[serdar]

Link to opportunity: http://bit.ly/cizktR

Link to opportunity: http://bit.ly/cizktR

The objective of this activity is to competitively solicit projects in the Coal Utilization Science area of the United States Department of Energy (DOE) National Energy Technology Laboratory’s (NETL) Advanced Research Program for the improvement of next generation fossil energy power systems. The goal of the Advanced Research – Coal Utilization Science Program is to conduct research that supports the development of technologies for clean, efficient electric power generation. This supports the DOE Strategic Plan by providing core competencies related to advanced power system technologies. NETL is seeking innovative research and development of sensor and control systems to support the full-scale implementation and operation of highly efficient, near zero emission power generation technologies. Power generation plants, both existing and emerging systems, have critical assets within the plant that contribute directly to their reliability and availability. Monitoring and managing plant assets are growing businesses for plant owners and operators, original equipment manufacturers, and third party vendors with contributing technology in this area. A single plant or fleet of plants within an organization can benefit directly from a robust asset management program or condition monitoring system primarily through employing preventative or just-in-time maintenance and effectively utilizing planned outages. Benefits include a reduction in the number of forced outages, reduction in time elapsed during planned outages, increase in the time between parts replacement, rebuild/refurbishment, and avoidance of unplanned events. These potential benefits obviously create financial incentives to the plant but may also impact the way in which the plant or unit is dispatched and may also contribute to the overall plant performance including improved heat rate and environmental performance. For example, minimizing water wall tube wastage in boilers may also aid in managing slag accumulation, heat transfer, combustion efficiency, and overall heat rate performance of a boiler. This is just one example of the potential benefits for the existing coal-fired power plant fleet, a fleet that provides over 50% of America?s electric power. For emerging power generation systems (please see NETL website references below), the conditions under which coal is utilized to produce electric power are more demanding and components within the plant must be operated closer to tolerances and within smaller operating margins in order to achieve the goals of high efficiency and near-zero emissions. As a result, these emerging high performance power plants may not offer the same overall reliability or availability as traditional coal-fired plants. A robust and advanced condition monitoring network is thus important to enhance the reliability and availability of emerging high efficiency near-zero emission systems. This Funding Opportunity Announcement (FOA) seeks to develop the technologies and approaches necessary to create such robust and advanced condition monitoring networks. Specifically, it seeks novel approaches in model development and validation, sensor materials, and wireless, self powered sensors that directly relate to monitoring the status of equipment, materials degradation, and process conditions that impact the overall health of a component or system. Of interest are approaches that would be applicable to emerging power systems and those that may also offer near term benefits to existing power generation facilities. Novel approaches to condition monitoring represent an ongoing effort within DOE NETL’s Advanced Research (AR) Program in Sensors and Controls (S&C). The AR S&C Program seeks to enhance the existing portfolio of research and development projects by building upon advancements in the areas of harsh environment sensing, novel approaches to sensor networking, and model predictive control type algorithms. The topics described below represent separate development efforts within the overarching theme of advanced condition monitoring. However, the various novel approaches, when considered in an integrated fashion, may offer the basis for the creation of a unit-specific condition monitoring network. Such a system would utilize a heterogeneous sensor network employing models and algorithms that provide prognostic capability and suggest preventative actions directly benefiting the management of assets within coal-fired power generation systems. Applicants may only respond to one topic per application. Multiple applications from the same applicant are permitted and teaming within a single application is also permitted. Please state clearly in the application which topic is being addressed. Topic Area A: Development of Prognostic Lifing and Preventative Maintenance Models Applications in this area are sought for the development of prognostic remaining life models and predictive maintenance algorithms tailored for units or systems within advanced Fossil Energy power generation facilities. Emerging advanced power systems will be costly and may be operated conservatively in order to extend the life of equipment within the units or systems. Often, the actions taken to extend component life equates to operating the system under conservative margins which reduces the full potential of the system when targeting efficiency and environmental performance. By developing and employing remaining life models, advanced power systems may be operated more closely to their design tolerances. Models based on first principals are encouraged. The ability to incorporate online data and operational information for the purposes of validation, adoption of learning/neural-based algorithms, and prediction of time-to-failure is considered central to a robust lifing m odel. To ensure a reasonable development timeline, applicants should select a specific unit or system from within the plant in which to focus their efforts. Approaches based on developments in other industries are acceptable. Note: DOE intends to limit the ability of recipients under this topic area to copyright generated software whenever said software is determined by DOE to be essential to its ongoing efforts to develop publicly-available modeling software. Topic Area B: Model-Based Sensor Placement for Component Condition Monitoring