Changes In Process Conditions Research Articles

Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm

ABSTRACT Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. The factors of spinning process condition changes are transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Based on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness, results show that for different usage periods of wire rings, the optimal breaking strength is 5.6, and the number of details is 33.3, 31.1, and 41.6 respectively. In some degree, the proposed algorithm can effectively adapt to the quality optimization problem of spinning process parameters under different spinning conditions, which could provide corresponding parameter optimization combinations for different spinning conditions.

Application of digital twin for industrial process control: A case study of gauge-looper-tension optimized control in strip hot rolling

Background During the hot rolling process, the performance of most control systems gradually degrades due to equipment aging and changing process conditions. However, existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies. Methods To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling, based on the digital twin method, this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling. First, a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform. Subsequently, relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process. Additionally, for controllers with poor Hurst index values, the crayfish optimization algorithm is employed for adjusting controller parameters to maximum the Hurst index. Results A real case of hot rolling steel production was used to validate the proposed data-driven optimized control method. Experimental results demonstrate that the proposed evaluation method can effectively recognize the state of gauge-looper-tension controller. Moreover, after optimizing the controller parameters through crayfish optimization algorithm (COA), the value of Hurst index showed significant improvement. Conclusions The proposed digital twin -based optimized control method effectively enhance the manufacturing capability and maintain strategy of hot rolling steel production. Through the proposed method, the Hurst index of gauge-looper-tension system increasing from 0.574 to 0.862.

Controlling Polarization‐Induced Polaron Accumulation in OLEDs via Device Design

AbstractSpontaneous alignment of molecular electric dipole moments, known as spontaneous orientation polarization (SOP), is present in many organic electronic materials. The SOP‐induced field changes the internal voltage distribution within the device, altering polaron injection and accumulation. The important role of SOP in organic light‐emitting devices (OLEDs) has recently been emphasized with clear links between dipole alignment and device efficiency and operational lifetime. Prior work has thus sought to engineer SOP via changes in thin film composition or processing conditions. These efforts have generally been directed at the OLED electron transport layer, which can show strong SOP. This work takes a different approach, focusing instead on how changes in device architecture can be applied to tune the magnitude and dynamics of polaron accumulation in response to SOP. In this work, it is demonstrated that the introduction of SOP into the device emissive layer offers control over the spatial location of accumulation. In parallel, insertion of a spacer layer between the hole transport and emissive layers can be used to tune the polaron accumulation rate and density during device operation. Both of these architectural changes permit an increase in OLED efficiency and a pathway around the deleterious impact of SOP‐induced polaron accumulation.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

Crack characteristics of pulsed laser brazed diamond grinding wheel

Experiments on pulsed laser brazing of diamond grinding wheels were carried out in this paper. The crack characteristics and residual stress of brazed layer were detected and evaluated. The influence laws of pulse width, frequency, diamond mixing ratio, number of stacked layers, powder thickness and preheating temperature on crack characteristics were investigated. The pulsed laser characteristic coefficients (peak pulse power density/pulse interval time) and the correlation law between thermal stresses and cracks are discussed. The results show that the pulse width and frequency of the pulsed laser affect the cracking rate by varying the energy input and the time between pulses. The existence of a suitable value for the pulse characteristic coefficient corresponding to pulse width and frequency corresponds to a lower cracking rate. Thermal stress variations due to changes in process conditions at different diamond percentages, number of stacked layers, and preheating temperatures are the main causes of crack rate variations. Cracking is not only affected by thermal stresses, but also by the quality of the formed surface when pulsed laser brazing diamond to nickel–chromium alloy brazing material. For example, the main reason for the variation in cracking rate is the change in the morphology of the molded surface due to the variation in the thickness of the powder spread at different variations in the thickness of the powder spread.

천연섬유 열가소성 복합재료 제조 및 특성 연구

In this study, a natural fiber thermoplastic composite material was prepared by applying the natural fibers jute and kenaf as reinforcement materials and polypropylene (PP) and high-density polyethylene (HDPE), which are thermoplastic resins, as matrix materials. The natural fibers were surface-treated to activate the interface between them and the thermoplastic resins. Differential scanning calorimetry analysis of the thermoplastic resins was performed to apply the hot-pressing process, and changes in hot-pressing process conditions were observed. The tensile and bending strengths as well as moisture absorption rate of the composite materials to which jute and kenaf were applied were compared and analyzed to confirm the possibility of replacing the glass fiber composite with the natural fiber composite for use as a small-ship material. In addition, for application to ship decks, a sandwich-structured composite material was manufactured and analyzed by applying polyvinyl chloride foam to the natural fiber composite material.

