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Wang, Hong

Publications and source records attributed to Wang, Hong.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A signal compensation based cascaded PI control for an industrial heat exchange system

Since the characteristics of the industrial heat exchange system (HES) are drastically affected by variations of the steam pressure, ambient temperature and water quality, it is difficult for the classical cascade PI control system to meet the desirable control objectives. Therefore, in this paper, a deterministic linear model with unmodeled dynamics is used to describe the dynamic characteristics of the HES. It should be noted that the unmodeled dynamics at the previously sampled instant can be calculated using actual measurements and that the tracking error, which includes effects of the rate of changes of unmodeled dynamics on the controlled model, can be accurately determined. An additional compensating signal is obtained with the feedforward control together with a one-step optimal control law. Consequently, the cascade PI control strategy with the signal compensation is established. Furthermore, the simulation and industrial application show that the proposed method can effectively improve the performance of the cascade PI control system, especially when the unmodeled dynamics is subjected to unknown variations.

42 ENGINEERING↗
DOE OSTI2020

Specimen for evaluating pressure pulse cavitation in rock formations

An apparatus 300 for simulating a pulsed pressure induced cavitation technique (PPCT) from a pressurized working fluid (F) provides laboratory research and development for enhanced geothermal systems (EGS), oil, and gas wells. A pump 304 is configured to deliver a pressurized working fluid (F) to a control valve 306, which produces a pulsed pressure wave in a test chamber 308. The pulsed pressure wave parameters are defined by the pump 304 pressure and control valve 306 cycle rate. When a working fluid (F) and a rock specimen 312 are included in the apparatus, the pulsed pressure wave causes cavitation to occur at the surface of the specimen 312, thus initiating an extensive network of fracturing surfaces and micro fissures, which are examined by researchers.

03 NATURAL GAS↗
DOE OSTI2020

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Synthesis of highly water soluble tetrabenzoporphyrins and their application toward photodynamic therapy

Novel tetraaryl-(pyridinium-4-yl)-tetrabenzoporphyrins have been successfully synthesized via a Heck-based sequence reaction. These tetrabenzoporphyrins were substituted with eight pyridyl groups at the fused benzene rings. Methylation of the pyridyl groups with methyl iodide afforded highly water soluble tetrabenzoporphyrins carrying eight ionic groups. The extended π-conjugation broadened and red-shifted the absorption band of these porphyrins to 650–750 nm. Furthermore, these cationic tetrabenzoporphyrins showed non-toxicity in the dark up to 100 uM. High phototoxicity with IC 50 values lower than 18 μM were obtained for these tetrabenzoporphyrins.

DFT calculation photosensitizer↗
DOE OSTI2020

Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter

In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.

stochastic systems, PI Controller, Entropy, Extend↗
DOE OSTI2020

Stochastic Fault Detection

This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.

Wang, Aiping↗
DOE OSTI2020

High-Temperature Stability of Aqueous Foams as Potential Waterless Hydrofracking Fluid for Geothermal Reservoir Stimulation

Hydraulic fracturing is the process of fracturing rock formations with high-pressure water-based fluids. In Enhanced Geothermal Systems (EGS) hydraulic fracturing is carried out by injecting high-pressure fluids into the Hot Dry Rocks (HDR) under carefully controlled conditions. The fluid used for fracturing is an important component for EGS, not only concerning the technical approch but also environmental impact. Recent research has been carried out to develop waterless fracturing technologies for EGS, including foam-based hydrofracking, where foams are mixtures of gas and liquid fluids. Foam fracturing fluids have potential benefits over water-based fluids because of less water consumption, less damage in water sensitive formations, and less liquid to recover and handle after fracturing process. One challenge for implementing foam fracturing in EGS is to achieve stable foams at high temperatures, as the foam stability tends to decay with increase in temperature.This research is focused on investigating the stability of foams at high temperatures. Preliminary results show that 1) N2 foams are more stable compared to CO2 foams, 2) foams containing only surfactants were not stable at high temperatures, and 3) addition of different stabilizing agents increased the thermal stability of all the foams. It was observed that different stabilizing agents interacted differently with anionic, nonionic, and cationic surfactants. It was further shown that the crosslinking agent with a base solution of pH 6 and graphene oxide dispersion was the most effective stabilizing agent for anionic surfactant AOS, while bentonite clay effectively enhanced thermal stability for another anionic surfactant SDS. SiO2 nanoparticles showed limited improvement in thermal stability at high temperatures compared to other stabilizing agents.

