First-principles Model Research Articles

Development of hybrid first principles – Artificial intelligence models for transient modeling of power plant superheaters under load-following operation

This paper develops different approaches for synergistic integration of physics-based models with data-driven models for modeling of high temperature power plant superheaters. Two types of data-driven models are developed, namely all-nonlinear static-dynamic neural network models as well as models obtained using a Bayesian machine learning approach. Series, integrated, and parallel coupling of first-principles models and data-driven models are proposed. Algorithms for solving the training problems for the data-driven models and for simulation of the hybrid models are developed. The developed approach is applied to high temperature power plant superheaters that face considerable modeling and measurement challenges. Measurement challenges arise due to harsh operating conditions that not only make placement of sensors difficult, but life of the placed sensors very limited. Modeling challenges arise due to complex inhomogeneous spatial distribution of flue gas and steam flowrates, especially under load-following operation and uncertainties in heat transfer characteristics because of multiple factors such as stochastic spatio-temporal variation of ash deposits on the superheater tubes in coal-fired power plants, internal oxide scale formation in the tubes, etc. First-principles two-dimensional differential–algebraic equation models of two industrial superheaters, one from a natural gas combined cycle plant and another from a coal-fired power plant, are developed. The results from the models are compared against industrial operational data. For all cases studied in this work, it is observed that both for steady-state and dynamic data, the hybrid models have much higher accuracy than when only the respective first-principles models are used. For example, during prediction of the average outside tube wall temperature of superheater tubes at the combined cycle power plant, the root mean squared error observed for the standalone first-principles model improved from 12.3 °C to 0.5 °C post hybridization with data-driven models. Similarly, while predicting the main steam outlet temperature in the coal-fired plant, the hybrid models resulted in reduction of the root mean squared error value from 19.5 °C to around 2 °C. In addition, the hybrid models yield highly resolved spatio-temporal temperature profiles that can be helpful for developing monitoring approaches of these critical components under load-following operation.

Toward Implementation of the Pressure-regulated, Feedback-modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of V. Springel & L. Hernquist (hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially resolved simulations of the turbulent, multiphase, star-forming interstellar medium (ISM) have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local-Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the pressure-regulated feedback-modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the “native” simulation outputs with postprocessed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step toward implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations.

ScatterX： A software for fast processing of high-throughput small-angle scattering data

Abstract Scattering experiments become increasingly popular in modern scientific research, including the areas of materials, biology, chemistry, physics, etc. Besides, various types of scattering facilities have been developed recently, such as lab-based X-ray scattering equipment, national synchrotron facilities and large neutron facilities. These above-mentioned trends bring up fast-increasing data amounts of scattering data, as well as different scattering types (X-ray, neutron, laser and even microwaves). This struggles with data analysis software of the last generation, where data are usually analyzed assuming certain first-principle models and given data formats. To help researchers process and analyze scattering data more efficiently, we developed a general and model-free scattering data analysis software based on matrix operation, which has the unique advantage of high throughput scattering data processing, analysis and visualization. To maximize generality and efficiency, data processing is performed based on a three-dimensional matrix, where scattering curves are saved as matrices or vectors, rather than the traditional definition of paired values. It can not only realize image batch processing, background subtraction and correction, but also analyze data according to scattering theory and model, such as radius of gyration, fractal dimension and other physical quantities. In the aspect of visualization, the software allows the modify the color maps of two-dimensional scattering images and the gradual color variation of one-dimensional curves to suit efficient data communications. In all, this new software can work as a standalone platform for researchers to process, analyze and visualize scattering data from different research facilities without considering different file types or formats. All codes in this manuscript are open-sourced and can be easily implemented in matrix-based software, such as MATLAB, Python and Igor.

Engineering Active Sites of Metal/Metal Oxide Catalysts by Oxide Ligand Overlayers.

Engineering sites of supported metal catalysts is essential to enhancing activity and selectivity. Such enhancement is typically achieved by particle size modification, surface alloying, or attaching molecular ligands. Yet, control strategies for complex, multifunctional molecules and catalysts, where selectivity is crucial, are lacking. Here, we demonstrate that submonolayer WOx with tunable coverage preferentially decorates well-coordinated Pt terrace sites as a stable ligand. By combining experimental kinetics with probe molecules, in situ spectroscopies, and first-principles modeling, we show that the WOx coverage on Pt modifies the metal-to-acid site balance while retaining the acid strength intact and results in optimal reactivity for metal-acid catalyzed reactions at a specific metal, size, and support-dependent WOx coverage. The oxide can also alter the reactant adsorption mode, reversing selectivity and pathways from terrace- to step-dominated, as evidenced in furfural decarbonylation and hydrogenation. The insights open avenues for improving metal/metal oxide catalysts beyond the specific system.

