Process Parameter Fluctuations Research Articles

Rupture energy coupling of alkali washing tower cylinder

The energy generated by the container during the explosion induces destructive effects on the surrounding objects and itself, such as deformation, compression, rupture, etc. The explosive energy is one of the richest pieces of evidence in the analysis of the accident cause, and therefore a comprehensive analysis of the accident explosive energy is essential for determining accident properties. Considering the rupture accident of an alkali washing tower in an aromatic hydrocarbon plant as an example, the destructive energy and the rupture internal pressure are estimated to ascertain the explosive properties of the accident by analyzing the field damage characteristics. This study aims to conduct a comprehensive analysis and calculation of the energy associated with an alkali washing tower rupture accident. The objective is to determine the cause of the accident, which can be attributed to the combined effect of wall thickness reduction and fluctuations of production process parameters. The findings of this analysis will serve as a scientific basis for the examination of similar accidents and the formulation of safety protection measures.

Transformer and cross-attention-based multi-sensor in-situ monitoring of molten pool stability and part quality in laser powder bed fusion

The fluctuations in process parameters, as a random phenomenon in the laser powder bed fusion (LPBF) process, is occasional. These subtle variations can lead to defects such as porosity if not monitored and corrected promptly. Therefore, monitoring the stability of the molten pool and small changes in process parameters is significant. For process monitoring, the commonly used Transformer architectures have demonstrated remarkable performance in single-sensor signal processing. However, as the core component of the Transformer, the self-attention mechanism has limitations in data fusion of multi-sensor signals. To this end, a novel Transformer-based deep learning method associated with the cross-attention mechanism (Trans-Cross) is proposed. Trans-Cross leverages the collected photodiode and acoustic signals to monitor the fluctuation of process parameters as well as the quality of parts. Specifically, the cross-attention mechanism is introduced to enhance representative feature extraction from photodiode and acoustic signals, exploiting their complementary information. To better characterize the raw signals, an improved adaptive variational mode decomposition (VMD) algorithm is proposed for data preprocessing. The proposed Trans-Cross achieves impressive prediction accuracies of 97.41 % for identifying the fluctuations in the process parameters and 99.73 % for part quality classification. Trans-Cross exhibits superior performance and strong robustness when dealing with constraints such as limited input signal length and training data ratio. These results validate the feasibility of the proposed method in accurately identifying the fluctuations in process parameters as well as the part quality. This work provided data guidance to enhance the stability of the LPBF process.

Robustness of macro-structured deep drawing process

Reaching robustness of forming processes is one of the main challenges in manufacturing technologies. Volatile material properties [1] and fluctuations in process parameters often result in insufficient part quality and rising production costs, especially in production chains [2]. As a common solution for this problem, numerical simulations are used to consider scattering of material parameters and define a useable process window with a specified safety margin. A new approach is the use of macro-structured tools. Result shown here indicate, in addition to the general enlargement of the useable process window [3], a high tolerance toward volatile material properties and variations of the blank. The produced parts show consistent quality with a reduction in residual stresses [4]. To examine the robustness in particular, virtual studies for steel alloy DX54 with varying alignment of the blank were performed. The resulting properties, such as the blank draw-in, thinning and springback, were examined in predefined positions on the blank to compare the experiments. The enlarged process window will be analysed and a strategy for a deeper understanding of the forming process will be generated, which will sustain the design of robust tools and processes.

Ultra-early prediction of the process parameters of coal chemical production

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

Research of On-Line Monitoring Technology Based on Laser Triangulation for Surface Morphology of Extreme High-Speed Laser Cladding Coating

This work aims to develop a novel method for on-line monitoring of coating quality during the Extreme High-speed Laser Cladding (EHLA) process. JG-11 coating was prepared by EHLA, and microstructure, microhardness, corrosion performance, and scratch resistance were investigated. To analyze the influences of fluctuations in processing parameters on coating quality, a single-factor experiment scheme was designed and an on-line monitoring system based on laser triangulation was built. Furthermore, a new forming method for the surface profile of EHLA coating was proposed, and a new comprehensive evaluation index of surface morphology was accordingly designed. Benefitting from the extremely high cooling rate, EHLA JG-11 coating had fine grains, high hardness, and better corrosion resistance and scratch resistance than those of Electroplating Hard Chromium (EHC). The results revealed that the surface morphologies presented different characteristics due to the fluctuations of process parameters, such as high surface flatness, deep pits, small peaks, poor directionality, etc. The comprehensive evaluation index composed of Sa, Ssk, and Str could effectively characterize the surface morphology of EHLA coating, which proved that the monitoring system and evaluation method could realize on-line monitoring of the process parameters during the EHLA process.

