Results for “Predictive Modeling” — Page 69

DOE OSTI2018

Report for MaRIE Drivers Workshop on needs for energetic material's studies.

Energetic materials (i.e. explosives, propellants, and pyrotechnics) have complex mesoscale features that influence their dynamic response. Direct measurement of the complex mechanical, thermal, and chemical response of energetic materials is critical for improving computational models and enabling predictive capabilities. Many of the physical phenomena of interest in energetic materials cover time and length scales spanning several orders of magnitude. Examples include chemical interactions in the reaction zone, the distribution and evolution of temperature fields, mesoscale deformation in heterogeneous systems, and phase transitions. This is particularly true for spontaneous phenomena, like thermal cook-off. The ability for MaRIE to capture multiple length scales and stochastic phenomena can significantly advance our understanding of energetic materials and yield more realistic, predictive models.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Characterization and CST Batch Contact Equilibrium Testing of Aged TCCR Tank 10H Batch 1A and 2 Process Supernate Samples

The Tank Closure Cesium Removal (TCCR) system uses ion exchange columns filled with Crystalline Silicotitanate (CST) media to process radioactive waste solutions for the removal of Cs-137. TCCR currently focuses on dissolving Savannah River Site (SRS) Tank 10H waste (primarily sodium saltcake solids) within the tank followed by at-tank ion exchange column treatment. Previous equilibrium batch contact tests conducted in the SRNL Shielded Cells laboratory and associated analyses indicated that CST cesium removal performance was lower than predicted by ZAM modeling for TCCR Tank 10H Production Supernate Batch 1A. An average cesium distribution coefficient of 1,948 mL/g which corresponds to 94.1% Cs + removal was observed for Batch 1A supernate at 38 °C in January of 2019 using laboratory pretreated IONSIV™ R9120-B a CST from Lot #209900034 (predominant TCCR CST batch) at a phase ratio of 122 mL/g. This cesium loading was approximately 32% lower than model predictions, presumably due to ion exchange competition or fouling associated with alkaline earth or transition metals present in the solutions at low levels. The current testing was designed to: 1) evaluate whether supernate compositional changes during storage would result in improved cesium removal performance with CST, and 2) assist in media selection and handling protocols for future TCCR columns by evaluating several CST manufacturer batches as well as samples of one CST batch which had been pretreated using two different methods (field and laboratory methods).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

AI Scaling Laws for Extremes (AISLE)

Focal Area(s) Using artificial intelligence to understand the physics of hydrologic events across temporal and spatial scales, to assess their representation in climate models, and to predict their response to climate warming. This covers both the prescribed foci of (2) predictive modeling using a hierarchy of models and (3) insight gleaned from complex data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Mobility and Energy Improvements Realized through Prediction-based Vehicle Powertrain Control and Traffic Management (Research Performance Final Report)

The goal of this project was to utilize individual vehicle and systems-level transportation big data to develop real-world implementable techniques for energy efficiency. We collected a real world dataset in Fort Collins, CO using technology that is currently available. This dataset was used to (1) create individual vehicle prediction models and emissions models using cutting-edge artificial intelligence (AI) techniques, (2) create traffic prediction models, (3) create boundary condition constraints for optimal trajectory derivation, and (4) develop a novel Mobility Energy Productivity (MEP) model for Fort Collins, CO at a fidelity and flexibility which did not previously exist. Each of these techniques is novel but the most interesting results occurred from the intersection of all techniques. It was found that when optimal vehicle control is combined with optimal traffic light control, that significant energy efficiency improvements that do not compromise travel time are available.