First-principles modeling of high-field transport in diamond

First-principles modeling of high-field transport in diamond

A hybrid predictive modeling approach for catalyzed polymerization reactors

Polymerization reactions are characterized by complex, nonlinear behaviors that pose significant challenges for conventional modeling techniques. Accurate and reliable models are crucial for advancing material science and enabling technological innovations across various industries. Conventional first-principles models often fall short in capturing the intricate dynamics of polymeric systems, leading to limitations in predictive accuracy. In this work, we propose a novel hybrid modeling approach that combines a conventional first-principles model with the strengths of a data-driven multi-layer perceptron (MLP) model and also a linear regression (LR) model to enhance the predictability of polymerization processes. Utilizing this hybrid approach significantly reduces the mean absolute error for predicting the concentrations of main reagents by 84% and 86%, respectively, in experiments with significantly deviant outcomes. Our results indicate that the model is capable of predicting the concentrations of both the main and side products with a maximum error margin of 3.5%.

Simulations of the stationary Q = 10 and the exit phase from the flat-top of an ITER 15MA baseline scenario: predictive JINTRAC simulation with a consistent treatment of D and T in the whole plasma

Abstract Designing a robust termination scenario for a burning ITER plasma is a challenge that requires extensive core plasma and divertor modelling. The presented work consists of coupled core/edge/SOL/divertor simulations, performed with the JINTRAC code, to study the Q = 10 flat-top phase and exit phase of the ITER 15 MA/5.3 T DT scenario. The modelling utilizes the recently implemented option to treat deuterium and tritium separately in the SOL/divertor, enabling a consistent treatment of deuterium and tritium in the whole plasma volume, which is a unique capability of JINTRAC. In addition, these are the first JINTRAC simulations of this scenario that use a first-principles transport model to self-consistently model the ECRH power deposition and to include tungsten while keeping track of tungsten sputtering and accumulation. The flat-top simulations demonstrate the possibility of sustaining a steady state fusion Q of 10 using pure deuterium gas puffs together with DT mixed pellets, which is an option to make a more effective use of tritium. Simulations of the exit phase are set up sequentially, with each phase providing initial conditions for the next, starting with a density decay at full current and auxiliary power, and demonstrate the possibility of reducing the density robustly within a few seconds. Following the density decay, a subsequent auxiliary power ramp-down in H-mode is performed with a late H–L transition at low auxiliary power, which may provide an option for the optimization of the plasma termination. The final ramp-down phase consists of a current ramp-down in L-mode to 3.75 MA.

First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method

We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. These include the kinetics and thermal effects of the limestone decomposition as well as the heat transfer between the gaseous and solid phases. The model consists of two coupled equation systems for the upper and lower part of the kiln. The results of the model are validated qualitatively and are used to train an artificial neural network that predicts the conversion and the temperature in the crossover channel. The artificial neural network performs very well with values of the root mean squared error that are two to three orders of magnitudes lower than the range covered within the data. Finally, we use a genetic algorithm to optimize the feed mass flows such that the conversion and the fuel efficiency are improved in a Pareto-optimal manner. The results are compared to those of a gradient-based optimization method, which shows the usefulness and validity of the approach with the genetic algorithm.

Grey‐box modelling for estimation of optimum cut point temperature of crude distillation column

AbstractA grey‐box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit (CDU) under uncertainty in crude composition and process conditions. First principle (FP) model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields. A hybrid methodology based on the integration of Taguchi method and genetic algorithm (GA) was employed to estimate the optimal cut point temperature for various sets of process variables. Optimised datasets were utilised to develop an artificial neural networks (ANN) model for the prediction of optimum values of cut points. The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA. The integration of the ANN and FP model makes it a grey‐box (GB) model. For the case of Zamama crude, the GB model helped in the decrease of up to 38.93% in energy required per kilo barrel of diesel and an 8.2% increase in diesel production compared to the stand‐alone FP model under uncertainty. Similarly, for Kunnar crude, up to 18.87% decrease in energy required per kilo barrel of diesel and a 33.96% increase in diesel production was observed in comparison to the stand‐alone FP model.