Reliability optimization of process parameters for marine diesel engine block hole system machining using improved PSO

The processing quality of the block hole system affects the working performance of the marine diesel engine block directly. Choosing an appropriate combination of process parameters is a prerequisite to improving the accuracy of the block hole system. Uncertain fluctuations of process parameters during the machining process would affect the process reliability of the block hole system, resulting in an ultra-poor accuracy. For this reason, the RBF method is used to establish the relationship between the verticality of the cylinder hole and process parameters, including cutting speed, depth of cut, and feed rate. The minimum cylinder hole verticality is taken as the goal and the process reliability constraints of the cylinder hole are set based on Monte Carlo, a reliability optimization model of processing parameters for cylinder hole is established in this paper. Meanwhile, an improved particle swarm algorithm was designed to solve the model, and eventually, the global optimal combination of process parameters for the cylinder hole processing of the diesel engine block in the reliability stable region was obtained.

Predicting springback variation and process-reliable tolerance limits of outer car-body panels by stochastic sheet metal forming simulation

Modern car-body design entails increasing requirements for the dimensional accuracy of outer car-body panels. However, tolerance limits to be met in this regard are based on specifications from component engineering aimed at ensuring dimensional accuracy of the product, less on real structural springback behaviour of outer car-body panels. Process-reliable tolerance limits for outer car-body panels can be characterized by a bandwidth in springback variation caused by fluctuations in material characteristics and process parameters during production. Differences between assumptions from component engineering and real structural springback behaviour of outer car-body panels thus leads to avoidable iterative corrections in die manufacturing and following processes. Therefore, the goal of this research work presented in this paper is to predict springback variation potentially occurring during production already in the virtual design stage of a car-body and hence set process-reliable tolerance limits. Stochastic sheet metal forming simulation is used for prediction of springback variation. Here, deterministic multi-stage springback simulation of a sidewall panel is extended by stochastic variation of material properties and process parameters. Simulation results are validated by measurement reports from series production. Results presented show strong fit in characteristics of springback variation between stochastic simulation and series measurement reports. In future, stochastic simulation results can be fed back into component engineering, making die manufacturing and following joining and assembly processes more effective.

Monitoring method for gasification process instability using BEE-RBFNN pattern recognition

To identify large fluctuations of parameters in the process of gasification, a control chart pattern recognition method based on optimized radial basis function neural network (RBFNN) is proposed in this paper to conduct pattern recognition of the parameters of gasification process. The proposed method is described in four aspects, feature description, feature extraction, classifier and algorithm learning and training. The initial data is described from shape features and statistical features to reduce the dimension of the data. The optimal characteristic set is selected by the association rule mining algorithm to reduce the complexity of the model. The performance of neural network is affected by the learning method. With RBFNN as a classifier, a learning method based on Bees algorithm is therefore put forward and then training recognition is compared between the proposed method and the traditional method. The results show that the proposed method has higher recognition rate and simpler structure than the traditional method, and it is very effective in monitoring and identifying abnormal fluctuations of gasification process parameters.

Research status of on-line monitoring of laser metal deposition

The process of laser metal deposition was easy to be affected by fluctuations of environment and process parameters, leading to the deviation of melt pool temperature or deposition morphology and so on. Accumulation of small deviation would result in the instability of deposition process and lead to the failure of forming. Therefore, the on-line monitoring of laser metal deposition process was studied, involving thermal history of forming parts, temperature and size of melt pool, deposition morphology and forming defects, and so on. In this paper, the on-line monitoring technologies, such as thermocouple temperature measurement, infrared thermal imaging, high-speed camera and other monitoring methods, were introduced, and the open-loop monitoring, closed-loop feedback control and closed-loop feedforward control technologies were illustrated in detail. According to the research situation, the future development of on-line monitoring in laser metal deposition was prospected.

Synthesis of Flexible Heat Exchanger Networks Considering Gradually Accumulated Deposit and Cleaning Management

This paper presents a flexible heat exchanger network (HEN) synthesis methodology to cope with (i) the fluctuations of uncertain process parameters and (ii) the gradually accumulated deposit, which...

Extraction of polyphenols from olive pomace: Mathematical modeling and technological feasibility in a high temperature and high pressure stirred reactor

A procedure to evaluate the technological feasibility at pilot scale of the extraction process of polyphenols from olive pomace is presented in this work. The proposed approach takes into consideration the extended kinetic route coupled with mathematical simulation. Detailed physically-based dynamic mathematical models, taking into account mass and energy balance equations, are adopted to describe both the lab-scale and the pilot-scale reactors. Chemical physical parameters appearing in the models are estimated from the experimental data at lab-scale or are partially taken from literature. Different heating systems are designed for the pilot scale reactor and their performance is tested by simulation. Characteristic times are evaluated also during start-ups and different control loops are analyzed in order to set-up the best process and operating variables. Average yields in polyphenols are finally evaluated for both the batch and the continuous operated pilot reactor, by considering feed variability and fluctuations of process parameters.