42 ENGINEERING↗

DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗

DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗

DOE OSTI2016

SN REFSDAL: PHOTOMETRY AND TIME DELAY MEASUREMENTS OF THE FIRST EINSTEIN CROSS SUPERNOVA

We present the first year of Hubble Space Telescope imaging of the unique supernova (SN) “Refsdal,” a gravitationally lensed SN at z = 1.488 ± 0.001 with multiple images behind the galaxy cluster MACS J1149.6+2223. The first four observed images of SN Refsdal (images S1–S4) exhibited a slow rise (over ∼150 days) to reach a broad peak brightness around 2015 April 20. Using a set of light curve templates constructed from SN 1987A-like peculiar Type II SNe, we measure time delays for the four images relative to S1 of 4 ± 4 (for S2), 2 ± 5 (S3), and 24 ± 7 days (S4). The measured magnification ratios relative to S1 are 1.15 ± 0.05 (S2), 1.01 ± 0.04 (S3), and 0.34 ± 0.02 (S4). None of the template light curves fully captures the photometric behavior of SN Refsdal, so we also derive complementary measurements for these parameters using polynomials to represent the intrinsic light curve shape. These more flexible fits deliver fully consistent time delays of 7 ± 2 (S2), 0.6 ± 3 (S3), and 27 ± 8 days (S4). The lensing magnification ratios are similarly consistent, measured as 1.17 ± 0.02 (S2), 1.00 ± 0.01 (S3), and 0.38 ± 0.02 (S4). We compare these measurements against published predictions from lens models, and find that the majority of model predictions are in very good agreement with our measurements. Finally, we discuss avenues for future improvement of time delay measurements—both for SN Refsdal and for other strongly lensed SNe yet to come.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Predicting Student Success using Analytics in Course Learning Management Systems

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Educational data mining↗

DOE OSTI2018

Numerical Simulation and Model Analyses of In-situ Biogeochemical Remediation of Chromium at the LANL - 18306

Environmental management problems associated with contamination of groundwater resources with heavy metals (such as chromium) typically require complex biogeochemical model simulations and analysis of in situ remediation. Frequently, the in situ remediation is the only viable treatment option. In these cases, a three-dimensional reactive transport model of relevant biogeochemical processes is invaluable. Recently, we have developed a novel unique biogeochemical simulator called CHROTRAN (http://chrotran.lanl.gov; Hansen et al. 2017, Vesselinov et al. 2018a) capable to represent the full three-dimensional dynamics of processes associated with in situ biogeochemical remediation of heavy metals in heterogeneous aquifers. CHROTRAN accounts for spatial and temporal transients of (1) the heavy metal to be remediated, (2) introduced amendments (e.g., through well injection), and (3) biomass growth and decay. The introduced amendments can include (1) an electron donor (e.g., molasses), (2) a nontoxic-conservative bio-inhibitor (e.g., ethanol), and (3) a biocide (e.g., dithionite). In addition, direct abiotic reduction by donor-metal interaction as well as donor-driven biomass growth and bio-reduction are modeled in CHROTRAN. Other critical processes, such as donor sorption, bio-fouling and biomass death are also modeled. CHROTRAN can simulate the groundwater flow dynamics introduced due a series of injection and extraction wells and account for reduction of aquifer permeability near the wells due bio clogging. CHROTRAN is based on the existing PFLOTRAN (http://pflotran.org; Lichtner et al, 2017a) code framework, taking advantage of the reaction interface allowing for implementation of complex model features and leverage other facets of PFLOTRAN, such as multi-component geochemistry and high-performance computing. Here, we present numerical simulations and model analyses related to the Los Alamos National Laboratory (LANL) chromium site. The analyses are performed by coupling of CHROTRAN with MADS (Model Analysis and Decision Support; http://mads.lanl.gov; Vesselinov et al., 2018b). We present analyses related to uncertainty quantification of model predictions, sensitivity of model parameters and remedial decision support. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Second-order adjoint sensitivity analysis methodology (2ND-ASAM) for computing exactly and efficiently first- and second-order sensitivities in large-scale systems - 14356

