Dynamic Behavior Of Process Research Articles

Laser power modulation between disturbed and undisturbed material removal regime in laser chemical processing

Laser chemical machining (LCM) is a method for removing material by thermally induced chemical dissolution of self-passivating metals. However, the process window is limited by disrupted material removal due to gas bubble formation and metallic salts and oxides deposition at higher energy input. Since the temperature increases, and therefore gas bubble growth takes time, it is hypothesized that the temporal modulation of laser power can remove the metal homogeneously, i.e., without disrupted material removal, while achieving a higher removal rate. Based on this, the dynamic process behavior of material removal is investigated for the LCM of titanium in phosphoric acid, using a rectangular modulation of the laser power with varying irradiation durations. As a result, however, high-speed videos show that gas bubbles are consistently generated, regardless of the applied laser power and power modulation, although the quantity of bubbles varies with different parameters. Even with short power durations (10 ms), the material deposition occurs after multiple irradiations. When the duration is longer, the material deposition increases in height along the laser scan direction. For the studied process parameters, a Fourier analysis in the spatial domain further indicates the correlation between the material removal frequencies and the modulation frequencies. In conclusion, the laser power modulation cannot prevent the disturbed material removal at high laser powers. Nevertheless, the material deposition can be utilized to generate periodic surface structures with a depth below and above the initial surface.

Parameter Optimization for Laser Peen Forming on 6005A-T6 Aluminum Alloy Plates to Enhance the Constrained Deformation of Integral Stiffened Plates.

Multiscale parameter optimization for laser peen forming (LPF) on 6005A-T6 aluminum alloy plates was conducted through a combination of simulation and experimentation. By obtaining the optimal parameter, this study aims to explore the constrained deformation and forming laws of the integral stiffened plates. Detailed descriptions were provided regarding the dynamic response process and transient behavior of aluminum alloy plates under ultrahigh strain rates, along with an in-depth analysis of the stress evolution. The results reveal that laser beam diameter and laser beam energy can achieve large range forming, while the number of tracks facilitates the precise deformation adjustment. During the 12-track LPF process, there is an overall upward trend in deformation values accompanied by a dynamic increase in the bend curvature. After static relaxation, the deformation value recovers to 55.2% of the final bending curvature. The chord direction scanning of stiffened plates exhibits a larger bending curvature, indicating its greater forming capacity for large-sized single unfolding direction formation; whereas, the unfolding direction scanning of stiffened plates excels in achieving efficient integrated two-way forming.

Optimization of Power to Gas system with cooled reactor for CO2 methanation: Start-up and shut-down tests with Ru-based and Ni-based kinetics

The dynamic behavior of a methanation process is analyzed in the context of Power to Gas (PtG) technology, to convert carbon dioxide from different sources and hydrogen produced via electrolysis with renewable energy to synthetic methane. The process configuration consists of a single cooled plug flow reactor (PFR) where both ruthenium and nickel-based kinetic models are tested in the Aspen Dynamics simulation environment. In particular, dynamic analyses are performed to study the start-up and shut-down phases and respect injection parameters in the existing gas network. The process consists of three main sections: conditioning, reaction and separation. After the development and optimization of the control system, the start-up and shut-down tests are carried out, to study the trend of composition for the injection in the gas network. The plant size is set according to the following values: 700 Nm3/h of hydrogen and 175 Nm3/h of carbon dioxide, which correspond to the methanation reaction stochiometric ratio. Different cases are considered in both the start-up and shut-down tests, by changing the GHSV values and the catalyst adopted. Moreover, in the shut-down test two different rates are considered to evaluate the behavior of the system.

Numerical method research and characteristics analysis on frozen start-up process of high-temperature heat pipe