Operation and performance analysis of direct hollow fiber nanofiltration: A pilot study at IJsselmeer

This study investigated the performance of direct hollow fiber nanofiltration (dNF40) membranes from NX Filtration BV on a pilot scale for the treatment of pre-treated IJsselmeer water from Waterwinstation Prinses Juliana (WPJ) in Andijk, as well as the direct treatment of raw IJsselmeer water. The objective was to evaluate the long-term fouling potential and the retention of ions and natural organic matter (NOM) using both WPJ pre-treated IJsselmeer water and raw IJsselmeer water. Additionally, the rejection of organic micropollutants (OMPs) under artificially elevated conditions, referred to as ‘spiked solution’, using WPJ pre-treated IJsselmeer water was investigated.Limited to no fouling was observed on the dNF40 membrane during stable operation when treating both WPJ pre-treated IJsselmeer water and raw IJsselmeer water, even under changing process conditions. NOM removal consistently exceeded 90% regardless of process conditions or water type. The retention of per- and polyfluoroalkyl substances (PFAS) was above 80%, with even higher retention observed for higher molecular weight values. Low molecular weight pharmaceuticals, all below the molecular weight cut-off (MWCO) of the dNF40 membrane (400 Da), exhibited approximately 30% retention. The dNF40 membrane showed better retention of negatively charged pharmaceuticals in the spiked solution compared to positively charged and neutral pharmaceuticals.A total cost of ownership (TCO) analysis unveiled that operational expenditures (OPEX) were three times higher than capital expenditures (CAPEX) for a 5-stage full-scale dNF40 system. Among the components, membrane replacement costs constituted the majority of OPEX (68%), followed by energy costs (31%) and chemical costs (<1%). Overall, the study showcased the suitability of the dNF40 membranes for treating IJsselmeer water, achieving effective removal of NOM and PFAS.

Machine learning meets process control: Unveiling the potential of LSTMc

AbstractIn the past three decades, proportional‐integral/PI‐differential (PI/PID) controllers and model predictive controller (MPCs) have predominantly governed complex chemical process control. Despite their advancements, these approaches have limitations, with PI/PID controllers requiring scenario‐specific tuning and MPC being computationally demanding. To tackle these issues, we introduce the long‐short‐term‐memory (LSTM)‐controller (LSTMc), a model‐free, data‐driven framework leveraging LSTM networks' robust time‐series prediction capabilities. The LSTMc predicts subsequent manipulated inputs by evaluating state evolution and error dynamics from both the current and previous time‐steps, which proved effective in our dextrose batch crystallization case study. Remarkably, the achieves less than 2% set‐point deviation, three times better than MPCs, and retains robustness even with 10%–15% sensor noise. With these results, LSTMc emerges as a promising alternative for process control, adeptly adjusting to changing process conditions and set‐points, providing efficient computation for an optimal input profile, and effectively filtering out common industrial process noise.

Industrial process fault diagnosis based on domain adaptive broad echo network

BackgroundIn response to the challenge that traditional fault diagnosis models are difficult to maintain satisfactory accuracy when data distribution changes due to changes in process conditions, a fault diagnosis model of industrial process based on domain adaptive broad echo network (DABEN) is proposed. MethodsThe DABEN model first constructs feature nodes through random feature mapping to extract shallow features of process data, and then inputs feature nodes into cascade reservoirs to extract dynamic features of different levels. On this basis, the objective function of DABEN is constructed, which starts from the four goals of minimizing the prediction error, maximum mean discrepancy distribution alignment, manifold regularization and minimizing cross-domain error to ensure that the features are as similar as possible between the source domain and the target domain. Significant FindingsFinally, two simulation cases show that DABEN can achieve good transfer fault diagnosis performance under different data distributions

Robotic welding system for adaptive process control in gas metal arc welding

Changing process conditions such as distortion, varying seam preparation or gap width during welding is a major challenge in automated gas metal arc welding (GMAW). While human welders can adjust the process during welding (e.g. welding speed, torch orientation), an automated welding system needs sensors to detect and actuators to adjust the process. Adjusting the process in response to changing process conditions is usually referred to as adaptive welding. The aim of this work is to build a robotic welding system capable of automatically adapting the welding process using some of the approaches of a human welder. To enable adaptive process control, a robotic welding system is built. It consists of four main components: a six-axis industrial robot for mechanical guidance of the welding torch, a welding power source, a monochrome visual camera as an image sensor and a process controller that combines the three components. The camera captures images of the weld pool during welding and processes the images to provide geometrical information such as the width of the weld pool and the position of the weld pool front. Changes in the weld pool geometry are quantified, and an adjustment strategy is generated in the process control unit in real time. Process adjustments can be mechanical (e.g. welding speed, torch orientation) and electrical by adjusting synergic process settings (wire feed speed, arc length, process dynamics). Validation tests demonstrate the functionality of the welding system. Two use cases were investigated. Firstly, a deposited weld bead was examined, and variations in the width of the weld pool were induced by varying the welding speed. The second application was a seam tracking application. The path is pre-programmed, and the specimen is positioned with an offset to the path. Compensation for the offset is implemented.

