Process Model Research Articles

Practical evaluation and theoretical modeling of charging process for Lead Acid batteries in PV systems

Practical evaluation and theoretical modeling of charging process for Lead Acid batteries in PV systems

Design of Experiments-Driven Optimization of Spray Drying for Amorphous Clotrimazole Nanosuspension.

This study employed a Quality by Design (QbD) approach to spray dry amorphousclotrimazole nanosuspension (CLT-NS) consisting of Soluplus® and microcrystallinecellulose. Using the Box-Behnken Design, a systematic evaluation was conducted toanalyze the impact of inlet temperature, % aspiration, and feed rate on the criticalquality attributes (CQAs) of the clotrimazole spray-dried nanosuspension (CLT-SDNS). In this study, regression analysis and ANOVA were employed to detect significantfactors and interactions, enabling the development of a predictive model for the spraydrying process. Following optimization, the CLT-SD-NS underwent analysis using Xraypowder diffraction (XRPD), Fourier transform infrared spectroscopy (FTIR), Dynamic Scanning Calorimetry (DSC), and in vitro dissolution studies. The resultsshowed significant variables, including inlet temperature, feed rate, and aspiration rate,affecting yield, redispersibility index (RDI), and moisture content of the final product. The models created for critical quality attributes (CQAs) showed statistical significanceat a p-value of 0.05. XRPD and DSC confirmed the amorphous state of CLT in theCLT-SD-NS, and FTIR indicated no interactions between CLT and excipients. In vitrodissolution studies showed improved dissolution rates for the CLT-SD-NS (3.12-foldincrease in DI water and 5.88-fold increase at pH 7.2 dissolution media), attributed torapidly redispersing nanosized amorphous CLT particles. The well-designed studyutilizing the Design of Experiments (DoE) methodology.

Data-scarce surrogate modeling of shock-induced pore collapse process

Data-scarce surrogate modeling of shock-induced pore collapse process

Market Participation and Farmers’ Adoption of Green Control Techniques: Evidence from China

As a standard clean production technology, the wide use of green control techniques (GCT) helps improve the quality of agricultural products and protect the environment. However, the level of application of GCT by Chinese farmers is not high. The market, as the ultimate place to realize the value of grain and the returns of grain farmers, plays an essential part in promoting GCT. Based on survey data from grain farmers in Sichuan Province, China, this study used a conditional mixed process (CMP) model to examine the effect of farmers’ market participation on their GCT adoption behavior and a mediated effects model to test the impact mechanism. The study further explored the effect of farmers’ market participation capability on their GCT adoption behavior. The results showed that (1) farmers’ market participation could significantly increase the likelihood of adopting GCT. (2) Market participation could influence farmers’ adoption behavior through agricultural income, market information, and green cognition. (3) Further research found that farmers were more likely to adopt GCT if their market participation capability was strong. These findings highlight the fact that developing countries have increased their efforts to encourage farmers to participate in markets and to enhance their market participation capability, thereby facilitating the adoption of GCT by farmers.

A new failure analysis and validation scheme for shale gas pilot processes

Shale gas pilot processes are usually composed of skid-mounted assemblies of individual equipment, with weak process matching, unknown unit equipment wear, and high failure rates. We propose a solution using the grey relational analysis model, analytical hierarchy process model, and reliability analysis model to achieve failure analysis, prediction, and improvement validation, respectively. The probability and degree of influence relationships between the factors affecting the occurrence of failures are discussed. The practical application of the study area shows that: when the threshold value is set to 0.8833, it can match the grey correlation conclusion well, connect the logic of hierarchical analysis, and cluster the engineering problems; this study area needs to focus on preventing the problems of erosion damages and puncture leakage. Although the short life of the nozzle has a large impact on the overall process, but the frequency of occurrence is not high, the next step needs to continue to collect data, using other mathematical models to mine the fault data internal information.

Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division

Remaining life prediction (RUL) is a critical link of maintenance decision-making, the accurate RUL prediction is an important means to monitor the operating status and achieve the safe operation of equipment. However, existing studies rarely considered the multi-stage characteristics of indicator fusion in the degradation process, and directly used the Wiener process to establish degradation model, which results in significant errors in RUL prediction results. Therefore, to solve above issues, a two-stage RUL prediction method of bearing based on the Wiener process model with data fusion and stage division is proposed in the paper. Firstly, the concept of multi-feature fusion is introduced to construct a comprehensive health indicator (CHI) that considers indicator performance. After that, a two-stage RUL prediction model based on the CHI is developed, and a method for detecting change points and dividing stages is proposed. Finally, the effectiveness and predictability of the proposed method and CHI are demonstrated based on the bearing test datasets.