Effects of freebase/protonated nicotine concentration, liquid composition and electrical power on throat hit in direct-to-lung vaping: theory and clinical measurements

BackgroundFor decades, the tobacco industry has engineered the sensory characteristics of combustible tobacco products including the degree of harshness experienced at the back of the throat. Commonly referred to as...

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.

Enhancing stability and position control of a constrained magnetic levitation system through optimal fractional-order PID controller

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error.

An Integrated Experimental and Modeling Approach for Assessing High-Temperature Decomposition Kinetics of Explosives.

We present a new integrated experimental and modeling effort that assesses the intrinsic sensitivity of energetic materials based on their reaction rates. The High Explosive Initiation Time (HEIT) experiment has been developed to provide a rapid assessment of the high-temperature reaction kinetics for the chemical decomposition of explosive materials. This effort is supported theoretically by quantum molecular dynamics (QMD) simulations that depict how different explosives can have vastly different adiabatic induction times at the same temperature. In this work, the ranking of explosive initiation properties between the HEIT experiment and QMD simulations is identical for six different energetic materials, even though they contain a variety of functional groups. We have also determined that the Arrhenius kinetics obtained by QMD simulations for homogeneous explosions connect remarkably well with those obtained from much longer duration one-dimensional time-to-explosion (ODTX) measurements. Kinetic Monte Carlo simulations have been developed to model the coupled heat transport and chemistry of the HEIT experiment to explicitly connect the experimental results with the Arrhenius rates for homogeneous explosions. These results confirm that ignition in the HEIT experiment is heterogeneous, where reactions start at the needle wall and propagate inward at a rate controlled by the thermal diffusivity and energy release. Overall, this work provides the first cohesive experimental and first-principles modeling effort to assess reaction kinetics of explosive chemical decomposition in the subshock regime and will be useful in predictive models needed for safety assessments.

Nitrate and phosphate removal by capacitive deionization using nanocellulose/polypyrrole electrodes

Capacitive deionization (CDI) is a desalination technology that shows promise for removing nitrate and phosphate ions. The main components of CDI cells are electrodes, which are typically made from activated carbon (AC). However, the electrosorption performance of AC electrodes is limited because of the negative zeta potential, hydrophobicity, and oxidation resulting in co–ions’ repulsion. This study investigates binder-free polypyrrole-coated nanocellulose (NC/PPy) electrodes, which are hydrophilic and possess various functional groups, as an alternative to AC electrodes. An asymmetric CDI cell with an NC/PPy positive electrode demonstrated greater energy and charge efficiencies compared to a symmetric cell. The NC/PPy electrode exhibited a threefold electrosorption capacity towards nitrate ions compared to the AC electrode, and 4.5 times higher than for phosphate ions. The electrosorption mechanism revealed by first-principles modeling indicates similar sorption energies for nitrate and phosphate ions, with higher nitrate capacity due to co-ion repulsion and diffusion effects.

First-principles modeling of H2CO3 molecular adsorption on CaSiO3(001) surface for application in the sequestration and utilization of CO2.

This work employed density functional theory (DFT) to further study the adsorption of H2CO3, HCO3-, and CO32- on the CaSiO3(001) surface, which could provide additional insights into the mechanism of carbonation on the CaSiO3 surface. It was concluded that the carbonation of CO2 promoted carbon sequestration, whereby the aqueous carbonation route increased the reaction rate substantially compared to direct gas-solid carbonation. H2CO3 is more conducive to CO2 sequestration, which can be attributed to the interaction of H atoms with the surface. Further, H2CO3 can be converted into HCO3- or CO32- on the CaO-terminated (001) surface, whereas only HCO3- was formed on the SiO-terminated (001) surface. All the adsorption energies of H2CO3 were negative, suggesting that H2CO3 adsorption was energetically stable and spontaneous. The most likely adsorption model of HCO3-, having negative adsorption energy, was the one adsorbed on the SiO-terminated (001) surface, in which HCO3- is transformed into CO32-. The other adsorption models of HCO3- and all the adsorption models of CO32- have positive adsorption energies. Considering the adsorption process of H2CO3, HCO3- and CO32- adsorption reactions may occur successively to some extent depending on the environment.

