Changes In Process Parameters Research Articles

Fractional-Order Control of Fluid Composition Conductivity

Dialysis refers to the procedure of removing waste products and excess fluids from the blood stream. This is the main form of treatment for both acute and chronic renal failure. The need for hemodialysis process optimization is increasing. More than 10% of adults are affected by chronic kidney disease, and it is the nineth leading cause of deaths worldwide. Critically ill patients are particularly at risk, and their mortality is significantly affected by the hemodialysis procedures. This is the reason why the design and control of the hemodialysis process is studied by many researchers. The present paper proposes a fractional-order control of the fluid composition conductivity in this process. Fractional-order PI and PID controllers are designed with different imposed performances in order to establish the best performing controller for this medical process. The proposed fractional-order controllers are compared to the classical controller’s results in different real-world scenarios, including process parameter changes, flow changes, and priming sequences. The results are compared with a classical PID controller used in current clinical practice. The simulation results show the robustness and advantages of the proposed fractional-order PID controller over other controllers. These results could improve the clinical use of the hemodialysis process.

Study on the application of a new surface burr treatment process

During the wood milling process, the milling cutter will jeopardize the continuity and integrity of wood fibres, resulting in the surface roughness not meeting the requirements. Surface roughness is an important indicator to assess the surface quality of wood products, which has a great impact on its subsequent sealing performance, painting and decorative quality. Therefore, the surface roughness requirements should be factored in when designing the machining process and determining the machining allowance. This paper uses a computer numerical control (CNC) woodworking milling machine to process camphor wood products. During this processing experiment, the paper analyses the impact of changes in major processing parameters on the surface roughness of wood products, such as spindle speed, feed rate, movement trajectory, milling depth, along and against the grain. When it comes to the state of the wood product surface not meeting the subsequent process requirements after milling, further measures are proposed for ablative finishing treatment. In order to find the best way to remove the surface burr without damaging the base surface, the surface burr of milling wood was studied and analyzed from the micro-angle. Using scanning electron microscopy along with microscopic imaging technology to observe and analyse, the paper establishes an ablation model of processing surface burrs and conducts simulation analysis of the influence of parameter changes, to provide some theoretical basis for practical production and related research.

On the Utility of the Thermal-Pseudo Mechanical Model’s Residual Stress Prediction Capability for the Development of Friction Stir Processing

This paper investigates the thermal-pseudo mechanical (TPM) model’s residual stress prediction capability for its utility in developing friction stir processing (FSP). Specifically, two FSP tests under different processing conditions were conducted, and the corresponding simulations were carried out to verify if the TPM model can predict residual stresses for various tool radii and workpiece materials. The model successfully predicted residual stresses with an error less than 4% for one of the tests but failed to work for the other test. Further simulations under different FSP conditions proved that the TPM model works for cast aluminum alloys and wrought aluminum alloys. In addition, the large FSP tool used was found to be the reason for the model’s failure on one of the tests. This indicates that there is a range of tool radii for which the TPM model is applicable. As a solution, this paper suggests modifications to the TPM model based on calibration to the FSP test temperatures. The resulting residual stress prediction is accurate and differs from the experimentally characterized stress values by only 6.5 MPa. The calibrated TPM model requires FSP to be carried out when using a tool with a different radius. Following that, the effect on residual stresses due to changes in the other process parameters, such as the tool traverse & rotation speeds and the clamping conditions, can be predicted.

A noise autocovariance model for SAR altimeter measurements with implications for optimal sampling