Relay feedback identification and PI-PD control of asymmetrical heating and cooling processes

A common type of nonlinear system in the field of process industry is asymmetrical heating and cooling processes. These systems have two operating modes, heating and cooling, each with a particular dynamic feature. This study deals with identifying and controlling such processes. The relay feedback identification method based on the state-space approach, which results in exact estimates, is used in the identification procedure to achieve two first-order plus dead time (FOPDT) process models. Following process identification, two sets of PI-PD controller parameters are acquired in line with the user-defined gain and phase margin specifications, and the system is controlled using a gain-scheduled PI-PD control method. Some simulation examples are provided to demonstrate the use and profitability of the suggested identification and control approaches. Comparisons with a similar study are provided for both identification and control approaches to clearly demonstrate the benefits of the suggested procedures.

Аналіз впливу на прохідність автобронетанкової техніки конструктивних факторів та методика її збільшення шляхом автоматичного регулювання тиску повітря в шинах коліс

The purpose of the study is to analyze and determine the influence of various factors on the patency of armored vehicles (AMT) operated by the National Guard of Ukraine. Determination of the change in patency during the movement of AMT in different road and climatic conditions. Analysis of static and dynamic loads, which are characteristic of AMT movement over rough terrain, and their impact on the efficiency of the vehicle. The theoretical component of the study was carried out using mathematical modeling of the work process of regulating the air pressure in tires of AMT wheels, using the example of changes in the characteristics of adhesion coefficients with the supporting surface and rolling resistance depending on the change in pressure and load on the tires of the wheels when the AMT is moving off-road. As a result of mathematical modeling, the dependences of air pressure in tires of AMT wheels were obtained, depending on the coefficient of rolling resistance and the load on each of the wheels. Which, in turn, makes it possible to move to a sufficiently complete mathematical modeling of the movement of AMT on off-road, taking into account the reference cross-country ability. In the process of theoretical research, the structural parameters of the impact on the patency of AMT were analyzed quite completely, and a generalized comparative indicator of the patency parameters of vehicles was proposed. When analyzing it, it was found that the AMT tire and, specifically, the change in air pressure in it, have a significant impact on the bearing capacity. The authors propose to use the following evaluation criteria as criteria for assessing the efficiency of the passability when driving on deformable surfaces: a generalized comparative indicator of the parameters of the passability of vehicles. As a result of consideration of which, a significant influence on the patency parameters of the tires and directly the air pressure in them was established. Regulation and change of the air pressure in the tires of the wheels depending on the change in the coefficient of rolling resistance as well as the change in the load on each wheel, which are the ratio of the work that is spent on the traction force when overcoming different degrees of heaviness of the resistance of the supporting surfaces with the effect of deformation. And the load that falls on each of the AMT wheels allowed us to draw conclusions about the improvement of the regular tire pressure regulation system (RPRS) of the wheels.

Power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing

Power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing

Development and qualification of 3 L scale-down model for large scale vaccine process on vero cell culture using microcarriers.

Scale-down models (SDM) are pivotal tools for process understanding and improvement to accelerate the development of vaccines from laboratory research to global commercialization. In this study, a 3 L SDM representing a 50 L scale Vero cell culture process of a live-attenuated virus vaccine using microcarriers was developed and qualified based on the constant impeller power per volume principle. Both multivariate data analysis (MVDA) and the traditional univariate data analysis showed comparable and equivalent cell growth, metabolic activity, and product quality results across scales. Computational fluid dynamics simulation further confirmed similar hydrodynamic stress between the two scales.

B-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT

Objective. Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. Specifically, we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests, which demonstrate b-MAR improvements of >1.4131 dB in PSNR, >0.3473 HU decrements in RMSE, and >0.0025 promotion in structural similarity index measurement over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model’s artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

Enhancing fusion welding in die-cast Mg alloys: A combined ultrasonic vibration and hot rolling approach for porosity reduction, microstructure refinement, and mechanical property improvement

Severe weld porosity has led the industry to believe that die-cast magnesium alloys cannot be fusion welded. To address this challenge, a new process of ultrasonic vibration coupled with hot rolling was employed to eliminate porosity, and the mechanism associated with weld porosity in die-cast magnesium alloys was analyzed in detail. Unlike conventional welding processes, ultrasonic vibration coupled with hot rolling is effective and efficient. In this study, a roller with ultrasonic vibrations is synchronized with the molten pool to apply pressure to the weld, which is in a semi-solid state on the upper surface. The results showed that hot rolling and ultrasonic vibration coupled with hot rolling could eliminate 78.12 % and 85.5 % of the porosity, respectively. Compared to hot rolling, the ultrasonic vibration coupled with the hot rolling process resulted in smaller and fewer porosities and finer β-Mg17Al12. This is attributed to the cavitation effect of ultrasonic vibration that promotes bubble overflow and breaks the β-Mg17Al12 dendrites in the melt pool, as well as the thermal effect that improves the plasticity of the weld. Furthermore, after closing the porosity under pressure, recrystallized grains will be produced around it. Tensile test results indicated that hot rolling was able to increase the ultimate tensile strength by 84.21 % and elongation by 44.03, whereas ultrasonic vibration coupled with hot rolling was able to increase the ultimate tensile strength by 244.46 % and elongation by 71.70 %. Combining the micron-scale industrial computed tomography results with theoretical calculations, it is found that the porosity of die-cast magnesium alloys is mainly caused by nitrogen, and they originate from gas entrapment during the die-casting process; further, the formation mechanism of die-cast magnesium alloys' fusion-welding porosity is revealed in detail. A physical model of the formation process of welded porosity morphology was established, and the analysis suggested that the pattern of the inner wall of the porosity was related to the Kelvin–Helmholtz instability. This research is expected to improve the weld quality of alloys manufactured by the die-casting process.