Borides as promising M2AX phase materials with high elastic modulus using machine learning and optimization

There is growing interest in novel MAX phase materials for various applications ranging from aircraft/spacecraft and defense to energy and electronics due to their unique combination of metallic and ceramic properties. Traditional materials discovery has mostly relied on human intuition coupled with rigorous experiments; however, this approach has been time-consuming and inefficient. Over the last few decades, advances in fundamental and data-driven approaches such as first-principles modeling, materials informatics, machine learning and optimization, coupled with an exponential rise in computational power, have enabled faster and more efficient materials discovery. Here, we present an exploration of high elastic modulus novel boride-based M2AX phase materials using a combination of the aforementioned methods. Specifically, an ensemble of gradient boosted machine learning models was developed to predict the elastic modulus from informatics-based structural features by leveraging a dataset of Density Functional Theory (DFT)-predicted elastic moduli for 223 M2AX phase materials (carbides and nitrides). Using Bayesian optimization, inverse modeling was carried out to maximize the model-predicted elastic modulus by identifying the optimal features. Finally, model predictions for 1,035 candidate M2AX materials were generated to compare their features with the optimal features to identify potential novel promising materials. We found that Ta2PB, Nb2PB, and V2PB have similar high elastic moduli (371.7, 351.5, and 347.4 GPa) to their carbide counterparts (364.7, 357.7, and 373.5 GPa), and our results support the possibility that borides can be a viable tertiary element for M2AX phases.

Modeling Bibb Lettuce Nitrogen Uptake and Biomass Productivity in Vertical Hydroponic Agriculture

Global fertilizer production and mismanagement significantly contribute to many harmful environmental impacts, revealing the need for a greater understanding of crop growth and nutrient uptake, which can be used to optimize fertilizer management. This study experimentally adapts first-principles microbial modeling techniques to the hydroponic cultivation of Bibb lettuce (Lactuca sativa) under nitrogen-limited conditions. Monod and Michaelis–Menten’s approaches are applied to predict biomass productivity and nutrient uptake and to evaluate the feasibility of reclaimed wastewater as a nutrient source of nitrogen. Experimental and modeling results reveal significantly different kinetic saturation constants (Ks = 1.331 and Km = 17.887 mg L−1) and a corresponding cell yield strongly dependent on nutrient concentration, producing visually and compositionally distinct tissue between treatments receiving ≤26.2 and ≥41.7 mgN L−1. The resulting Monod model overestimates dry mass predictions during low nutrient conditions, and the collective results support the development of a dynamic Monod curve that is temporally dependent during the plants’ lifecycle. Despite this shortcoming, these results support the feasibility of reclaiming nitrogen from wastewater in hydroponic agriculture, expecting to produce lesser biomass lettuce exhibiting healthy tissue. Furthermore, this study provides a mathematical foundation for agricultural simulations and nutrient management.

Self-stabilizing economic nonlinear model predictive control applied to modular systems

Recent advances have been made in self-stabilizing Economic Nonlinear Model Predictive Control (eNMPC) formulation without pre-calculated setpoints, which leverages norm-based steady-state optimality conditions to enhance system robustness. To enable practical implementation, a generalized time-domain formulation is proposed, accommodating the discrete-time nature of control instrumentation and the continuous-time nature of first-principles models. A case study involving a modular membrane reactor illustrates the applicability of self-stabilizing eNMPC in real-world industrial scenarios.

Non-invasive detection of hazardous materials with a thermal-to-epithermal neutron station: a feasibility study towards practical application

The growing scale of the devastation that even a single terrorist attack can cause requires more effective methods for the detection of hazardous materials. In particular, there are no solutions for effectively monitoring threats at sea, both for the off-shore infrastructure and ports. Currently, state-of-the-art detection methods determine the density distribution and the shapes of tested subjects but only allow for a limited degree of substance identification. This work aims to present a feasibility study of the possible usage of several methods available on the thermal-to-epithermal neutron station, VESUVIO, at the ISIS neutron and muon spallation source, UK, for the detection of hazardous materials. To this end, we present the results of a series of experiments performed concurrently employing neutron transmission and Compton scattering using melamine, a commonly used explosive surrogate, in order to determine its signal characteristics and limits of detection and quantitation. The experiments are supported by first-principles modelling, providing detailed scrutiny of the material structure and the nuclear dynamics behind the neutron scattering observables.