Future space exploration technology requires a long-life and reliable power source that is not reliant on solar energy. Space micro-reactors are able to meet this need, with Heat Pipe Cooled Reactors (HPR) emerging as a notable type of space micro-reactor that has attracted widespread attention in recent years. The HPR utilizes high-temperature alkali metal heat pipes for heat transfer, which presents certain complexities due to the solid state of the alkali metals working medium at room temperature. This results in a three-phase transition during the high-temperature heat pipes start-up process, which significantly impacts the heat transfer characteristics and dynamic behavior of the HPR start-up process. Consequently, thorough research is necessary in this area. Numerical simulation is a crucial tool that can effectively analyze, predict, and guide experiments. This article utilizes the Finite Volume Method (FVM) to develop a simulation code for high-temperature heat pipe frozen start-up. Various physical models are integrated to describe different components of the heat pipe: the container is represented by a two-dimensional axisymmetric heat conduction equation, the wick region utilizes a Fixed Grid Method (FGM) to depict the melting process of the medium, and the vapor channel is described through a two-dimensional axisymmetric compressible laminar flow. The wick region and vapor channel are coupled through the evaporation and condensation of the medium. For the vapor channel, numerical methods such as SIMPLE and PISO are used for solving. Adaptive time step and OpenMP acceleration are employed in the code to enhance computational efficiency. Finally, by comparing the calculated results with experimental data, the feasibility and accuracy of the code are assessed, highlighting special phenomena during the start-up process. The findings confirm that the developed code accurately predicts parameter changes during start-up, and can serve as a heat pipe analysis module for multi-physics coupling analysis of HPR.

An Interpretable Light Attention–Convolution–Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention–convolution–gate recurrent unit (LACG) architecture with three sub-modules—a basic module, a brand-new light attention module, and a residue module—that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.

Dynamic learning of flue gas desulfurization process using deep LSTMs neural network

SO2 emissions are known to pose great harm to both human health and atmospheric air, and flue gas generated from coal-fueled power plant is the prime source of sulfur dioxide. For this reason, flue gas desulfurization (FGD) technology has found wide applications in most coal-fired power stations. Correctly describe the dynamic behavior of an FGD process is the precondition of controlling it effectively. However, FGD process modeling is by no means an easy task, as the underlying process dynamics are highly nonlinear in nature, meanwhile time-delay effect is significant therein. Long short-term memory (LSTM) network possesses remarkable long-term memory capability, hence it is anticipated to have a powerful identification capability. In this paper, the connection between deep learning and system identification is established, further a unidirectional/bidirectional LSTM deep network is designed and employed to identify a real FGD process. Simulation results clearly demonstrate the effectiveness of deep learning-based identification approach, and the superiority of deep LSTMs over other conventional identification models is also verified.

The stable sulfur isotope and abundance fluxes of reduced inorganic sulfur and organic sulfur phases recorded in the Permian-Triassic transition of the Meishan type section

Sulfur cycle fluxes implicated in the Permian-Triassic mass extinction have traditionally been studied by the sulfur phase abundances in sedimentary rocks and the stable sulfur isotopic value (δ34S) of seawater sulfate inferred from mineral sulfate analyses. This information might be complemented by studies of the reduced inorganic sulfur and organic sulfur produced following bacterial sulfate reduction. To explore this potential the δ34S and concentration analyses of total reduced inorganic sulfur (TRIS) and organic sulfur – separately in the forms of kerogen (Ker) and individual organosulfur compounds, specifically dibenzothiophenes (DBTs) – has been conducted on sediments across the Late Permian to Early Triassic marine type section of Meishan-1 (South China). The relatively steady δ34S profiles (e.g., < 5 ‰ variation) of all sulfur phases measured through much of the late Permian were indicative of a primary seawater sulfate control, but other biogeochemical modulators caused prominent δ34S fluctuations of TRIS and DBT adjacent to the extinction event. The late Triassic δ34STRIS profile of Meishan-1 displayed a notable 34S enrichment (+15 ‰ increase) in bed 22–24 sediments concomitant with lower δ34SDBT values (−7 ‰ decrease), whereas co-eval δ34SKerS values remained relatively constant. The contrasting δ34SDBT and δ34SKerS data suggests the dynamic behavior of specific diagenetic sulfurisation processes may be resolved by the δ34S of discrete organic sulfur compounds (i.e., dibenzothiophenes, DBTs), but dissipated by the sulfurisation collective represented by the bulk kerogen fraction. The inverse isotopic trend observed between DBT and TRIS resulted in negative Δδ34SDBT-TRIS values identifying an organic sulfurisation pathway(s) with an unusual preference over pyrite (FeS2) for the lighter stable sulfur isotope. A redox control of the δ34SDBTs and δ34STRIS deviations in the bed 22–24 extinction interval was confirmed by coincident variation in TRIS/(TRIS + KerS) and pyrite (Py) and highly reactive (HR) iron ratios (FePy/FeHR). The iron (Fe) speciation data indicated a transition to ferruginous conditions, ruling out Fe2+ limitation as a factor in the bias against 34S evident in DBT formation. The 34S depletion of the DBTs promoted by the ferruginous setting may arise from the rapid and irreversible reaction of organic substrates with labile sulfur anions (e.g. HS-) or be supported by an especially localised sediment–water depositional microenvironment. Our study highlights the potential of incorporating stable sulfur isotope analytics of reduced and organic sulfur phases, particularly of specific organic compounds, into a holistic assessment of the dynamic sulfur biochemical periods of Earth’s past.

Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks

Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot of attention recently for its exceptional modeling capabilities of nonlinear systems, are an essential candidate for model-based control systems. In particular, recurrent neural networks (RNN) have shown remarkable capacity to forecast the dynamic behavior of nonlinear processes using time-series data. In order to regulate the rate at which chemical species are produced in intricate processes, RNNs have successfully served as the predictive model in model-based controllers. However, the necessity for massive amounts of data to capture complex processes is a drawback of typical RNN models. With the goal of efficient utilization of the available data, a different, transfer learning-based training approach for RNNs is presented in this work. The transfer learning strategy is taken into consideration to overcome the challenges stemming from lack of data for the construction of RNN models. In particular, weight-sharing RNNs are developed using a priori physical knowledge. Next, a comprehensive analysis of a complex chemical process on a large scale is conducted to showcase the benefits of the suggested approach across various data sizes and its effectiveness when combined with MPC in contrast to conventional RNNs.

Performance and dynamic behavior of H2 layered-bed PSA processes using various activated carbons and zeolite LiX for steam methane reforming gas

Layered-bed pressure swing adsorption (PSA) processes using activated carbons (ACs) and zeolite LiX were investigated to elucidate the performance and dynamic behavior of each AC in H2 separation. Experimental adsorption and re-adsorption breakthrough characteristics of ACs (CAC: parent AC, KACa: KOH-activated CAC, and KACi: KOH-impregnated CAC) and zeolite LiX were compared using a steam methane reforming gas. A rigorous dynamic model was used to evaluate the performance of four and six layered-bed PSA processes in terms of purity, recovery, productivity, and impurities. The KACi bed demonstrated the longest breakthrough time of impurities, but its desorption performance was adversely affected by the strong affinity induced by intercalated potassium compounds. While KACa showed higher adsorption capacities than CAC, its slow adsorption rate resulted in the shortest breakthrough time. On the other hand, CAC presented a separation performance between KACi and KACa beds, but its weak CO adsorption affinity led to a relatively wide CO mass transfer zone. Zeolite LiX demonstrated the longest CO breakthrough time, indicating its potential to purify trace CO in the layered-bed, but its layer length must be carefully decided to prevent CO2 propagation. The four layered-bed PSA produced a fuel cell grade H2 (99.9999% purity, 81.54% recovery, 115 mol/kg/day productivity, and < 0.2 ppm CO), which was further improved by the six layered-bed PSA to 1.5% at the same H2 purity. The dynamic behaviors and sensitivity analysis (P/F ratio, adsorption time, AC/LiX ratio, AC type, and number of beds) provide valuable insights and guidelines for the design and optimization of H2 layered-bed PSA.

Aging behavior of biodegradable polylactic acid microplastics accelerated by UV/H2O2 processes

The usage of biodegradable plastics is expanding annually due to worldwide plastic limits, resulting in a substantial number of microplastics (MPs) particles formed from biodegradable plastic products entering the aquatic environment. Until now, the environmental behaviors of these plastic product-derived MPs (PPDMPs) have remained unclear. In this work, commercially available polylactic acid (PLA) straws and PLA food bags were used to evaluate the dynamic aging process and environmental behavior of PLA PPDMPs under UV/H2O2 conditions. By combining scanning electron microscopy, two-dimensional (2D) Fourier transform infrared correlation spectroscopy (COS) and X-ray photoelectron spectroscopy, it was determined that the aging process of the PLA PPDMPs was slower than that of pure MPs. The 2D-COS analysis revealed that the response orders for the functional groups on the PLA MPs differed during the aging process. The results demonstrated that the oxygen-containing functional groups of the PLA PPDMPs were the first to react. Subsequently, the –C–H and –C–C− structural responses began, and the polymer backbone was ruptured by the aging process. However, the aging of the pure-PLA MPs started with a brief oxidation process and then breakage of the polymer backbones, followed by continuous oxidation. Moreover, compared to the PLA PPDMPs, the pure-PLA MPs exhibited a greater adsorption capacity, which was increased by 88% after aging, whereas those of the two PPDMPs only increased by 64% and 56%, respectively. This work provides new insights into the behaviors of biodegradable PLA MPs in aquatic environments, which is critical for assessing the environmental risks and management policies for degradable MPs.