Earlier work has empirically demonstrated some advantages of an increased posting rate of Synthetic Aperture Radar (SAR) altimeters beyond the expected ground resolution of about 320 m in Delay-Doppler (unfocused SAR, UFSAR) processing, corresponding to ∼20 Hz sampling. Higher posting rates of 40–80 Hz were shown to prevent spectral aliasing of the signal, enable to measure swell wave related signal distortions and may lead to a reduced root mean square error of 1 Hz estimates of Sea Surface Heights (SSH), radar cross section (sigma0) and Significant Wave Heights (SWH) from current SAR altimeters. These improvements were explained by the narrow noise autocovariance function of the waveform signal’s power speckle noise in along-track direction on one hand, and frequency doubling by power detection (squaring of the signal) on the other. It has not been explained, however, why the power speckle noise decorrelates faster than anticipated by the predicted Doppler resolution, and whether this decorrelation depends on the altimeter and processing configuration. Also, it has not been shown explicitly that the estimates of SSH, SWH and sigma0 decorrelate in the same way. Describing the noise autocovariance function – or equivalently the noise power spectral density via the Wiener-Kintchin theorem – is necessary on two counts: Knowing the noise autocovariance allows to apply optimal filtering strategies that maximize precision on one hand, while the noise power spectral density predicts the frequencies contained in the noise (and signals), which in turn determines the required sampling frequency according to the Nyquist theorem. Using a newly derived analytic noise autocovariance model for UFSAR-processed altimeter data, we show that the swift signal decorrelation is mainly due to the observation geometry. Furthermore, our results demonstrate that the noise autocovariance functions of power speckle, SSH, SWH and sigma0 estimates in along-track direction are different and depend on the sea state. On top of that, the noise autocovariance functions are strongly dependent on the number of Doppler beams used for multilooking, the used retracker, and the processing choices such as antenna gain pattern compensation and windowing within the UFSAR processing (Level-1b). We validated our noise autocovariance model with segments of 42 Sentinel-3B overpasses. Our findings are in accordance to all earlier work, but indicate that the reported precision improvements with respect to 20 Hz may have been too optimistic and that the SSH, SWH and sigma0 generally decorrelate slower than the power speckle noise. We found that the required posting rate is always higher or equal to 40 Hz. Our results will potentially enable improved spectral analysis and optimal filtering of any UFSAR altimetry data. More importantly, our results can be used to trade off different aspects for determining an optimal posting rate in UFSAR altimeter processing in different sea states and with changing processing parameters, which is necessary in view of strict precision requirements of existing and future SAR altimetry missions.

Investigation of fused deposition modeling parameters on mechanical properties and characterization of ABS/carbon fiber composites

The effect of parameters of acrylonitrile butadiene styrene composites reinforced with carbon fabricated by fused deposition modeling was studied in this work. Parameters like infill density, raster angle, layer thickness, and printing parameters are varied and their influence on mechanical properties was analyzed experimentally. Experiments were planned by the design of experiments technique and mathematical equations were generated to analyze the impact of these parameters. ANOVA is involved to ensure the validity of the mathematical equations. The mechanism of the fractured samples was studied using scanning electron microscopy. The tensile strength of ABS/carbon fiber composites ranges from 21.74 to 28.09 N/mm2, while the flexural strength ranges from 2.62 to 3.14 N/mm2 and the impact strength ranges from 3.66 to 5.19 kJ/m2. The fracture of composites is characterized by cavities, infill gaps, bonding, dimples, tearing debonding of fiber, crack, delamination, shearing, crack, and fiber protrusion. The nature of failure varied from ductile to brittle depending upon the change in process parameters.

An Improved Charting Scheme to Monitor the Process Mean Using Two Supplementary Variables

A control chart is the most well-known statistical monitoring tecnique to address unfavourable process parameter (s) changes. Quality practitioners always desire a charting device that promptly identifies the undesired changes in the process. This study intends to design a sensitive homogeneously weighted moving average chart using two supplementary variables (hereafter, TAHWMA). The two supplementary variables are correlated with the study variable in the form of a regression estimator, which is an efficient and unbiased estimator for the process mean. The suggested TAHWMA charting structure is checked out and compared in terms of appearance and non-appearance of multicollinearity amidst the two additional variables. Average run length-related measures are taken as performance measures. It is observed that the proposed TAHWMA scheme performs effectively when the two supplementary variables have no collinearity. A comprehensive comparison between the proposed TAHWMA and existing charts is also carried out, showing the proposed’s supremacy over existing counterparts. For execution purposes, two illustrative examples, one belonging to carbon fibre manufacturing-related data and the other using a simulated dataset and where our simulated dataset belongs to symmetrical distribution, are also presented for the application of the recommended TAHWMA chart.

