Model predictive control simulations with block-hierarchical differential–algebraic process models
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97 MATHEMATICS AND COMPUTING↗
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A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation
Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.
Kim, Hyeonjin [Pacific Northwest National Laborato↗
Optimizing power system restoration with damaged communications
Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.
97 MATHEMATICS AND COMPUTING↗
Laboratory investigation of hydraulic fracturing in granitic rocks using active and passive seismic monitoring
SUMMARY Knowledge of the fracturing processes can be important for the optimization of pressurized fluid injection operations in the deep underground rock mass. Active and passive seismic monitoring techniques have been used in the field for tracking or mapping the propagating hydraulic fracture. Although both these monitoring techniques provide valuable information about the generated fracture network, it is difficult for either technique to comprehensibly identify the different processes associated with hydraulic fracturing. The combined active and passive monitoring has the potential for better characterization of the complex hydraulic fracturing phenomena. In this study, laboratory hydraulic fracturing experiments with combined active and passive seismic monitoring were conducted on true triaxially loaded Barre granite cubes with different fluid injection rates. The seismic inelastic fracturing was detected by 16 passive acoustic emission sensors, where 3678 and 2370 seismic source events were detected for the high and low injection rate experiments, respectively. For active monitoring, strong variations in the attributes of signals were observed which were transmitted through four source–receiver pairs, placed both perpendicular and parallel to the generated hydraulic fracture. Positive velocity changes were observed for active sensor pairs with ray paths passing through the generated hydraulic fracture indicating fluid permeation, whereas isolated dry deformation was characterized by a slight but permanent velocity decrease. Compared to velocity, the energy of the active signals was 1–2 orders of magnitude more sensitive to different hydraulic fracturing processes. However, the sensitivity and signatures of the active signal attributes were found to be dependent on the frequency range and direction of ray path with respect to the location of the generated fracture network. Using the coupled evaluation of the active and passive signals we were able to systematically identify various hydraulic fracturing processes including: (1) aseismic deformation, (2) fracture initiation and fluid permeation, (3) pressure build-up, (4) fracture propagation and (5) pressure release and leak-off. The results of this study showed that combining the respective advantages of active and passive seismic techniques and using both of them to monitor the failure processes can facilitate a more comprehensive understanding and better control of the hydraulic stimulations in subsurface operations.
Geochemistry & Geophysics↗
Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings
Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.
Prakash, Anand Krishnan↗
A reference open-source controller for fixed and floating offshore wind turbines
Abstract. This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide modern controller features. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All of the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5 MW reference wind turbine and International Energy Agency 15 MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by over 10 % compared to the NREL 5 MW reference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by nearly 30 % when using the platform feedback routine instead of a more traditional low-bandwidth controller.
17 WIND ENERGY↗
Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing
Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.
Optics↗
Constructing efficient CuO x -CeO 2 catalyst for NO reduction by CO: New insights into the structure–activity relationship
CuO-CeO 2 based materials have been recognized as promising substitutes for precious metal catalysts in emission control field due to their superior redox property and low cost. In this work, by optimizing the deposition process of CeO 2 and CuO onto γ-Al 2 O 3 , highly dispersed CuO clusters on unique CeO 2 -Al 2 O 3 support with small CeO 2 particles (7Cu-Ce/CeAl) were successfully constructed for efficient NO reduction by CO, which exhibited much higher NO removal efficiency and N 2 selectivity than CuO catalysts supported on γ-Al 2 O 3 (7Cu/Al) and conventional CeO 2 -Al 2 O 3 support (7Cu/CeAl). Moreover, H 2 O showed limited inhibition effect on the catalytic performance of 7Cu-Ce/CeAl catalyst. With the help of Raman spectra, X-ray absorption spectroscopy, in situ diffuse reflectance infrared Fourier transform spectroscopy, etc., it was clearly revealed that the abundant Cu + /Ce 3+ paired sites with surface synergetic oxygen vacancies (SSOV) on 7Cu-Ce/CeAl catalyst could effectively facilitate the adsorption and activation of CO and NO, thus significantly enhancing the NO removal efficiency.
36 MATERIALS SCIENCE↗
Chemical controls on the propagation and healing of subcritical fractures
Human activities involving subsurface reservoirs—resource extraction, carbon and nuclear waste storage—alter thermal, mechanical, and chemical steady-state conditions in these systems. Because these systems exist at lithostatic pressures, even minor chemical changes can cause chemically assisted deformation. Therefore, understanding how chemical effects control geomechanical properties is critical to optimizing engineering activities. The grand challenge in predicting the effect of chemical processes on mechanical properties lays in the fact that these phenomena take place at molecular scales, while they manifest all the way to reservoir scales. To address this fundamental challenge, we investigated chemical effects on deformation in model and real systems spanning molecular- to centimeter scales. We used theory, experiment, molecular dynamics simulation, and statistical analysis to (1) identify the effect of simple reactions, such as hydrolysis, on molecular structures in interfacial regions of stressed geomaterials; (2) quantify chemical effects on the bulk mechanical properties, fracture and displacement for granular rocks and single crystals; (3) develop initial understanding of universal scaling for individual displacement events in layered geomaterials; and (4) develop analytic approximations for the single-chain mechanical response utilizing asymptotically correct statistical thermodynamic theory. Taken together, these findings advance the challenging field of chemo-mechanics.