Astromorphic Self-Repair of Neuromorphic Hardware Systems

While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair.

Coalescence dynamics of droplets collision on substrates with different contact angles

Droplet collision coalescence is widely encountered in the petroleum industry, chemical industry, and agricultural applications. In this study, the dynamic behavior of the coalescence process of a free-falling droplet impacting a sessile droplet on substrates with different wettabilities is investigated via high-speed photography. The influences of the substrate contact angle (α) and the Weber number (We) of the droplet collision were analyzed. Based on the experiments, the process of droplet collision coalescence was divided into four stages: coalescence, spreading, retraction, and oscillation. The maximum spreading diameters, maximum jet heights, spreading velocities, and retraction velocities of droplets with different values of the substrate contact angle and We were obtained. The dynamic mechanism of different collision coalescence stages was revealed by the interconversion of inertial forces, surface tension, and wall adhesion forces. The relationship between the dimensionless spreading coefficient and the dimensionless time was established for different values of the collision We and contact angle α based on the experimental results. The effects of We and α on the coalescence process of the droplets were obtained by analysis of the energy conversion.

Tube‐based batch model predictive control for polystyrene polymerization reaction process

AbstractThis paper focuses on product quality control issue of polystyrene polymerization reaction process. A novel tube‐based batch model predictive control (BMPC) strategy based on a data‐driven model is presented, which is inspired by the tube‐based robust model predictive control (MPC) strategy. First, the dynamic behavior of the polystyrene polymerization reaction process is captured with high accuracy by establishing a just‐in‐time learning (JITL) model. Then, the built JITL model is regarded as the nominal system, and a BMPC is designed on the basis of JITL model to obtain the nominal trajectory, which is an integrated control system framework composed of iterative learning control (ILC) and MPC. Meanwhile, another auxiliary MPC (AMPC) is designed to minimize the deviation between the actual trajectory and the nominal trajectory, and the actual tracking errors are limited to the tube invariant set centered on the nominal errors to restrain the performance deterioration of the control system caused by the modeling errors. Finally, the effectiveness of the proposed tube‐based BMPC method is verified by simulating an industrial batch polystyrene polymerization reaction process. The results indicate that the presented control algorithm not only enhances the tracking performance of the system but also provides a more robust system stability than existing methods. This paper provides a new solution to improve the product quality of polystyrene polymerization reaction process.

Physics-informed machine learning for MPC: Application to a batch crystallization process

This work presents a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model of the aspirin crystallization process is first developed to describe the formation of crystals through nucleation and growth. Then, the PIRNN modeling scheme is introduced to integrate observational data and mechanistic models for the development of machine learning models. Additionally, the physical constraints on process states are embedded in the machine learning models to prevent physically unreasonable predictions. Subsequently, the PIRNN model that captures the dynamic behavior of the batch crystallization process is utilized in the design of model predictive controller that optimizes the operation of the crystallizer. Through open-loop and closed-loop simulations, it is demonstrated that the PIRNN models using less training data achieve prediction accuracy and closed-loop performance comparable to the purely data-driven model.

A novel strategy for dynamic modeling of genome-scale interaction networks.

The recent availability of omics data allows the construction of holistic maps of interactions between numerous role-playing biomolecules. However, these networks are often static, ignoring the dynamic behavior of biological processes. On the other hand, dynamic models are commonly constructed on small scales. Hence, the construction of large-scale dynamic models that can quantitatively predict the time-course cellular behaviors remains a big challenge. In this study, a pipeline is proposed for the automatic construction of large-scale dynamic models. The pipeline uses a list of biomolecules and their time-course trajectories in a given phenomenon as input. First, the interaction network of the biomolecules is constructed. To state the underlying molecular events of each interaction, it is translated into a map of biochemical reactions. Next, to define the kinetics of the reactions, an ordinary differential equation (ODE) is generated for each involved biomolecule. Finally, the parameters of the ODE system are estimated by a novel large-scale parameter approximation method. The high performance of the pipeline is demonstrated by modeling the response of a colorectal cancer cell line to different chemotherapy regimens. In conclusion, Systematic Protein Association Dynamic ANalyzer constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which are critical for the simulation of therapeutic interventions in precision medicine. Detailed information about the constructed large-scale model of colorectal cancer is available in supplementary data. The SPADAN toolbox source code is also available on GitHub (https://github.com/PooyaBorzou/SPADAN). Supplementary data are available at Bioinformatics online.