Analysis of Flat-Band-Voltage Dependent Breakdown Voltage for 10 nm Double Gate MOSFET

The existing modeling of avalanche dominated breakdown in double gate MOSFETs (DGMOSFETs) is not relevant for 10 nm gate lengths, because the avalanche mechanism does not occur when the channel length approaches the carrier scattering length. This paper focuses on the punch through mechanism to analyze the breakdown characteristics in 10 nm DGMOSFETs. The analysis is based on an analytical model for the thermionic-emission and tunneling currents, which is based on two-dimensional distributions of the electric potential, obtained from the Poisson equation, and the Wentzel-Kramers-Brillouin (WKB) approximation for the tunneling probability. The analysis shows that corresponding flat-band-voltage for fixed threshold voltage has a significant impact on the breakdown voltage. To investigate ambiguousness of number of dopants in channel, we compared breakdown voltages of high doping and undoped DGMOSFET and show undoped DGMOSFET is more realistic due to simple flat-band-voltage shift. Given that the flat-band-voltage is a process dependent parameter, the new model can be used to quantify the impact of process-parameter fluctuations on the breakdown voltage.

Dynamic modeling of the isoamyl acetate reactive distillation process

Abstract The cost-effectiveness of reactive distillation (RD) processes makes them highly attractive for industrial applications. However, their preliminary design and subsequent scale-up and operation are challenging. Specifically, the response of RD system during fluctuations in process parameters is of paramount importance to ensure the stability of the whole process. As a result of carrying out simulations using Aspen Plus, it is shown that the RD process for isoamyl acetate production was much more economical than conventional reactor distillation configuration under optimized process conditions due to lower utilities consumption, higher conversion and smaller sizes of condenser and reboiler. Rigorous dynamic modeling of RD system was performed to evaluate its sensitivity to disturbances in critical process parameters; the product flow was quite sensitive to disturbances. Even more sensitive was product composition when the disturbance in heat duties of condenser or reboiler led to a temperature decrease. However, positive disturbance in alcohol feed is of particular concern, which clearly made the system unstable.

Advanced Modeling of CSP Plants with Sensible Heat Storage: Instantaneous Effects of Solar Irradiance

A flexible model for concentrated solar thermal power plants with two tank active direct storage system is developed on gPROMS platform. Eutectic mixture of potassium and sodium nitrates salt melt is used as heat transfer fluid and storage medium. The solar radiation data obtained from a weather station, during 10 years in the city of Isfahan are used. It is showed that the Bird’s clear sky model well represents the normalized weather data. The storage system is designed to feed the boiler for 15 hours in the absence of sun. The model predicts the instantaneous effects of solar irradiance change and calculates variable with time parameters of the CSP plant; namely, the salt melt flow rates through receiver and boiler, and tank levels during 24 hr. The results indicate that a collector surface area of 35 m 2 and 250Kg mixture of nitrate salts, per 1 KW electricity generation, is necessary. With respect to the “variable with time” nature of solar irradiance, the role of storage tanks to damp fluctuations in process parameters of a CSP plant is further quantified. A 2500 Kg of heat storage material eliminates variable collector surface area need of 100 m 2 to 1500 m 2 or equivalently, eliminated increasing boiler temperature greater than 1000 o C.

РАСЧЕТНО-ЭКСПЕРИМЕНТАЛЬНЫЕ ИССЛЕДОВАНИЯ СОБСТВЕННЫХ ЧАСТОТ И ФОРМ КОЛЕБАНИЙ ЛОПАТКИ СПРЯМЛЯЮЩЕГО АППАРАТА ИЗ ПОЛИМЕРНЫХ КОМПОЗИЦИОННЫХ МАТЕРИАЛОВ

The object of this research is composite outlet guide vane (OGV) for advanced aircraft engine. The weight reduction due to using of polymer composite materials (PCM) instead of the metal in OGV can reach 40 %. The vane is exposed to intense aerodynamic loads during operation. The modal analysis is needed for the detuning a structure’s resonance frequency. The results of such analysis are presented in this work. Experimental technique of modal analysis for composite OGV in frequency range up to 6,4 kHz is described in paper. Experimental study was carried out for three full-scale OGV samples with the help of three-component scanning laser vibrometer using PSV-400-3D hardware. As the results the mean values and coefficients of variation of natural frequencies were obtained and the main natural modes were shown. The numerical simulation of this problem was carried out by finite element method (FEM) with ANSYS Workbench software using high-performance computing complex. The technological scheme of laying out of anisotropic plies was taken into account in the developed OGV model. The results of numerical simulations of natural frequencies and modes were compared with the test data. Good correlation was found. This fact confirmed that the stiffness of a full-scale OGV, manufactured with various possible fluctuations of process parameters and mechanical properties of materials, meets the required conditions. For further researches the developed numerical model allows to study the effect of reinforcing scheme and other design parameters changing on OGV frequency response. Laboratory modal analysis can be used to control the dimensional stability and material mechanical properties.