Thakore, Virensinh↗
DOE OSTI2020

Study of Foam Fracturing for Enhanced Geothermal Systems Using Model Material

Foam fracturing is considered a potential approach to address water concerns with hydraulic fracturing in the development of enhanced geothermal systems (EGS). In many EGS sites, water required for hydraulic fracturing is either unavailable, extremely costly or environmentally unsustainable. This paper presents work performed in the first year of a project sponsored by the U.S. DOE GTO Waterless Stimulation Initiative that investigates foam fracturing as an alternative approach to hydraulic fracturing. One element of this work explores foam formulations that are suitable for EGS conditions. A separate paper submitted to this workshop covers the relevant work on foam characterization (Thakore et al., 2020).This paper describes the development of an experimental setup for studying the applicability of foam fluids for hydraulic fracturing along with enhanced fracture efficiency through cyclic pressurization. The system is equipped with pulse rate automation, dual pulse valves for enhanced pulsation amplitudes, and other functions. It can perform static injection at a given pressurization rate, and pulsed injection at specified pulse size and rate. Both single-phase and foamed fluids can be used. Initial experimental results of foam fracturing using cement as a model material are reported. The observations from the experimental work will be presented and discussed.

Wang, Hong↗
DOE OSTI2020

Risk-Oriented PMU Placement Approach in Electric Power Systems

The objective of traditional PMU placement approaches is to use the minimal number of PMUs to cover system complete observability. Very few consider the probability and the consequence severity of observability loss. For example, following line trip, grid topology changes, which may results in observability loss and overloaded lines. As long as the overloaded lines are still observable, then we can take action in time to correct the system; and the consequence of the observability loss is not severe, although some parts of the system is not observable. Otherwise, if the overloaded lines become unobservable, then we lose the situation awareness of the system and the consequence of the observability loss is severe. To address the above points in PMU placement, this paper proposes a risk-oriented PMU placement approach. The proposed approach uses two indices, namely the probability of observability loss and its corresponding consequence of severity to design the optimal PMU placement strategy. We show that our proposed approach is able to capture the critical events while the traditional approaches fail to do so. Extensive simulation results carried out on the IEEE 39-bus and 118-bus systems demonstrate its effectiveness.

observability, risk of observability loss, PMUs pl↗
DOE OSTI2020

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

We discuss how traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

42 ENGINEERING↗
DOE OSTI2019

High-throughput investigation of crystal-to-glass transformation of Ti–Ni–Cu ternary alloy

A high-throughput investigation of metallic glass formation via solid-state reaction was reported in this paper. Combinatorial multilayered thin-film chips covering the entire Ti-Ni-Cu ternary system were prepared using ion beam sputtering technique. Microbeam synchrotron X-ray diffraction (XRD) and X-ray fluorescence (XRF) measurements were conducted, with 1,325 data points collected from each chip, to map out the composition and the phase constitution before and after annealing at 373 K for 110 hours. The composition dependence of the crystal-to-glass transition by solid-state reaction was surveyed using this approach. The resulting composition-phase map is consistent with previously reported results. Time-of-flight secondary ion mass spectroscopy (ToF-SIMS) was performed on the representative compositions to determine the inter-diffusion between layers, the result shows that the diffusion of Ti is the key factor for the crystal-to-glass transition. In addition, both layer thickness and layer sequence play important roles as well. This work demonstrates that combinatorial chip technique is an efficient way for systematic and rapid study of crystal-to-glass transition for multi-component alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information

A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach light-off temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.

V2V, Cooptimization↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
DOE OSTI2019

Distributed Energy Resources Coordination over Time-varying Directed Communication Networks

In this paper, we consider an optimal coordination problem for distributed energy resources (DERs) including distributed generators and energy storage devices. We first propose an algorithm based on the push-sum and gradient method to optimally coordinate storage devices and distributed generators in a distributed manner. In the proposed algorithm, each DER only maintains a set of variables and updates them through information exchange with a few neighboring DERs over a time-varying directed communication network. We show that the proposed distributed algorithm with appropriately chosen step-sizes solve the optimal DER coordination problem if the time-varying directed communication network is uniformly jointly strongly connected, which is a mild condition on the connectivity of communication topologies. The proposed distributed algorithm is illustrated and validated by numerical simulations.

Yang, Tao↗
DOE OSTI2019

Additive Manufacturing of Bi-Continuous Piezocomposites With Triply Periodic Phase Interfaces for Combined Flexibility and Piezoelectricity

An additive manufacturing-enabled bi-continuous piezocomposite architecture is presented to achieve mechanical flexibility and piezoelectricity simultaneously in piezoelectric materials. This architecture comprises an active ferroelectric ceramic phase and a passive flexible polymer phase, which are separated by a tailorable phase interface. Here, triply periodic minimal surfaces were used to define the phase interface, due to their excellent elastic properties and load transfer efficiency. A suspension-enclosing projection-stereolithography process was used to additively manufacture this material. Postprocesses including polymer infiltration, electroding, and poling are introduced. Piezoelectric properties of the piezocomposites are numerically and experimentally studied. The results highlight the role of tailorable triply periodic phase interfaces in promoting mechanical flexibility and piezoelectricity of bi-continuous piezocomposites.

42 ENGINEERING↗