Arc shape and dynamic behavior of the Tri-Arc twin-wire GMAW process

Arc shape and dynamic behavior of the Tri-Arc twin-wire GMAW process

Machine Learning-Based MPC of Batch Crystallization Process Using Physics-Informed RNNs

This work presents a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model of aspirin crystallization process is first developed to describe the formation of crystals through nucleation and growth. Then, the PIRNN modeling scheme is introduced to integrate observational data and mechanistic models for the development of machine learning models. Subsequently, the PIRNN model that captures the dynamic behavior of the batch crystallization process is utilized in the design of model predictive control. Through open-loop and closed-loop simulations, it is demonstrated that the PIRNN models using less training data achieve prediction accuracy and closed-loop performance comparable to the purely data-driven model.

Experiments on the Dynamic Behavior of the Chemical Looping Combustion Process

AbstractThe dynamic operation of thermal power plants becomes more and more important due to more diverse and volatile power sources. Load changes of biomass, bituminous coal and methane were conducted during six experimental runs in a chemical looping combustion pilot plant which combines power generation and CO2 capture. The resulting step responses of the hydrodynamics and gas concentrations were compared to each other to understand the transient behavior of the interconnected fluidized bed system. The influence of the fuel rate and solid circulation on the response time and intensity was assessed.

Generalized autoregressive score models based on sinh-arcsinh distributions for time series analysis

Models with time-varying parameters have become more popular for time series analysis. Among these models, Generalized Autoregressive Score (GAS) models are based on the specification of the mechanism through which past observations of the variable of interest affect the current value of the time-varying parameters. GAS models allow capturing the dynamic behavior of time series processes, which is an advantage over models such as ARMA and GARCH with fixed parameters. In this paper, we extend the distribution setting of GAS models from classical densities to sinh-arcsinh (SAS) ones, with emphasis on SAS-Gaussian and SAS-t distribution. The SAS family provides flexible distributions that allow modeling the asymmetry as light or heavy tailed. The parameters of the family enable clear interpretations, and limiting distributions are especially appealing as shape parameters tend to their extreme values. The proposed method’s performance is illustrated in simulations and a real-world application to a fish condition dataset. In conclusion, the SAS-Gaussian distribution fits the dataset best by far.

Effects of the ground-electrode temperature on the plasma physicochemical processes and biological inactivation functions involved in surface dielectric barrier discharge

In this work, a surface dielectric barrier discharge (SDBD) device coupled with power electronics technology was designed for precise control of the ground-electrode temperature to investigate the dynamic behavior of the physicochemical processes and biological inactivation functions involved in SDBD plasma. It was found that an increase of the electrode temperature from 30 to 210 °C reduced the breakdown voltage and increased the current pulse amplitude because the reduced electric field strength and average electron density of the SDBD plasma were consistently enhanced. The change in the plasma-chemistry mode (O3-dominant to NO x -dominant) was more sensitive to the ground-electrode temperature than that of the power density and gas temperature. O3 in the gas and liquid phases could not be detected at electrode temperatures above 90 °C, and the NO x mode almost immediately occurred after the plasma was turned on for ground-electrode temperatures of ⩾180 °C. The increase in the electrode temperature increased the acidity of the plasma-activated water and, more importantly, short-lived reactive species OH and NO were detected at electrode temperatures ⩾120 °C in the case of aqueous solutions treated directly with SDBD plasma. The biological inactivation function of the SDBD plasma, i.e. for bacterial suspensions and tumor cell cultures, was improved by about three orders of magnitude and 40% at the optimal electrode temperatures of 180 °C and 120 °C, respectively. This is an important breakthrough for development of SDBD-based biomedical devices for specific purposes on a commercial level by regulating the plasma chemistry through the ground-electrode temperature, overcoming the limitations of chamber heating and compressed air supply.