Spectroscopy-based smart optical monitoring system in the applications of laser additive manufacturing

The potential defects during the additive manufacturing (AM) process greatly deteriorate the mechanical properties of the fabricated structures and, as a result, increase the risks of part fatigue failure and even disasters. As laser additive manufacturing is such a complex process, many different physical phenomena such as electromagnetic radiation, optical and acoustic emission, and plasma generation will occur. Unlike vision and acoustic methods, the spectroscopy based smart optical monitoring system (SOMS) provides atomic level information revealing mechanical and chemical condition of the product. By monitoring plasma, multiple information such as line intensity, standard deviation, plasma temperature, or electron density, and by using different signal processing algorithms such as vector machine training or wavelet transforming, AM defects have been detected and classified. Utilizing two fiber optic components, a bifurcated fiber and a split fiber, the experimental results were performed to improve SOMS signal-to-noise ratio. Defects, including subsurface pores and sudden changes of process parameters including shielding gas shut-off and foreign substance, were identified by the spectroscopy based SOMS. For chemical composition characterization, a degree of dilution in terms of chemical element variation is identified by a spectral peak intensity ratio through the SOMS. It turned out that the information on the Cr/Fe ratio of deposit at a certain layer is vital to design the mechanical property in the IN625 deposition on the mild steel case. The SOMS has also demonstrated that the chemistry ratio can be determined from the calibration curve method based on the known alloy samples and that the ratio of the maximum intensities of multiple species provides more information about the quality of the alloy.

Hysteresis dynamic model of metal rubber based on higher-order nonlinear friction (HNF)

Metal rubber (MR) is a kind of porous and highly elastic pure metal damping material, but its strong nonlinear hysteresis characteristics are the main factors limiting its engineering application. In this paper, a high-order nonlinear friction (HNF) model was proposed for the first time according to the inclination angle and contact form of the spatial distribution of metal wires in MR. At the same time, combined with nonlinear elastic restoring force, nonlinear damping force, and coulomb hysteresis friction force, a general hysteresis dynamic model of MR was constructed. In order to verify the accuracy of the model, three kinds of MR with different densities were prepared and tested. The results show that the HNF hysteresis dynamic model has a very high prediction accuracy (R2= 0.9999) and a high signal-to-noise ratio (SNR > 700). The hysteresis curve of the HNF hysteresis dynamic model was highly consistent with the experimental hysteresis curve, while the accuracy and versatility of the traditional dynamic model decreased with the change of density. It is because the HNF term includes the influence factors that can characterize the contact form of the metal wire inside MR and the material properties. These influence factors will be adjusted in real-time to reduce the error of the model according to the changes in the preparation process parameters or material properties. Therefore, the HNF hysteresis dynamic model could effectively demonstrate the nonlinear hysteresis characteristics of MR and had the adaptive ability to change process parameters. This paper provided a new theoretical model for the dynamic system of MR materials with a complex network structure.

Characterization and modelling of Al and Cu busbar during charging and discharging of Li-ion battery for electric vehicles

• The simulated temperature of the Al-Cu busbar attached to the Li-ion pouch cell is validated with the experimental result. • The temperature generation in the Li-ion cell is influenced by the electrical contact resistance of the weld joint. • The joint resistance of a busbar is mainly dependent on intermetallic formation and grain size at the weld joint of a busbar. The electrical components such as bimetallic busbar joints of the lithium-ion (Li-ion) batteries should be able to withstand high voltages during charge and discharge processes. The busbar is an essential component that transmits high power to electrify the vehicle. The present study describes the sustainability of friction stir welded (FSW) busbar at different C-rates by simulating a Li-ion battery attached to a busbar, then correlating the heat generation of simulation results with an experimental result at 1, 1.5, and 2C-rates. The change in process parameters of FSW samples varies with electrical conductivity at the weld interface. The variation in electrical conductivity with different busbars is due to the formation of various intermetallic and changes in the grain size of the Al and Cu joints. However, the busbar with Cu-rich intermetallic exhibits smaller electrical resistivity. The specific electrical contact resistance of a busbar is obtained from simulation by validating the heat generated during constant time charge-discharge cycles. The temperature rises due to contact resistance in the Al-Cu busbar which can lead to thermal runaway and, eventually, short circuits in the Li-ion battery pack. Based on previous simulation parameters, the Li-ion cells are simulated at 5 and 10C-rates to understand thermal runaway behaviour.