Sorption of zinc chloride complexes from acid-based solutions by using anionic resins: process optimization and modelling

Sorption of zinc chloride complexes from acid-based solutions by using anionic resins: process optimization and modelling

Analyzing Data Streams from Cyber-Physical-Systems: A Case Study

We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point.

Experimental and artificial intelligence optimization of paint wastewater (PWW) coagulation using novel Phaseolus vulgaris seed extract (PVSE)

This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel Phaseolus vulgaris seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1oC.

Sequential Infiltration Synthesis of Al2O3 in PMMA Thin Films: Temperature Investigation by Operando Spectroscopic Ellipsometry.

Sequential infiltration synthesis (SIS) is a scalable and valuable technique for the synthesis of organic-inorganic materials with several potential applications at the industrial level. Despite the increasing interest for this technique, a clear picture of the fundamental physicochemical phenomena governing the SIS process is still missing. In this work, infiltration of Al2O3 into thin poly(methyl methacrylate) (PMMA) films using trimethyl aluminum (TMA) and H2O as precursors is investigated by operando dynamic spectroscopic ellipsometry (SE) analysis. The TMA diffusion coefficient values at temperatures ranging from 70 to 100 °C are determined, and the activation energy for the TMA diffusion process in PMMA is found to be Ea = 2.51 ± 0.03 eV. Additionally, systematic data about reactivity of TMA molecules with the PMMA matrix as a function of temperature are obtained. These results provide important information, paving the way to the development of a comprehensive theory for the modeling of the SIS process.

Optimization of Hydrochemical Leaching Process of Kaolinite Fraction of Bauxite with Response Surface Methodology

A technology for the hydrochemical processing of the kaolinite fraction of bauxite has been developed, and it involves preliminary chemical activation in a sodium bicarbonate solution and alkaline leaching in a recycled high-modulus solution with the addition of an active form of calcium oxide. Chemical activation allows for the transformation of the difficult-to-explore kaolinite phase to form easily soluble phases of dawsonite, sodium hydroaluminosilicate and bemite. An active, finely dispersed form of calcium oxide was obtained as a result of CaO quenching in Na2SO4 solution at elevated temperature and pressure. Using a central composite design (CCD) via response surface methodology (RSM), the technological leaching mode was achieved. The influence on the leaching process was studied by adjusting the CaO/SiO2 ratio, temperature, alkaline solution concentration and duration. It was found that the determining factors are the concentration of the leaching solution and the temperature. At a stable CaO/SiO2 ratio, a combination of these two factors determines the duration of the process to achieve the predicted degree of recovery. The results of experiments carried out using the developed model of the leaching process confirmed the validity of the calculated indicators, with an error of 2.01%. In an optimal technological mode at a Na2O leaching solution concentration of 260 g/L, a temperature of 260 °C, a CaO/SiO2 ratio of 1.5 and a leaching time of 5 h, the extraction of Al2O3 into the solution was 89.7%, which is close to the value of 87.9% predicted by the model.

Influence of Rolling Resistance and Particle Size Distribution in the Simulation of Sand Infiltration into the Static Gravel Bed

Fine sediment infiltration and subsequent clogging in a gravel bed affect several fluvial, ecological, and biological processes, resulting in the degradation of the river ecosystem. Despite many experimental and a few numerical studies, the process is yet to be entirely understood. We employed a pure Lagrangian framework, called the Discrete Element Method (DEM), to numerically investigate the infiltration process. Special attention is given to tackling the issue of non-spherical and irregular particle shapes and particle size distributions (PSDs) in numerical simulations. Due to computational limitations, these aspects were either not considered or simplified in previous numerical studies. We implicitly included non-spherical and irregular shape effects through rolling resistance models, which do not cause excessive computational overhead. Our study shows that rolling resistance models greatly influence packing and fine sediment infiltration. However, they may also lead to unphysical particle behavior; thus, they should be carefully used in numerical simulations. Oversimplified PSDs do not mirror natural systems, and full PSDs pose computational challenges. Sufficient grain classes are needed to mimic the non-homogeneity and poly-dispersity found in natural fluvial sediments. The infiltrating characteristics of sand concerning PSD and shape effects are linked to size ratio D15,Gravel/D85,Sand, assuring physical and realistic modeling of the infiltration process.

Detecting rhythmic spiking through the power spectra of point process model residuals.

Objective.Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach.In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nrmilliseconds. Finally, we compute the PSD of the model's residuals.Main results.We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance.These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Detecting rhythmic spiking through the power spectra of point process model residuals.

Objective.Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach.In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nrmilliseconds. Finally, we compute the PSD of the model's residuals.Main results.We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance.These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.