36 MATERIALS SCIENCE↗
Feedstock Supply Chain Analysis
Addressing the challenge of biomass feedstock variability that affects the optimization of processing and conversion processes, the project's goal is to maximize biorefinery economics by improving the process quality control of feedstock, which will lead to greater plant availability and predictable yields of high value biofuels and co-products.
09 BIOMASS FUELS↗
An automated and portable method for selecting an optimal GPU frequency
Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.
97 MATHEMATICS AND COMPUTING↗
Spray‐induced gene silencing to identify powdery mildew gene targets and processes for powdery mildew control
Abstract Spray‐induced gene silencing (SIGS) is an emerging tool for crop pest protection. It utilizes exogenously applied double‐stranded RNA to specifically reduce pest target gene expression using endogenous RNA interference machinery. In this study, SIGS methods were developed and optimized for powdery mildew fungi, which are widespread obligate biotrophic fungi that infect agricultural crops, using the known azole‐fungicide target cytochrome P450 51 (CYP51) in the Golovinomyces orontii–Arabidopsis thaliana pathosystem. Additional screening resulted in the identification of conserved gene targets and processes important to powdery mildew proliferation: apoptosis‐antagonizing transcription factor in essential cellular metabolism and stress response; lipid catabolism genes lipase a , lipase 1 , and acetyl‐CoA oxidase in energy production ; and genes involved in manipulation of the plant host via abscisic acid metabolism ( 9‐cis‐epoxycarotenoid dioxygenase , xanthoxin dehydrogenase , and a putative abscisic acid G‐protein coupled receptor ) and secretion of the effector protein, effector candidate 2 . Powdery mildew is the dominant disease impacting grapes and extensive powdery mildew resistance to applied fungicides has been reported. We therefore developed SIGS for the Erysiphe necator–Vitis vinifera system and tested six successful targets identified using the G. orontii–A. thaliana system. For all targets tested, a similar reduction in powdery mildew disease was observed between systems. This indicates screening of broadly conserved targets in the G. orontii–A. thaliana pathosystem identifies targets and processes for the successful control of other powdery mildew fungi. The efficacy of SIGS on powdery mildew fungi makes SIGS an exciting prospect for commercial powdery mildew control.
59 BASIC BIOLOGICAL SCIENCES↗
PD21A680: Additive Manufacturing Process Optimization and Qualification FY21 Year-end Report
This project builds upon knowledge gained from previous projects on Additive Manufacturing (AM) operation, process control and monitoring, repeatability, and process improvement. This project will exclusively focus on metal powder bed additive manufacturing (PBAM). Objective 1: Advance metal powder bed AM R&D capabilities. Perform enhanced R&D on increased build volumes and throughput, safety and handling controls, process monitoring, and diagnostics with a focus on production end-states. Objective 2: Build upon knowledge gained from previous PDRD projects to develop deeper understanding of process controls, essential for transitioning metal AM processes for future applications. Objective 3: Maintain business development and research advantages through collaborations with external industry and university partners.
36 MATERIALS SCIENCE↗
Screw-Dislocation-Driven Growth of 2D Perovskite Spiral Microplates
Two-dimensional (2D) organic–inorganic halide perovskites are solution-processable semiconductors that are promising for optoelectronic applications. Understanding crystallization mechanisms to achieve control over nanostructures is important for optimizing desired properties. Here we introduce a versatile strategy to synthesize spiral microplates of diverse 2D perovskites at the air–water interface through screw-dislocation-driven growth. Spirals of 11 2D perovskite compositions (LA) 2 (A) n−1 Pb n X 3n+1 with different spacer (LA) cations, A-cations, halide (X) anions, and n-number can be grown. They typically consist of single- or few-layer perovskite step heights but exhibit stacking complexity when multiple dislocations interact. The spiral microplates exhibit the characteristic optical properties (photoluminescence and second-harmonic generation) of the underlying 2D perovskites. Fluorescence-detected circular dichroism imaging shows that the chirality of the spiral center does not translate to the observed chiroptical properties of the microplate, consistent with the length scale of the chiral distortion. Furthermore, this solution growth of perovskite spirals diversifies the perovskite microstructures for optoelectronics and other applications.
2D halide perovskites↗
Pulse-level noisy quantum circuits with QuTiP
The study of the impact of noise on quantum circuits is especially relevant to guide the progress of Noisy Intermediate-Scale Quantum (NISQ) computing. In this paper, we address the pulse-level simulation of noisy quantum circuits with the Quantum Toolbox in Python (QuTiP). We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian that describes the unitary evolution of the physical qubits. Various types of noise can be introduced based on the physical model, e.g., by simulating the Lindblad density-matrix dynamics or Monte Carlo quantum trajectories. In particular, the user can define environment-induced decoherence at the processor level and include noise simulation at the level of control pulses. We illustrate how the Deutsch-Jozsa algorithm is compiled and executed on a superconducting-qubit-based processor, on a spin-chain-based processor and using control optimization algorithms. We also show how to easily reproduce experimental results on cross-talk noise in an ion-based processor, and how a Ramsey experiment can be modeled with Lindblad dynamics. Finally, we illustrate how to integrate these features with other software frameworks.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids With Topology Reconfiguration
This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.
14 SOLAR ENERGY↗
Optimization-based Approaches for Design of Chemical Process Families Using ReLU Surrogates
Presentation delivered at 2023 Foundations of Computer-Aided Process Operations (FOCAPO) and Chemical Process Control (CPC) conference.
Stinchfield, Georgia↗
Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis
The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