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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Systematic characterization of unknown compounds via dimensionality reduction of time series

Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of an all-solid state nanobattery using molecular dynamics simulations under an external electric field

Present Li-ion battery (LIB) technology requires strong improvements in performance, energy capacity, charging-time, and cost to expand their application to e-mobility and grid storage. Li-metal is one of the most promising materials to replace commercial anodes such as graphite because of its 10 times higher specific capacity. However, Li-metal has high reactivity with commercial liquid electrolytes; thus, new solid materials are proposed to replace liquid electrolytes when Li-metal anodes are used. We present a theoretical analysis of the charging process in a full nanobattery, containing a LiCoO 2 cathode, a Li 7 P2S 8 I solid-state electrolyte (SSE), a Li-metal anode as well as Al and Cu collectors for the cathode and anode, respectively. In addition, we added a Li 3 P/Li 2 S film as a solid electrolyte interphase (SEI) layer between the Li-anode and SSE. Thus, we focus this study on the SEI and SSE. We simulated the charging of the nanobattery with an external voltage by applying an electric field. We estimated temperature profiles within the nanobattery and analyzed Li-ion transport through the SSE and SEI. Here, we observed a slight temperature rise at the SEI due to reactions forming $PS_{3}^{–}$ and $P_{2}S_{7}^{4}$$^{–}$ fragments at the interfaces; however, this temperature profile changes due to the charging current under the presence of the external electric field ε = 0.75 V Å –1 . Without the external field, the calculated open-circuit voltage (OCV) was 3.86 V for the battery, which is within the range of values of commercial cobalt-based LIBs. This voltage implies a spontaneous fall of available Li-ions from the anode to the cathode (during discharge). The charge of this nanobattery requires overcoming the OCV plus an additional voltage that determines the charging current. Thus, we applied an external potential able to neutralize the OCV, plus an additional 1.6 V to induce the transport of Li + from the cathode up to the anode. Several interesting details about Li + transport paths through the SSE and SEI are discussed.

25 ENERGY STORAGE↗
DOE OSTI2021

Nanoscale chromatin imaging and analysis platform bridges 4D chromatin organization with molecular function

Extending across multiple length scales, dynamic chromatin structure is linked to transcription through the regulation of genome organization. However, no individual technique can fully elucidate this structure and its relation to molecular function at all length and time scales at both a single-cell level and a population level. Here, we present a multitechnique nanoscale chromatin imaging and analysis (nano-ChIA) platform that consolidates electron tomography of the primary chromatin fiber, optical super-resolution imaging of transcription processes, and label-free nano-sensing of chromatin packing and its dynamics in live cells. Using nano-ChIA, we observed that chromatin is localized into spatially separable packing domains, with an average diameter of around 200 nanometers, sub-megabase genomic size, and an internal fractal structure. The chromatin packing behavior of these domains exhibits a complex bidirectional relationship with active gene transcription. Furthermore, we found that properties of PDs are correlated among progenitor and progeny cells across cell division.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Thermally Stable Co@C3N4 Single-Atom Catalysts for CO Oxidation: Atomic-Level Insights into Structure and Activity

Single Co atoms supported on C3N4 (Co@C3N4) have demonstrated high activity and selectivity in photocatalysis. However, the investigation of structure–function relationships and reaction mechanisms under photocatalytic conditions is very challenging due to the complex conditions of light absorption, charge transfer, and catalysis. In this study, we employed thermal CO oxidation as a prototypical probe reaction to benchmark the intrinsic catalytic performance and track the active-site evolution of Co@C3N4. Single Co atoms were identified and shown to be the catalytically active sites for CO oxidation based on control experiments and isotope-labeling experiments. The Co sites remained atomically dispersed before, during, and after the reaction with temperatures up to 400 °C, as established by in situ X-ray absorption fine structure (XAFS) combined with density functional theory (DFT), FDMNES simulations, and dynamic-time-warping (DTW)-assisted X-ray absorption near edge structure (XANES) matching. Together with theoretical calculations, the integrated analysis reveals a stable coordination environment under reaction conditions, which correlates with sustained activity, establishing Co@C3N4 single-atom catalysts as thermally stable CO oxidation catalysts. Beyond these findings, the current study provides a workflow for unambiguously assigning active sites in Co@C3N4 for thermal CO oxidation. This workflow will aid the understanding of their behavior in photocatalysis in the future, where light-driven dynamics obscure direct structure–function links. Notably, this study provides fundamental insights for the rational design of robust single-atom catalysts and a foundation for the broader application of Co@C3N4 catalysts in oxidation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Topology driven and soft phonon mode enabled Na-ion diffusion in quaternary chalcogenides, Na 3 ZnGaX 4 (X = S, and Se)

