improvement of separation efficiency and optimal design. Methods: Based on the comparative analysis for the latest patents related to fly ash triboelectrostatic separation, the future development is presented. Results: The patents focused on the charging efficiency and separation efficiency. Studies show that remarkable improvements have been achieved for the fly ash triboelectrostatic separation. Some patents have been used in industrial production. Conclusion: According to the current technology status, the researches related to process optimization and anti-interference ability will be beneficial to overcome the influence of operating conditions and complex environment, and meet system security requirements. The intelligent control can not only ensure the process continuity and stability, but also realize the efficient operation and management automatically. Meanwhile, the researchers should pay more attention to the resource utilization of fly ash processed by triboelectrostatic separation.

THE EULERIAN CODE DISGAS (DISPERSION OF GAS) APPLICATION TO H2S DISPERSION EMITTED FROM GEOTHERMAL POWER PLANTS OF TUSCANY (ITALY):

2017 ◽  
Author(s):  
Renato Somma ◽  
◽  
Domenico Granieri ◽  
Claudia Troise ◽  
Carlo Terranova ◽  
...  
Keyword(s):  
Power Plants ◽  
Geothermal Power
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