temperature gradient was found to change directions on all nodes in SOEC solid materials, with the greatest impact on the upstream nodes. The SOEC model was also tested at the thermal neutral voltage using actual process air flow parameters as variable model inputs. Variable process air temperatures were found to induce alternating local temperature gradients on SOEC solid materials. These are new operational mechanisms for SOEC degradation relevant for load following operational modes yet distinct from previous reports. To mitigate these unfavorable features, the SOEC can be operated at voltages that are slightly (+/- 20 mV) deviated from the thermal neutral voltage. The corresponding net thermal energy change was less than 1.6% of the electric power consumption. This 1D real-time SOEC model established the basis of cyber-physical simulation of SOEC hybrid systems. System integration and dynamic operability between SOEC and balance-of-plant (BoP) components are major technical challenges before realizing rapid load following of SOEC systems. Cyber-physical simulation (CPS) is a leading-edge digital engineering approach and is regarded as the next step beyond Digital Twins. CPS approach can be used to research SOEC system integration and develop dynamic controls prior to actual pilot testing without using a real SOEC. To seamlessly couple with BoP hardware and access non-observable operational parameters (e.g., local temperature gradient) during transients, a distributed one-dimensional (1D) real-time SOEC model was developed. Its real-time execution was demonstrated for 20 to 640 nodes at the fixed time step of 5 ms. A higher excess air ratio enabled smaller local temperature gradients on SOEC solid materials and faster transients upon current density step change from 0.15 to 0.55 A cm- 2. During the transients, the magnitude of the peak temperature gradient nearly doubled in 10 s from -3.5 to -5.9 degrees C cm- 1. This represents a significant operating risk that can impact the dynamic operability of SOEC systems. In addition, the local

In the broad field of fault detection, approaches utilizing process conditions are established for systems with adequate datasets. However, systems including recently commissioned and novel processes have limited datasets available for model-based fault detection. Moreover, these systems have far greater proportions of normal operating data than adequate fault examples due to the time for which they have been operated. In this work, a combined hybrid framework for fault detection is developed that enables augmentation of the limited dataset available with HAZOP data, allowing for the utilization of both human expert knowledge and generated pseudo-process data. Additionally, the generation of artificial data is performed for reducing false positives in adversarial training. A semi-supervised distance variant of center loss is used to improve the consistency of deep feature activations from paired and unpaired data. A comparison between the proposed approach and an approach utilizing only process data in the limited data case is presented. Overall, the proposed approach shows 4.1 % and 8.8 % improvements in average detection rate when compared to the state-of-the-art supervised method for the Tennessee Eastman process and subcritical coal-fired power plant case studies, respectively, enabling the use of unlabeled data to supplement labeled process data for fault detection.

Abstract This work presents a novel study for identifying alterations in the control states of a desuperheater system based on real closed-loop data from a coal-fired power plant operating under various loads using linear and nonlinear system identification techniques. Specifically, Transfer Functions (TFs) and Gaussian Processes within a Nonlinear AutoRegressive eXogenous model (GP-NARX) are utilized. The desuperheater system comprises two units, north and south, each modeled as a single-input single-output (SISO) system based on spray valve positions and outlet temperatures. To identify changes in the control states using TFs, deviations in the coefficients of three poles and two zeros transfer functions are analyzed. Significant shifts in the control states of the north desuperheater are observed when transitioning from nominal to half and low loads, with deviations of up to four orders of magnitude. Substantial changes in control states are also observed for the south desuperheater when moving from nominal to low load, with a deviation in the coefficients of up to five orders of magnitude, whereas the transition from nominal to half load shows a smaller deviation of up to three orders of magnitude. In the GP-NARX approach, model uncertainties are used to indicate the changes in the control states. The south desuperheater showed a significant uncertainty of up to 8°F from the nominal to the low load, evidencing a change in the control states. Regarding the north desuperheater, increased uncertainty, up to 6°F, is also observed but in shorter time intervals when compared to the south desuperheater. Ultimately, this work shows that both approaches can be used as a basis for system identification, employing real closed-loop power plant data.

