Advanced Fossil Energy Research: Novel Developments in Sensors and Controls for Fossil Energy Power Generation and Fuel Production Technologies

The summary for the Advanced Fossil Energy Research: Novel Developments in Sensors and Controls for Fossil Energy Power Generation and Fuel Production Technologies grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Energy Technology Laboratory, which is the U.S. government agency offering this grant.

Advanced Fossil Energy Research: Novel Developments in Sensors and Controls for Fossil Energy Power Generation and Fuel Production Technologies: Project Description: 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. The scope of this activity will include soliciting both novel and applied research projects from the three areas of research described below. Objective: The objective of this activity is to competitively solicit projects in the Sensors and Controls area of the National Energy Technology Laboratory's (NETL) Advanced Research (AR) Program. The AR Program sponsors innovative and transformational research and development (R&D) that bridges the gap between basic sciences and applied engineering. These R&D efforts are oriented and prioritized towards the full-scale implementation and operation of the next generation of fossil energy power systems and improvements to existing fossil energy power systems. These advancements are driven by the need for highly efficient, near-zero emission power systems that utilize domestic resources. The Department of Energy (DOE) NETL, in concert with industrial and university collaborators and other government laboratories, have realized advancements in the areas of advanced combustion, gasification, turbines, gas cleaning and separation technologies, and carbon capture. Technology development is also in place for the concurrent production of synthetic fuels from coal and other domestic resources. Advancements in these areas, while highly beneficial, have brought about other challenges that drive the need for new Sensors and Controls technology. The primary challenges being addressed by the AR Sensors and Controls include: 1)Sensing in harsh environments because higher efficiencies and CO2 management result in extreme temperature and pressure conditions and highly reactive and corrosive process conditions; 2)Monitoring the condition of equipment and processes since high system reliability is paramount to having a cost-effective operation. The conditions under which fuel is converted to power includes multiple components where condition monitoring is needed to improve reliability and proactive maintenance; and 3)Management of complexity within a process control system to enable full plant integration and optimization. The NETL AR Program in Sensors and Controls has supported innovations in these areas but seeks to expand its efforts in the select areas outlined below. These are viewed as timely and important technology developments needed to enhance the current technology portfolio. This Funding Opportunity Announcement (FOA) seeks to develop the technologies and approaches necessary to: 1)Develop decision-based algorithms for interfacing heterogeneous sensor networks to enable intelligent real-time decision making capability and allow for appropriate methods for managing complexity at the sensor network level. The full description of this topic is provided under Topic Area A; 2)Develop novel coatings and embedded sensors for online and in-situ assessment of a component or process. The full description of this topic is provided under Topic Area B; and 3)Develop real-time sensor networks that enable multi-dimensional reconstruction of reacting systems and operating components. The full description of this topic is provided under Topic Area C. Applicants with novel approaches to one of these topic areas are encouraged to apply as it is a goal of the Advanced Research Program to support such innovative concepts. Applicants interested in becoming familiar with current AR Sensors and Controls Portfolio may access project descriptions and other reference documents via the link: http://www.netl.doe.gov/technologies/coalpower/advresearch/ref-shelf.html. NETL?s website also provides descriptions of advanced power systems and active research in these areas (http://www.netl.doe.gov/technologies/coalpower/index.html). 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. Application documents must clearly state which topic is being addressed. Technical Topics: Topic Area A: Development and Application of Decision-based Algorithms for Interfacing Heterogeneous Sensor Networks The development of heterogeneous sensor networks has emerged as a primary element of the AR Sensors and Controls Program. The networks are viewed as an enabling approach to manage complexity and dynamics associated with advanced power systems. Sensor networks may consist of tens to thousands of sensors and nodes that have computational and cognitive capability to self-organize and recommend actions to control a process. In context to an advanced power plant, sensor networks may consist of strategically distributed sensors to monitor process operations and assess the condition of process equipment. Sensors and nodes may be wired or wireless, and the primary measurements will include pressure, temperature, flow, strain, gas composition, levels (e.g. tank or silo level), and equipment condition (e.g. fan or belt speed). As more sensors are deployed in an industrial environment, the value of data from individual sensors is diminished and the information developed by a relev ant sensor network becomes important for managing and controlling systems. The objective of this topic is to develop computational techniques and