Triple Homogeneously Weighted Moving Average Charts for Monitoring Process Dispersion

Homogeneously weighted moving average (HWMA) charts have recently achieved popularity for monitoring small changes in process parameters (location and/or dispersion). Furthermore, the DHWMA (double HWMA) and THWMA (triple HWMA) are the advanced versions of the HWMA charts. The HWMA chart for the process dispersion is designed to detect only the upward (one‐sided) shift (i.e., process deterioration). Employing a two‐sided chart for concurrently detecting both process improvement and process deterioration is an important aspect of statistical process monitoring. By taking this point as motivation, one and two‐sided THWMA charts (symbolized as the THWMAV) are proposed for monitoring the process dispersion. The Monte Carlo simulations are performed to investigate the performance behavior of the THWMAV charts in terms of certain performance indicators, including ARL, SDRL, EQL, RARL, and PCI. The comparison among the THWMAV versus existing charts (DHWMAV, HWMAV, TEWMAV, DEWMAV, and EWMAV) indicates that the THWMAV charts outperform the existing charts. Finally, a dataset is also analyzed to illustrate the implementation of the THWMAV charts.

Solubility Enhancement of a Model Drug Quercetin by Nanotechnology Based Formulation Approach

Approximately, 40% of drug candidates have poor water solubility and its oral delivery is frequently associated with implications of low bioavailability, hepatic first pass metabolism, enzymatic degradation, high intra- and inter subject variability, and lack of dose proportionality. Interests of solid lipid nanoparticles (SLN) to improve the oral bioavailability of such drugs (PWSD) are well known and documented in literature. Different batches of SLNs were prepared by emulsification-ultrasonication methods by changing process parameters, and finally on the basis of nanoparticle size, polydispersity index (PDI), zeta potential (ZP), encapsulation efficiency (EE), an optimum system was designed. The optimized formulation demonstrated particle size, polydispersity index, zeta potential and encapsulation efficiency; 327 nm, 0.118, 32.3 mV, and 57.9%, respectively. The optimized formulation has shown the highest zeta potential (-32.3. mV) confers its stability. In-vitro release of drug showed significant improvement in the release of quercetin from SLN formulation as compared to plain drug. Furthermore, quercetin-loaded SLN were found to be stable at 4 °C for 1 months of study period. The formulation was successfully prepared by proposed method that can be used as a potential carrier for successful delivery of poorly water-soluble drugs associated with poor oral bioavailability.

When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Acoustic Emission (AE) is an effective method to monitor and control the quality in different technical processes and phenomena, including tribology and fracture mechanics. However, in highly dynamic processes such as Laser Additive Manufacturing (LAM) of metal, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome the complexity of the large data processing with a reasonable computational time. In this contribution, we have summarized our prior works. To start with, we demonstrated that the combination of AE with state-of-the-art signal processing including AI makes it possible to differentiate several process conditions, workpiece quality and materials. Then, we present some limitations when changing process parameters and materials. Finally, we also introduce alternative AI methods to reduce the amount of data needed to train the AI algorithms as well as transfer the knowledge from one material to another one. This will give the reader an overview of the advances of monitoring the LAM process combining AE with AI techniques to make a significant step forward in in situ and real-time process monitoring and quality control. In particular,

Characterization of porosity in lack of fusion pores in selective laser melting using the wavefunction