Power plants are increasingly required to cycle to meet dispatch demand. Performance degradation in a recuperative gas turbine power system designed for hybridization was studied using start-up transients over a six-year period. The advanced power system consisted of a gas turbine, compressor, exhaust gas recuperator, hybrid system plenum, natural gas combustor, generator, and electric load bank. A Python script was used to align, filter, organize, transform, and summarize process data from facility start-up sequences. Data analytics including principal component and discriminant analyses were applied to identify potential sources of performance degradation for hybrid power systems in this hybrid configuration. Over 177 start-up tests from 103 datasets were considered in the analysis. The fuel flow increased with timestamp for the same start-up sequence, indicating a loss in efficiency. The decrease in efficiency could be detected in similar variations in pressures and temperatures around the turbine, but the changes in these indicators were unaffected by replacing the turbine. It was inferred from the results that a leak in the compressed gas system was responsible for the degradation. This multivariable methodology can be readily adapted to investigate the effects of system component wear and tear resulting in a decrease in system performance.

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coal-fired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis (PCA), Canonical Variate, and Fisher Discriminant Analysis (CV-FDA) were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. About 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables. (c) 2020 Elsevier Ltd. All rights reserved.

Due to the increased penetration of renewable power sources into the electric grid, the current number of existing coal-fired power plants shifting from baseload to load-following operations has also increased. This shift creates challenges especially for the power industry as coal-fired power plants were not designed for ramping situations, leading to added stress on major components of these plants. This stress causes the system to degrade over time and eventually develop faults. As boilers are still the primary component that fails and causes forced outages, accurate characterization of faults and fractures of boilers is now becoming increasingly critical to reduce plant downtime and extend the plant life during cycling operations. This work focuses on modeling sections of a subcritical coal-fired power plant and proposes algorithms for fault detection in MATLAB/Simulink. The developed model simulates the process dynamics including steam and feedwater flow regulating valves, drum-boiler, and heat rate on the regulation of pressure, drum level and production of saturated steam. The model also simulates the dynamics of superheaters for increasing the energy content of steam, and a spray section for regulating the temperature of steam upstream of the high-pressure turbine to allow for power output adjustment within a given valve operating range. Furthermore, an extension to a leak detection framework proposed by co-authors in previous work is explored. The new framework includes a modification to the threshold analysis portion of the previous work. The extended framework is then applied to a subcritical coal-fired power plant model for leak detection. In particular, this framework analyzes mismatches or deviations in expected plant dynamics with an identified transfer function model. The mismatch is flagged after it exceeds a threshold. The developed algorithm thus aids in rapid detection of faults to reduce impeded plant performance. The results of this work will support real plant operations by providing an accurate characterization of faults in the operation of coal-fired power plants.

Dynamic analyses have been applied to the temporal signals from an Electro Capacitance Volume Tomography instrument located nearmid-height on the riser of an industrial-scale cold-flow circulating fluidized bed to characterize gas-solids flow behavior in the riser. Twelve capacitance electrodes surround the cylindrical riser over a height of 1.3 m. The instrument used a neural network deconvolution algorithm to determine the spatially resolved solids fraction recorded at 52 Hz. Experiments were carried out over a range of gas and solids flows in the transport regime using a Geldart Group B bed material, high density polyethylene with mean particle size of 880 mu m. The radial solids distribution was found to vary from one-time step to the next between profiles typical of laminar and turbulent flow. The duration of time spent in each of these flow profiles depended upon the operating regime - dilute, core-annular, or fast fluidized bed. The chaotic structure of the temporal data was characterized using the three conventional approaches: the first 4 moments from the distribution of signal in time, system memory parameters from the autocorrelation function and the Hurst exponent, and analysis of the correlation entropy and correlation dimension of the attractor. These signal analysis techniques were used to clearly distinguish differences between different transport operating regimes. Specifically, it was experimentally observed that a riser transitions from core annular flow profile to dilute and dense regimes via increasing the frequency of short term transients to either dilute or dense flow profiles, respectively. A regime map was generated based upon these dynamics using solids flux and gas velocity axes. Fast fluidized, core annular, and dilute each exhibited different degree of dynamic characteristics typical of fluid dominated or particle compromising behavior. It should be noted that the magnitude for the different statistics? was in the same range regardless of the regime, it was the radial profile for the statistic that changed and subsequently identified that there was a change in the regime. Finally, a reduced regime map was developed consisting of plotting the gas velocity normalized by the upper transport velocity versus the solids flux normalized by the saturation carrying capacity. The use of this reduced plot allowed the data from widely different conditions to be plotted and compared on the same graph. Note that in many instances, some of the statistics identified the operating point as being in one regime while others indicated that it was in another indicating a transition region between dilute or core annular regimes and between the core annular and fast fluidization regimes. This now provides a tool that can be used to optimize process performance, identify changes in operating states, or replicate process dynamics during process scaling or changing operating parameters. Published by Elsevier B.V.

Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (≪ 1% misclassified observations) and were able to distinguish normal operations from those time periods with steam leaks even while operating the power plants in power cycling mode. Multivariable statistical analyses, including Principal Component (PCA), cluster, and Fischer Discriminant Analysis (FDA) were used to characterize the leak occurrence. Normal and operational states with steam leaks were provided in the original process datasets. These datasets were split into two different groups for training and validation purposes. The data were sorted chronologically, and every third observation was assigned to training the Discriminant Function Model (DFM) while the rest were reserved for validation. PCA was used to reduce dimensionality of the original datasets. Canonical and FDA analyses were used to investigate the relationship between process variables. The outcome of the analyses revealed that nearly 35,000 observations were classified correctly; less than 0.05% of total observations were misclassified to be leaking, i.e. both false positives and false negatives.

An experimental study was conducted in a large scale circulating fluidized bed (0.3 m diameterx15.5 m tall) equipped with nonmechanical L-valve (0.25 m diameterx1.52 m long) varying the riser superficial gas velocity (U-g), bed inventory (m), L-valve aeration velocity (U-Lvalve), and standpipe aeration velocity (U-more). The effects of these operational parameters on pressure drop were investigated in each of the different components in the CFB and on the solid circulation rate with two different Group B particles (180 mu m glass bead and 750 pm phosphorescent polyethylene beads, PPE). Tests were carried out at ambient temperature and pressure conditions using air as the fluidization gas. The operating parameters were shown to significantly affect pressure drops across various sections of the CFB due to redistribution of solids within the system. The pressure drop in the horizontal section of the L-valve was affected by aeration rates in addition to the solid flow rate reported in previous studies of smaller L-valves without aeration. A new dimensionless semi-empirical correlation was able to explain >98% of the variance between pressure drop across the horizontal section of the L-valve and operating parameters for these Group B particles. The results of present study indicated that the nonmechanical L-valve cannot be isolated from the CFB system for performance evaluation. Published by Elsevier B.V.

A cyber-physical observer (CPO) is under development to control the operations of a fluidized bed-chemical looping reactor (FB-CLR) pilot plant. The cyber-physical approach is a novel means to combine computational modeling with hardware to integrate complex components in a dynamic fashion. Experience from pilot plant operations is that control of the solids flows and bed inventories are often challenging and may result in the need to reload oxygen carrier particles multiple times during a weeklong test campaign. The solids transfer between process units in coupled fluidized bed systems cannot be simulated with sufficient speed or accuracy to identify slugging, choking, flooding, or the hysteresis events that can occur near incipient fluidization leading to flow disruption/reversal that upset process operations. The CPO is being designed to duplicate, or mirror, the multiphase flow system in real time hardware.

This paper presents the design, development, and operation of a reactor system for CO2 capture. Modifications were implemented to address differences in sorbent from 180 μm Geldart group B to 115 μm Geldart group A material; operational issues were discovered during experimental trials. The major obstacle in system operation was the ability to maintain a constant circulation of a solid sorbent stemming from this change in sorbent material. The system consisted of four fluid beds, through which a polyamine impregnated sorbent was circulated and adsorption, preheat, regeneration, and cooling processes occurred. Pressure transducers, thermocouples, gas flow meters, and gas composition instrumentation were used to characterize thermal, hydrodynamic, and gas adsorption performance in this integrated unit. A series of shakedown tests were performed and the configuration altered to meet the needs of the sorbent performance and achieve desired target capture efficiencies. Methods were identified, tested, and applied to continuously monitor critical operating parameters including solids circulation rate, adsorbed and desorbed CO2, solids inventories, and pressures. The working capacity and CO2 capture efficiency were used to assess sorbent performance while CO2 closure was used to define data quality and approach to steady-state. Testing demonstrated >90% capture efficiencies and identified the regenerator to be the process step limiting throughput. Sorbent performance was found to be related to the reactant stoichiometry. A stochastic model with an exponential dependence on the relative CO2/amine concentration was used to describe 90% of the variance in the data.