Applications in this area are sought for the development and demonstration of models and simulations to determine optimized sensor locations and types that can support the creation of a robust condition monitoring network. These approaches can be based on pre-existing representations of a Fossil Energy-derived power generation component or system. Novel approaches, which may not have an existing base, for determining sensor placement at the unit or system level are also acceptable. Current power system operators generally install sensors where throughputs are available, limiting the benefit sensors can provide. In order to promote information-rich control strategies necessary to reduce costs, minimize plant interference, and increase industrial acceptance, sensors require effective placement and flexible sensing capabilities. Computational models have proven to be valuable resources in optimization of sensor placement and development of flexible sensing systems. At the conclusion of the development effort, applicants must be able to virtually demonstrate the ability of the model or algorithm to determine optimal sensor locations and types for a particulate component or system. These models should also determine the optimal number of sensors needed to monitor the component or system given a unit/system specific geometry, size, complexity, and operating conditions and should maximize the effectiveness of the sensor network. Approaches may include the use of hybrid sensor architectures, when necessary, in order to sense multiple elements (pressure, temperature, wear, etc.) at a single location to compensate for sensor damage or loss during operation. Note: DOE intends to limit the ability of recipients under this topic area to copyright generated software whenever said software is determined by DOE to be essential to its ongoing efforts to develop publicly-available modeling software. Topic Area C: Novel Sensor Development for Online Condition Monitoring Applications in this area are sought for the development of an integrated self-powered, networked monitoring device. Approaches should incorporate emerging technologies in harsh environment sensing, wireless networking and communication, and energy harvesting for powering the sensor. Targeted sensor measurements include temperature, corrosion, vibration, proximity of rotating equipment, and stress/strain under suitable conditions for a selected power generation unit or system. Multiple measurements within the same sensor device may also be considered. The sensor portion of the device must be able to function reliably under the harsh conditions of a power system. Sensors that cannot operate within a reasonable range of temperatures between 500?C – 1300?C will not be considered. Wireless communication and networking should consider the industrial setting that devices are intended for and are encouraged to comply with standards and protocols set forth for secure and reliable communication. The ability to harvest energy from the generating unit itself is of interest. Within a power plant, there are numerous locations where waste heat and vibration are available as sources of power sufficient to support the operation of a sensor device. Approaches to energy harvesting must consider the suitability for survival in harsh industrial environments. Applicants should relate the proposed sensor development to value within a condition monitoring network or importance to the unit/system. Examples include the value of online tip clearance measurement to advanced turbine system operation or the value of online corrosion monitoring to boiler tube condition in Advanced Combustion/Boiler Systems. Topic Area D: Transformational Approaches for Monitoring Refractory or Coatings Applications in this area are sought for fundamental and transformative approaches to online monitoring of refractory used in coal gasifiers or coatings used in turbines. Coal gasifiers operate under extreme conditions including high temperature (1200?C-1400?C with excursions up to 1600?C) and high pressure (up to 1000 psi) to convert coal to synthesis gas and molten slag. The formation of molten slag under extreme conditions including highly reducing, erosive, and corrosive conditions requires that the gasification vessel be protected with multiple layers of refractory material. These refractory materials degrade over time and in response to coal slag formation and process conditions. Refractory liners are required to be replaced at predetermined intervals and the costs associated with replacement of the refractory materials and lost production time are significant. The ability to monitor the integrity of the refractory materials during gasifier operation and to accurate ly estimate remaining life of the refractory materials would contribute significantly to improving the overall operational performance and reliability of coal gasifiers. Novel approaches to assess the condition of the innermost and exposed layer of refractory are of primary interest. Novel approaches that enable a condition quality assessment, which minimize the number of access points to the gasifier are also of interest. For example, an ideal measurement capability would be to detect cracking and spallation of a bricked liner within a cylindrical geometry so that detection of hot spots can be made before detrimental impacts on the liner are realized. Practically, gasifier operators must be able to predict refractory life/replacement schedule without process upset or shutdown. That ability depends on a continuous understanding of the overall thickness or remaining life of the innermost refractory liner. Applications are also sought for novel approaches to assess the status of coatings used in advanced combustion turbine systems. Degradation of coatings used on blades and other components within combustion turbines is a primary concern to monitoring the overall condition of the turbine system. Many turbines use thermal barrier coatings in the first set of turbine blades that are exposed to the harshest combustion conditions (up to 1400?C and 30:1 pressure ratios). Ideal monitoring of the coatings is one in which the full coating surface can be assessed and does not change or damage the coating itself. For turbine applications, a non contact two-dimensional mapping of the coatings is highly desirable.

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