Selective laser melting (SLM) is used extensively in the manufacture of components for both production and domestic applications. However, the lack of fusion pores is one of the most common defects in the SLM process, affecting the performance and life of the part and hindering the development of the SLM process. Meanwhile, the defects are particularly sensitive to changes in SLM process parameters. The micro-selective laser melting (μ SLM) model was established by molecular dynamics simulation, and the lack of fusion pores in the melt pool was analyzed by a multifunctional wavefunction analyzer to understand the difference of the porosities under different processes. The results show that both laser power and scanning speed can prolong the existence time of the melt pool by changing the input energy density. The melted powder has more time to fill the lack of fusion pores, thus reducing the porosity. The larger scanning spacing hinders the combination of adjacent melt pools, leading to an increase in porosity. Reducing scanning spacing will lead to sintering or remelting, thus improving the bonding quality of adjacent melt pools and effectively reducing porosity.

Towards a Deep Learning-based Online Quality Prediction System for Welding Processes

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding (GMAW). The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in GMAW. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.

Hybrid modeling supported development of an industrial small-molecule flow chemistry process

Continuous manufacturing of small molecules finds increasing application in developing novel processes due to its significant advantages. The development of these processes is increasingly being supported by models, in a quest to reduce the number of experiments and increase insight. With the Quality by Design paradigm in mind, answers to questions anent the impact of changes in process parameters and material attributes on the process performance and product quality are sought to be answered.In this contribution, a hybrid model is used to describe the impact of raw material variations and process parameter changes on the product quantity and quality of flow chemistry process. The process model is then used for process optimization aiming at maximizing the product concentration. During the optimization, a constraint that accounts for the risk of model misprediction is considered to explore only process conditions for which the model can describe the process behavior. The obtained optimal conditions are in agreement with expectations and underline the potential for hybrid model-based process development.

A Study on the Gap-Fill Process Deposited by the Deposition/Etch/Deposition Method in the Space-Divided PE-ALD System

This study concerns the development of a gap-fill process technology for isolating trench patterns. There are various gap-filling techniques in the case of trench patterns; nevertheless, a processing technology adopting the DED (deposition/etch/deposition) method was developed in this study. After the etch step, an Ar/O2 (1:2) plasma treatment technology reduced the residual amount of F in the films to 0.05%. By improving the etch uniformity, the deposition uniformity after the DED process on a 12-inch flat wafer was secured within <1%, and a high-quality SiO2 thin film with a dielectric constant of 3.97 and a breakdown field of 11.41 MV/cm was fabricated. The DED method can be used for gap-filling even in patterns with a high aspect ratio by changing process parameters, such as RF power and division of etch steps, according to the shape, depth, and CD size of the pattern. This study confirmed that a void-free gap-fill process can be developed in a trench pattern with a maximum aspect ratio of 40:1.

Advantages and Limitations of Anaerobic Wastewater Treatment—Technological Basics, Development Directions, and Technological Innovations

Anaerobic wastewater treatment is still a dynamically developing technology ensuring the effective degradation of organic compounds and biogas production. As evidenced in the large scale-up, this technological solution surpasses aerobic methods in many aspects. Its advantages stem from the feasibility of operation at a high organic load rate, the smaller production of difficult-to-manage sewage sludge, the smaller space and cubature required, and the high-methane biogas ultimately produced. The exploitation of anaerobic reactors is in line with the assumption of a circular economy, material recycling by reduced CO2 emissions and energy consumption, and the production of renewable energy. Despite their unquestionable advantages, there is still a need to seek novel approaches and improve the currently exploited installations. The key avenues of research entail improvements in the stability of bioreactor operations and the enhancement of bioreactor adaptability to changing and unfavorable process parameters. The versatility of such systems would also be greatly improved by increasing nitrogen and phosphorus removal rates. Attempts have been made to achieve these goals by setting up separate zones within bioreactors for the individual steps of methane fermentation, incorporating active fillings to promote nutrient removal, and introducing chemical and physical treatments. An interesting solution is also the use of microwave radiation to stimulate temperature conditions and induce non-thermal phenomena, such as enhancing the enzymatic activity of methanogenic microflora. Another prospective approach is to integrate digesters into microalgal biomass production systems. The aim of this review paper is to present the thus-far technological knowledge about anaerobic wastewater treatment, including standard solutions and innovative ones, the effectiveness of which has been corroborated in pilot-scale installations.