The compounds Na 3 ZnGaX 4 (X = S, Se) are potential solid electrolyte materials in sodium-based batteries, which have certain advantages over oxide materials and have shown significant ionic conductivity at ambient temperature. In this paper, we bring out atomic-level features of the diffusion process in these new materials using the microscopic techniques of inelastic neutron scattering (INS), quasielastic neutron scattering (QENS), and ab initio molecular dynamics (AIMD) simulations. The insights obtained from these techniques are unique and not available from other macroscopic experiments. Neutron scattering experiments have been performed at temperatures from 100 to 700 K. The simulations have been carried out up to 900 K. We have calculated the phonon spectra and the space–time correlation functions and found good agreement with the results of the neutron scattering experiments. The simulations enable detailed analysis of the atomic-site dependent dynamical information. We observe low-energy phonon modes of ~6 meV involving the vibrations of certain Na atoms in the lattice. This reveals that the Na at 32g Wyckoff sites (Na2) has sufficiently shallow potential among the two available crystallographic sites. This shallow potential facilitates diffusion. Furthermore, the specific structural topology of the network of interconnected zig-zag chains of the Na2 atomic sites provides the low-barrier energy pathways for diffusion. A small fraction of vacancy defects appears essential for diffusion. We further observe that the Na2 atoms undergo jump-like diffusion to the vacant next or the 2nd next neighbour sites at ~4 Å. While the QENS experiments reveal the jump-like diffusion and its time scale, detailed analysis of the AIMD simulations shows that the jumps appear mostly along zig-zag chains of the Na2 sites in the tetragonal ab-plane, as well as between the chains along the c-axis. In conclusion, the net diffusion is essentially 3-dimensional, with little anisotropy despite the anisotropy of the tetragonal crystal structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Multiplexable intrinsic Fabry-Pérot interferometers inscribed by femtosecond laser for vibration measurement in high temperature environments

This article reports fabrication technique and demodulation algorithm developments to use multiplexable intrinsic Fabry-Pérot interferometer (IFPI) fiber sensor array for distributed vibration measurements at high temperatures. Using femtosecond laser direct writing scheme, IFPI array were fabricated through laser-induced Type II scattering points in single mode fiber cores. Reflection spectra of IFPI array were demodulated in real-time using modified Bunemen frequency analysis. The demodulation algorithm, which was implemented using a photodetector-array spectrometer, achieves 64-nε dynamic strain resolution at 1-kHz spectral acquisition rate and 2-kHz maximum vibration bandwidth. Performance and stabilities of IFPI sensor array were characterized from room temperatures to 800 °C. The static strain resolution was 0.6 με and the minimum detectable dynamic strain amplitude was 23 nε/√Hz at 800 °C. The multiplex performance was also tested by measuring dynamic strain in time domain through six IFPI sensor array fabricated in one fiber. This paper presents an integrated and low-cost sensing solution to perform distributed vibration measurements in harsh environments.

42 ENGINEERING↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The estimation of Poisson’s ratio by time-averaging and Cornu’s method for isotropic beams

Prior research suggests that direct static (e.g., uniaxial testing) and dynamic (e.g., ultrasonic wave propagation analysis) measurements differ in the estimation of Poisson’s ratio because anisotropies and heterogeneities in the sample material affect the two types of tests differently. Even assuming isotropic and homogeneous material properties, prior research further suggests that discrepancies between static/dynamic test results will exist because the error of the diagnostic techniques for the measurand are inherently different. Finally, thermodynamic effects are not present in static tests but can significantly affect dynamic test results. Given the potential for all these variables to produce discrepancies, it would be helpful to have the measurement of Poisson’s ratio obtainable from the same theory and experimental measurements by either static or dynamic testing methods. Our finite element calculations show that by combining time-averaged scanning digital holography with Cornu’s method, it is theoretically possible to estimate the effective Poisson’s ratio from the anticlastic contours at the antinode of the first out-of-plane bending mode shape. This is true regardless of frequency and therefore applicable for both static and dynamic measurements. Our results show that the estimate of Poisson’s ratio by Cornu’s method using data from simulations of mode shapes approaches the true value of Poisson’s ratio. Additionally, our research suggests that beam geometry and boundary conditions are fundamental factors limiting the convergence of the estimate of Poisson’s ratio to the true value of Poisson’s ratio regardless of performing a static or dynamic test.

42 ENGINEERING↗
DOE OSTI2021

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Abstract In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

de Carvalho, Miguel↗
DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Non-stoichiometry Governs the Pathway from Amorphous to Crystalline Calcium Carbonate

The controlled crystallization of calcium carbonate underlies the elaborate architectures of marine corals, the design of biomimetic materials, and the global carbon cycle. Despite its ubiquity, the chemical mechanisms that govern the crystallization of calcium carbonate from its amorphous precursor, amorphous calcium carbonate (ACC), remain elusive, largely due to the difficulty in resolving the structure and chemistry of this transient amorphous state. Here, we use time-lapse photography, image analysis, spatially resolved pH determination, in situ synchrotron pair distribution function (PDF) analysis, and dynamic nuclear polarization (DNP) solid-state nuclear magnetic resonance (NMR) spectroscopy to reveal pervasive compositional variability in ACC that underpins its metastability and crystallization. By evaluating the kinetics of the amorphous-to-crystalline transition across different solution chemistries, we demonstrate that ACC is non-stoichiometric and CO 3 -deficient. Counterions from the precursor (e.g., NO 3 - from Ca(NO 3 ) 2 ) substitute into the ACC network, displacing CO 3 ions and mediating both ACC stability and transformation. Crystallization proceeds through refinement of the stoichiometry toward CaCO 3 and uptake of free CO 3 2- anions. Furthermore, these findings are consistent across a wide range of concentrations and different carbonate sources, explaining the diverse behaviors observed for ACC and providing a chemical framework for controlling calcium carbonate crystallization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Symmetry instability induced by topological phase transitions