This work presents the second-order adjoint sensitivity analysis methodology (2.-ASAM) for computing exactly and efficiently the second-order functional derivatives ('sensitivities') of system responses to the system's model parameters. For a physical system comprising N{sub α} parameters and N{sub r} responses, forward methods require a total of at least (N{sup 2}{sub α}/2+3N{sub α}/2) large-scale computations for obtaining all of the first- and second-order sensitivities, for all N{sub r} system responses. On the other hand, for one functional-type system response, the 2.-ASAM requires one large-scale computation using the first-level adjoint sensitivity system (1.-LASS) for obtaining all of the first-order sensitivities, followed by at most (2N{sub α}+1) large-scale computations using the second-level adjoint sensitivity systems (2.-LASS) for obtaining all of the second-order sensitivities. The second-order sensitivities contribute decisively to causing asymmetries in the response distribution, since they are the leading contributors to the third-order response correlations (skewness). They also cause the 'expected value of the response' to differ from the 'computed nominal value of the response'. The implementation of the 2.-ASAM requires very little additional effort beyond the construction of the adjoint sensitivity system needed for computing the first-order sensitivities. Only the sources on the right-sides of the diffusion (differential) operator needed to be modified; the left-side of the differential equations (and hence the 'solver' in large-scale practical applications) remain unchanged. The application of the 2.-ASAM is illustrated on a benchmark heat conduction/convection problem, which makes transparent the underlying mathematical derivations. For this illustrative problem, 4 'large-scale' adjoint computations suffice for computing exactly all of the 6 first- and 21 distinct second-order derivatives. The 2.-ASAM presented in this work should enable the hitherto very difficult, if not intractable, exact computation of all of the second-order response sensitivities for large-systems involving many parameters, which is expected to affect significantly other fields that need efficiently computed second-order response sensitivities, such as optimization, data assimilation/adjustment, model calibration, and predictive modeling. (author)

42 ENGINEERING↗

DOE OSTI2025

Interesting Graphite Facts

Interesting facts about graphite. Graphite within HTRs and how long they will last, potential issues prediction, model building to predict expected behavior, and frequent and thorough inspection and monitoring strategy.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Geothermal Heat Pump System Showcase: Short-Term Validation of Borehole Heat Exchanger Performance from Field Data to Numerical Modeling: Preprint

Since 2011, a geothermal heat pump (GHP) system has been operating to provide space heating and cooling for the Solar Radiation and Research Laboratory building at the National Laboratory of the Rockies (NLR) in Golden, Colorado. The system consists of 23 vertical boreholes, each extending to a depth of 300 ft (91 m), connected to 11 water-to-air heat pump units and four circulation pumps. Between fiscal years 2023 and 2025, additional power meters and temperature sensors were retrofitted to support detailed system performance assessment and model development. This study presents preliminary monitoring results and the development of an initial numerical model of the borehole heat exchanger field. The model incorporated site-specific geometry, ground thermal properties derived from thermal response tests, and ambient temperatures, and simulated system behavior over a representative operating day in September. Model predictions of outlet temperatures were compared against corresponding field measurements. Results showed that modeling initialized with a simplified linear subsurface temperature gradient presents systematic discrepancies in outlet temperature, whereas incorporating depth-resolved borehole temperature measurements for initialization yields substantially improved agreement with observations. The findings highlight the sensitivity of short-term predictive modeling to the representation of initial subsurface thermal conditions and underscore the value of high-resolution field measurements for model calibration and validation. These preliminary results inform ongoing efforts to extend the modeling framework to longer time horizons and to refine monitoring and modeling strategies that support the design guidance and operational optimization of GHP systems in research and commercial buildings.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

OPERA models for predicting physicochemical properties and environmental fate endpoints

The collection of chemical structure information and associated experimental data for quantitative structure–activity/ property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Probabilistic modeling and prediction of out-of-plane unidirectional composite lamina properties

Computational simulation provides an efficient means to predict the behavior of customized hybrid material configurations using validated, physics-based models. One limitation to this approach is the quality and quantity of available data to characterize the many constituent input properties. Therefore, a systematic approach to identify the most influential parameters on the hybrid behavior and quantify the corresponding uncertainty in predictive capabilities is required. Here, an approach using Bayesian multimodel inference and imprecise global sensitivity analysis is presented to investigate the effects of sparse constituent data on the prediction of composite material properties. The methodology allows the identification, using quantified uncertainties, of the most influential constituent material parameters for specified homogenized properties. This sensitivity analysis further enables a dimension reduction when assessing the influence of uncertainties on material properties and can be used to inform testing programs of the constituent properties that require additional testing/data collection in order to minimize uncertainty in macro-scale composite properties. The methodology is specifically demonstrated on the prediction and sensitivity analysis of out-of-plane mechanical properties of a unidirectional lamina.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Investigation of the Effect of Injected CO 2 on the Morrow B Sandstone through Laboratory Batch Reaction Experiments: Implications for CO 2 Sequestration in the Farnsworth Unit, Northern Texas, USA