Esterification of stearic acid using novel protonated and crosslinked amidoximated polyacrylonitrile ion exchange fibres

This study will demonstrate the conversion of stearic acid to methyl stearate (biodiesel) using for the first time crosslinked amidoximated polyacrylonitrile ion-exchanged mesh protonated by sulphuric acid. Quantitative analysis of conversion was by GC-FID, 1H NMR and ATR-FT-IR with GC-FID the most reliable. A molar ratio of methanol to stearic acid of 35.5: 1 gave 94 % conversion to the ester at 90 °C. At 65 °C and a greater ratio of 87.1:1 conversion was 94.1 % comparable to the 98 % yield obtained with 1wt% H2SO4. Re-use at 65 °C gently dropped to 57 % on the 13th cycle. Regeneration by washing with dichloromethane and re-acidification achieved 84 % conversion but quickly deactivated due to blocking of sites by methyl stearate as shown by ATR-FTIR. This protonated amidoxime PAN catalyst (distinct from sulfonated fibres) exhibited higher stability than other heterogeneous protonated catalysts (ion-exchange resins, zeolites, clays) used lower temperatures and shorter reaction times. Advantages of using a mesh in place of powders are its ease of removal/replacement for continuous flow reactors promoting quick changes in process parameters. This study is promising as providing a sustainable protonated catalyst for use in converting Fats, Oils and Greases (FOG’s) in waste oils and wastewater to biodiesel.

Data Driven Modelling and Control Strategies to Improve Biogas Quality and Production from High Solids Anaerobic Digestion: A Mini Review

Anaerobic Digestion (AD) is one of the oldest processes for producing biofuels from organic waste. Approximately 180 years have passed since the construction of the first modern plant, however, large prospects for improvement are still feasible, especially in regards to the quality and uniformity of the biogas produced. This work focalizes on the main quality issues and the available post-production treatment processes for biogas; subsequently, a mini-review on data-driven models and control strategies for biogas and bio-methane production plants is presented. Attention is focused on High Solids Anaerobic Digesters (HSADs), since these reactors present many interesting advantages, including a high number of operating variables which enable process optimization, high methane concentration in exit, reduced reactor volume and low water requirements. HSADs are the reactors with which Europe is aiming to rapidly increase the production of biogas and bio-methane, in order to carry out de-carbonization and reduce dependence on external methane imports. Crucial points for achieving these objectives include qualitative leaps in process operation and management, which, contrary to current practice in existing plants, require a significant increase in process automation, with control of product quality and reduction of stops due to death of bacteria at changing process parameters (such as temperature and pH). The most significant papers related to biogas quality, data-driven models and control strategies are briefly analyzed.

Heat Integration for Phenols and Ammonia Recovery Process of Coal Gasification Wastewater Considering Optimization of Process Parameters

A heat integration optimization method that considers the changes in process parameters is proposed to find the global optimal process scheme for a coal chemical company’s phenols and ammonia recovery process. The phenols and ammonia recovery process is simulated by Aspen Plus, and a programming method for heat exchanger networks synthesis that can simultaneously optimize process parameters and heat integration is constructed by Matlab. Taking the total annual cost as the objective function, the following process parameters are optimized: the hot feed temperature and cold/hot feed ratio of sour water stripper, the temperature of three-step partial condensation system, the feed temperature and column pressure of both solvent distillation column and solvent stripper. The result shows that, compared with the heat integration process under original process parameters, the new heat integration process saves 14.3% energy consumption and reduces the total annual cost by about 15.1%. The new heat integration process provides guidance for the optimization of the phenols and ammonia recovery process. The proposed heat integration optimization method based on changing process parameters is an effective and practical tool that offers good application prospects.