Variability-aware 7T SRAM circuit with low leakage high data stability SLEEP mode

The design of nanoscale static random access memory (SRAM) circuits becomes increasingly challenging due to the degraded data stability, weaker write ability, increased leakage power consumption, and exacerbated process parameter variations in each new CMOS technology generation. A new asymmetrically ground-gated seven-transistor (7T) SRAM circuit is proposed for providing a low leakage high data stability SLEEP mode in this paper. With the proposed asymmetrical 7T SRAM cell, the data stability is enhanced by up to 7.03x and 2.32x during read operations and idle status, respectively, as compared to the conventional six-transistor (6T) SRAM cells in a 65nm CMOS technology. A specialized write assist circuitry is proposed to facilitate the data transfer into the new 7T SRAM cells. The overall electrical quality of a 128-bit×64-bit memory array is enhanced by up to 74.44x and 13.72% with the proposed asymmetrical 7T SRAM cells as compared to conventional 6T and 8T SRAM cells, respectively. Furthermore, the new 7T SRAM cell displays higher data stability as compared to the conventional 6T SRAM cells and wider write voltage margin as compared to the conventional 8T SRAM cells under the influence of both die-to-die and within-die process parameter fluctuations.

Cost assessment of membrane processes: A practical example in the dairy wastewater reclamation by reverse osmosis

The profitability of a reverse osmosis application has been evaluated by using a well-known economic model based on experimental data, where additional operating costs were considered to represent a more realistic estimation. A calculation template was included to provide an easy-access to a preliminary economic evaluation of a specific membrane installation. The response of the model was checked with a real reverse osmosis application as this is the condensate recovery from a dairy ultra-high-temperature plant. A sensitivity analysis was performed to show the model behaviour when subjected to some process parameter fluctuations. The base case, which considers a design flow of 20m3/h, produced a payback period of 3.3 years, being a cost-effective facility for the amortization period studied. The recovery rate proved to be a relevant parameter of influence in the economic viability of the process, as an increase of 20% produced an improvement of 48.6% in the net present value estimated.

A Comprehensive Comparison of Data Stability Enhancement Techniques With Novel Nanoscale SRAM Cells Under Parameter Fluctuations

Conventional Static Random Access Memory (SRAM) cells suffer from an intrinsic data instability problem due to directly-accessed data storage nodes during a read operation. Noise margins of memory cells further shrink with increasing variability and decreasing power supply voltage in scaled CMOS technologies. A seven-transistor (7T), an eight-transistor (8T), a nine-transistor (9T), and 3 conventional six-transistor (6T) memory circuits are characterized for layout area, data stability, write voltage margin, data access speed, active power consumption, idle mode leakage currents, and minimum power supply voltage in this paper. A comprehensive electrical performance metric is evaluated to compare the memory cells considering process parameter and supply voltage fluctuations. The triple-threshold-voltage 8T and 9T SRAM cells provide up to 2.5× stronger data stability and 765.9× higher overall electrical quality as compared to the traditional 6T SRAM cells in a TSMC 65 nm CMOS technology.

Reactivation Noise Suppression With Sleep Signal Slew Rate Modulation in MTCMOS Circuits

Multi-threshold CMOS (MTCMOS) is commonly used for suppressing leakage currents in idle integrated circuits. Power and ground distribution network noise produced during SLEEP to ACTIVE mode transitions is an important reliability concern in MTCMOS circuits. Sleep signal slew rate modulation techniques for suppressing mode-transition noise are explored in this paper. A triple-phase sleep signal slew rate modulation (TPS) technique with a novel digital sleep signal generator is proposed. Reactivation time, mode-transition energy consumption, leakage power consumption, and layout area of different MTCMOS circuits are characterized under an equal-noise constraint. Influences of within-die and die-to-die parameter variations on the reactivation noise, time, and energy consumption of sleep signal slew rate modulated MTCMOS circuits are evaluated with a process imperfections aware robustness metric. The proposed triple-phase sleep signal slew rate modulation technique enhances the tolerance to process parameter fluctuations by up to <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$183.1\times$</tex></formula> as compared to various alternative MTCMOS noise suppression techniques in a UMC 80-nm CMOS technology.