About one million tons of CO 2 have been injected into the Farnsworth unit to date. The target reservoir for CO 2 injection is the Morrow B Sandstone, which is primarily made of quartz with lesser amounts of albite, calcite, chlorite, and clay minerals. The impact of CO 2 injection on the mineralogy, porosity, and pore water composition of the Morrow B Sandstone is a major concern. Although numerical modeling studies suggest that porosity changes will be minimal, significant alterations to mineralogy and pore water composition are expected. Given the implications for CO 2 storage effectiveness and risk assessment, it is crucial to verify the accuracy of theoretical model predictions through laboratory experiments. To this end, batch reaction experiments were conducted to model conditions near an injection well in the Morrow B Sandstone and at locations further away, where the CO 2 has been diluted by formation water. The laboratory experiments involved submerging thin sections of both coarse- and fine-grained facies of the Morrow B Sandstone in formation water samples with varying levels of CO 2 . The experiments were conducted at the reservoir temperature of 75 °C. Two experimental runs were conducted, one lasting for 61 days and the other for 72 days. The initial fluid composition used in the second run was the same as in the first. The mineralogy changes in the thin sections were analyzed using SEM and the Tescan Integrated Mineral Analyzer (TIMA), while changes in the composition of the formation water were determined using ICP-AES. During each experiment, a thin layer of white fine-grained particles consisting mainly of dolomite and silica formed on the surface of the thin sections, leading to significant reductions in Ca, Mg, and Sr in the formation water. This outcome is consistent with numerical model predictions that dolomite would be the primary mineral that would react with injected CO 2 and that silica would be oversaturated in the formation water. Changes in mineral abundance in the thin sections themselves were much less systematic than in the theoretical modeling experiments, perhaps reflecting heterogeneities in the mineral grain size surface area to volume ratios and mineral distributions in the thin sections not considered in the numerical models.

58 GEOSCIENCES↗

DOE OSTI2019

Benchmarking the accuracy of coverage-dependent models: adsorption and desorption of benzene on Pt (111) and Pt 3 Sn (111) from first principles

Bimetallic catalysts have demonstrated properties favorable for upgrading biofuel through catalytic hydrodeoxygenation. However, the design and optimization of such bimetallic catalysts requires the ability to construct accurate, predictive models of these systems. To generate a model that predicts the kinetic behavior of benzene adsorbed on Pt (111) and a Pt 3 Sn (111) surface alloy (Pt 3 Sn (111)), the adsorption of benzene was studied for a wide range of benzene coverages on both surfaces using density functional theory (DFT) calculations. The adsorption energy of benzene was found to correlate linearly with benzene coverage on Pt (111) and Pt 3 Sn (111); both surfaces exhibited net repulsive lateral interactions. Through an analysis of the d-band properties of the metal surface, it was determined that the coverage dependence is a consequence of the electronic interactions between benzene and the surface. The linear coverage dependence of the adsorption energy allowed us to quantify the influence of the lateral interactions on the heat of adsorption and temperature programmed desorption (TPD) spectra using a mean-field model. A comparison of our simulated TPD to experiment showed that this mean-field model adequately reproduces the desorption behavior of benzene on Pt (111) and Pt 3 Sn (111). In particular, the TPD correctly exhibits a broadening desorption peak as the initial coverage of benzene increases on Pt (111) as well as and a low temperature desorption peak on Pt 3 Sn (111). Furthermore, due to the sensitivity of the TPD peak temperature to the desorption energy, precise alignment of experimental and theoretical TPD spectra demands an accurate calculation of the adsorption energy. Therefore, an analysis of the effect of the exchange-correlation functional on TPD modeling is presented. Through this work, we show the necessity of incorporating lateral interactions into theoretical models in order to correctly predict experimental behavior.

09 BIOMASS FUELS↗

DOE OSTI2023

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Report

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of subcritical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Program Review

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of sub-critical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