The symmetry-topology interplay dictates how to define order parameters and classify material ordered phases. However, current understanding of this interplay has been predominately approached from a one-sided perspective, with topological states being classified within the constraints imposed by specific fixed symmetries. Here, in this study, we complete this full circle by demonstrating spontaneous symmetry breaking that results from a periodic alteration of topological phases induced by light in a centrosymmetric Dirac material ZrTe 5 . The distinguishing feature is the observation of robust correlation and striking anomalies in the fluence and temperature dependence of key transport parameters. First, both shift current 𝐽 𝑠 and displacement current 𝐽 𝑑 , arising from interband transition and infrared phonon driving, respectively, along with charge carrier pumping, exhibit similar behaviors. Second, they all peak at similar low pump fluence, followed by a subsequent reduction as the fluence further increases. This behavior cannot be explained by conventional energetically allowed, direct excitations. Third, all the three observables exhibit anomalies when they approach the topological phase transition temperature. These results highlight the unique low-energy pumping behaviors in ZrTe 5 , characterized by reversible fluence dependence and a “hinge-like” interaction that connects various electronic and lattice observables, including phonons, charge carriers, and currents. Our findings, supported by model analysis, provide key insights into the fragility of crystalline (inversion) and time-reversal symmetries during the dynamics of topological phase transitions. This fragility drives spontaneous symmetry breaking, evidenced by the synchronized emergence of off-resonant infrared phonons and broken-symmetry photocurrents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Efficient perturbation-tracking method for directly probing the spectral phonon properties from molecular dynamics simulations

Existing methods for directly extracting the spectral phonon properties from molecular dynamics (MD) simulations, like the normal mode analysis (NMA) and spectral energy density analysis, all require a very long simulation time to produce reliable results with good convergence. So far, these methods are mainly applied in studies using small systems and with empirical potentials, as the heavy computational load has greatly hindered their further applications. Here we propose a perturbation-tracking (PT) method for directly probing the mode-wise phonon anharmonic frequencies and lifetimes. We show that results obtained from our method are in excellent agreement with those from the conventional NMA approach, using Si as the model material system. Comparing with the NMA approach, the PT method offers a greater accuracy and significant improvement of efficiency. It takes an average of two orders of magnitude and up to three orders of magnitude less simulation time to obtain the same lifetime result of a phonon mode with intermediate to high accuracy. Meanwhile, our method preserves all the dynamics of probed phonon mode from a particular state, which means it is capable of studying the transient thermal transport processes in a nonequilibrium system. Besides the exceptional efficiency, our method also comes with freedom to choose to probe only those modes of interest. This makes it ideal for use with large systems and in computationally demanding applications, such as ab initio MD simulations. Moreover, the PT method we propose here is very straightforward and easy to implement.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Imaging, understanding, and control of nanoscale materials transformations

The development of liquid cells for transmission electron microscopy has enabled breakthroughs in our ability to follow nanoscale structural, morphological, or chemical changes during materials growth and applications. Time-resolved high-resolution imaging and chemical analysis through liquids opened the opportunity to capture nanoscale dynamic processes of materials, including reaction intermediates and the transformation pathways. In this article, a series of work is highlighted with topics ranging from liquid cell developments to in situ studies of nanocrystal growth and transformations, dendrite formation, and suppression of lithium dendrites through in situ characterization of the solid–electrolyte interphase chemistry. The understanding garnered is expected to accelerate the discovery of novel materials for applications in energy storage, catalysis, sensors, and other functional devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

With growing freshwater scarcity, direct potable reuse (DPR) systems that reclaim wastewater for drinking are becoming increasingly important for sustainable water supply. Reliable operation requires minimizing downtime in ultrafiltration (UF) units, where membrane fouling leads to elevated trans-membrane pressure (TMP). This study develops data-driven regression models based on random forest (RF) and autoregressive (AR) approaches to forecast the initial TMP at the start of each UF filtration cycle in a pilot-scale DPR system. The RF model consistently outperforms baseline methods, including historical mean, last observation carried forward, and AR models, across multiple forecast horizons, achieving the lowest root mean square error. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent input variables across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is assessed for both direct and recursive RF modelling approaches. The proposed RF framework establishes a robust foundation for predictive monitoring and real-time optimization of UF operations, supporting sustainable and reliable water reuse.

direct potable reuse↗