algorithms that can interface with multiple sensors and nodes to transform data into information and enable cognitive-type computation at the network level. Approaches that allow for self-organization of the sensor networks and are scalable are of interest. Utilization of models, heuristics and other novel emerging approaches are encouraged to allow the decision making to occur at the network level. Cognitive and decision making algorithms are analogous to algorithms for robotics control, but the physics and time scales of the power system(s) must be considered. Major systems with an advanced power plant may include steam systems, air & water handling systems, combustion & steam turbines, coal gasification, gas cleaning systems, carbon capture systems, power generation systems, etc. More information about Advanced Power Systems can be found at NETL's website (http://www.netl.doe.gov). Approaches that limit the number of sensors and nodes to less than 10 inputs are not of interest. Approaches that extend the capability of traditional proportional, integral, derivative (PID) control algorithms are also not of interest. Applications submitted under this Topic Area should clearly be marked Topic Area A. Topic Area B: Development of Novel Materials and Designs for Embedded Active Sensing Applications Embedded sensors and smart materials represent an area of sensing that interest many scientific and industrial disciplines. In the area of power generation, embedding sensors and employing smart materials or coatings can lead to greater understanding of the system and allow for better system design and operation. Historically, the Advanced Research Sensors and Controls Program supported developments in materials and sensor designs that enabled survivability in harsh environments. While advancements have been made in these areas, few technologies have emerged as either an embedded sensor or smart material. Development opportunities for embedded sensor design and engineered smart materials extend from biologically-activated approaches and biomimetics to high temperature meta-materials, chemical- or temperature- activated coatings, and optical materials. Research in non-conventional smart systems leverages recent progress in these alternative technologies to develop advanced power generation concepts. The objective of this topic is to develop embedded sensors and smart materials that can be applied to power systems to enable real-time, online assessment of a system or component. Concepts that are novel but offer the potential to be demonstrated at bench-scale at the completion of the project are of interest. Approaches that can be interrogated remotely or are wireless are also of interest. Critical systems and operating components may include embedded sensors or smart materials capable of indicating/measuring strain, temperature, flow, composition, and pressure. The research for these sensing concepts should consider performance factors such as selectivity, accuracy, response time, recovery time and working lifetime. More information about Advanced Power Systems can be found at NETL's website (http://www.netl.doe.gov/technologies/coalpower/index.html). Approaches to sensing that require a probe design and those approaches that do not have the potential to be applied to systems utilized in central industrial power production are outside the scope of this effort and will not be considered under this FOA. Applications submitted under this Topic Area should clearly be marked Topic Area B. Topic Area C: Development of Real-time Sensor Networks to Enable Multi-Dimensional (3-D) Reconstruction of Reacting Systems and Operating Components The use of active sensor networks in power plant operations is a novel approach to improving plant operations and providing more efficient power generation. Sensor networks may consist of tens to thousands of sensors randomly distributed throughout a particular volume or component in an advanced power system with the ability to measure multiple variables such as pressure, flow, strain, temperature, and composition. These networks have the potential to provide a greater understanding of the physical characteristics of a power plant for which to control and efficiently operate future advanced power generation systems. The data gathered by these large sensor networks could be used to reconstruct images of multiple measuring volumes inside reactors or vital power system (or subsystem) components. The objective of this topic is to develop real-time sensor networks that enable multi-dimensional (3-D) reconstruction of reacting systems and operating components. Using the data from these sensor networks, reconstruction may include, but is not limited to, (1) mapping (temperature, pressure, deformation) of internal system components such as turbine blades; (2) cross-sectional, volumetric reconstruction of flows within a gasifier or combustor (single- or multi-phase); (3) cross-sectional, volumetric reconstruction of gas species within a gasifier; and (4) volumetric reconstruction of the combustion flame. The data reconstruction generated from these sensors shall emulate the 3-D graphical representations generated using Computational Fluid Dynamics and Finite Element software. Applications shall include self-organizing features of spatial-temporal relationships for the physical phenomena being observed by the sensor networks. Extrapolation of single point measurements to generate 3-D reconstruction as well as sensor networks that cannot measure an entire cross-sectional volume representative of those found within components of industrial power systems are not of interest. Applications submitted under this Topic Area should clearly be marked Topic Area C.