There is a paucity of riser data for industrial scale units, particularly with regard to the solids fraction. This is especially true for detailed spatially distributed values. To alleviate this problem, NETL installed a 0.445m tall electrical capacitance volume tomography (ECVT) sensor 9.66m from the gas distributor of its industrial size (15.45m high and 0.3m diameter) circulating fluidized bed (CFB) cold model. A series of tests were conducted to investigate the gas-solid flow behavior using high density polyethylene (PPE) solids. Static electricity was successfully minimized using Larostat and humidification. Time averaged radial solid fractions profiles are presented and discussed. The time and spatially averaged solid fractions measured by the ECVT agree well with estimates from the pressure drop. The annular thickness was measured and found to increase with increases in the solids flow rate and decrease with increases in the gas velocity. Comparisons of the annular thickness and solids fraction as determined from the ECVT unit were compared to existing correlations. The average error ranged from 13% to 275% which is not surprising since the literature correlations were developed from data on much smaller units and for significantly different particles.

A series of statistically designed experiments were conducted using two different Geldart Group B particles in a 0.3 m diameter circulating fluidized bed (CFB) cold model to evaluate the nonmechanical L-valve for controlling the solids flow rate. The objective of this study was to investigate the effects of standpipe aeration, L-valve aeration, solids inventory, and superficial gas velocity through the riser on the solids flow rate. A stochastic correlation was developed for calculating the solid flow rate as a function of these variables. It was found that the solid flow rate increased directly proportional to each of these variables. Dimensional analysis was used to generate an expression for the solids turnover ratio. The model developed in the present study is intended to simulate flow in the present CFB loop. It is not intended for scale-up and will probably not apply for alternative CFB loops with different geometries/pressure balance conditions.

Compressor surge and stall is one of the main operational challenges in solid oxide fuel cell gas turbine (SOFC/GT) hybrid systems. The problem arises because of the added large volume between compressor and gas turbine and resultant changes to system fluid dynamics. The compressor stall and surge and its recovery were characterized at different transient states in the SOFC/GT hybrid system. In this work, the Hybrid Performance Project built at the U.S. Department of Energy, National Energy Technology Laboratory was used to perform the experimental tests. The bypass of cold air dramatically increased the compressor surge margin in the hybrid system. The effects of ramping the cold air valve from 25% to 50% open and from 50% to 30% on the compressor map were evaluated to show the resulting pressure and airmass flow dynamics. An automated compressor surge recovery was demonstrated using a cold air bypass strategy at nominal speed and for emergency shutdown. The study shows that the actuation of cold air bypass helps improve the compressor surge margin and prevents stall. The surge recovery in the hybrid system was successful because bypass of the compressed cold air into the turbine inlet modified the system pressure and reduced the air flow transients.

This concept directly incorporates thermal energy storage (TES) within a solid oxide fuel cell (SOFC). The SOFC interconnect thickness was employed as a storage medium. By increasing it’s thickness the benefits were quantified to demonstrate the potential for improving the flexibility of fuel cell hybrid power systems and increasing the efficiency in standalone fuel cell systems. A significant amount of energy can be recovered without damage to the fuel cell when thermal energy storage was incorporated in the SOFC design. Also, less airflow was required on the cathode for thermal management when the thermal mass of the fuel cell was increased. The ohmic losses were predicted to decrease, thereby leading to higher cell voltage and system efficiency.

Riser hydrodynamics are a function of the flow rates of gas and solids as well as the exit geometry, particularly when operated above the upper transport velocity. This work compares the exit voidage for multiple geometries and two different solids: Geldart group A glass beads and Geldart group B coke. Geometries were changed by modifying the volume of an abrupt T-shaped exit above the lateral riser exit. This was accomplished by positioning a plunger at various heights above the exit from zero to 0.38m. A dimensionless expression used to predict smooth exit voidage was modified to account for the effect of the depth of the blind-T. The new correlation contains the solids-gas load ratio, solids-to-gas density ratio, bed-to-particle diameter ratio, gas Reynolds Number, as well as a term for the exit geometry. This study also found that there was a minimum riser roof height above the blind-T exit beyond which the riser exit voidage was not affected by the exit geometry. A correlation for this minimum riser roof height has also been developed in this study. This study covered riser superficial gas velocities of 4.35 to 7.7m/s and solids circulation rates of 1.3 to 11.5kg/s.