Techno-economic and environmental analyses of the pyrolysis of food waste to produce bio-products

Food waste has become a source of concern as it is generated abundantly worldwide and needs to be valorised into new products. In this study, cucumber, tomato, and carrot wastes were investigated as pyrolysis feedstocks as a single component (cucumber), a binary component mixture (cucumber and tomato), and a ternary component blend (cucumber, tomato, and carrot). Fourteen scenarios were simulated and evaluated based on varying the feedstock blend (single, binary, and tertiary), temperature (300 and 500 °C), and feedstock moisture content (5, 20, and 40%). Using an established empirical model, the effect of these parameters on product yields, techno-economic implications, energy requirements, and life cycle analysis (LCA) outcomes were investigated. The best performers of each scenario were determined, and their strengths and weaknesses were identified and compared with other scenarios. In terms of product yields, all three systems (single, binary, and tertiary) followed a similar pattern: bio-oil yields increased as temperature and feedstock moisture content increased, while biochar yields decreased as temperature and feedstock moisture content increased. The production of syngas, on the other hand, was only observed at elevated temperatures. The total energy requirement exhibited an increase with increasing temperature and feedstock moisture content. The economic evaluation revealed that the return on investment (ROI) value for the single component at 5% moisture content at 300 °C is 29%, with a payback period (PB) of only 3.4 years, which is potentially very appealing. The water footprint increased with increasing pyrolysis temperature but decreased with increasing moisture content in all scenarios. The land footprint is observed to remain constant despite changes in process conditions. The study's findings contribute to the pyrolysis process's scalability, technological advancement, and commercialisation.

Evaluation of in-line monitoring of micro-forming tribo-systems by consecutive U-bending

Robustness in micro-forming, a critical part of which is system monitoring, is important due to increasing demand for micro-components. In this work, a simulation of a micro-deep drawing tribo-system is applied to investigate the influence of die radius and production speed on process stability and tool wear. Monitoring of force and thermoelectric current are used as tools for wear detection, showing that both signals respond well to wear. The thermoelectric current is more sensitive to slight changes in process conditions and can be recommended as a wear monitoring method in micro-forming over using the force signal. The results of signal monitoring are compared to the surface roughness of formed parts as function of number of strokes, showing a good correlation.

Cytokines as fast indicator of infectious virus titer during process development

Measuring infectious titer is the most time-consuming method during the production and process development of live viruses. Conventionally, it is done by measuring the tissue culture infectious dose (TCID50) or plaque forming units (pfu) in cell-based assays. Such assays require a time span of more than a week to the readout and significantly slow down process development. In this study, we utilized the pro-inflammatory cytokine response of a Vero production cell line to a recombinant measles vaccine virus (MVV) as model system for rapidly determining infectious virus titer within several hours after infection instead of one week. Cytokines are immunostimulatory proteins contributing to the first line of defence against virus infection. The probed cytokines in this study were MCP-1 and RANTES, which are secreted in a virus dose as well as time dependent manner and correlate to TCID50 over a concentration range of several logarithmic levels with R2 = 0.86 and R2 = 0.83, respectively. Furthermore, the pro-inflammatory cytokine response of the cells was specific for infectious virus particles and not evoked with filtered virus seed. We also discovered that individual cytokine candidates may be more suitable for off- or at-line analysis, depending on the secretion profile as well as their sensitivity towards changing process conditions. Furthermore, the method can be applied to follow a purification procedure and is therefore suited for process development and control.

Morphology of poly-3-hexyl-thiophene blends with styrene-isoprene-styrene block-copolymer elastomers from X-ray and neutron scattering.

The nano- and micron scale morphology of poly(3-hexylthiophene) (P3HT) and polystyrene-block-polyisoprene-block-polystyrene (PS-PI-PS) elastomeric blends is investigated through the use of ultra-small and small angle X-ray and neutron scattering (USAXS, SAXS, SANS). It is demonstrated that loading P3HT into elastomer matrices is possible with little distortion of the elastomeric structure up to a loading of ∼5 wt%. Increased loadings of conjugated polymer is found to significantly distort the matrix structure. Changes in processing conditions are also found to affect the blend morphology with especially strong dependence on processing temperature. Processing temperatures above the glass transition temperature (Tg) of polystyrene and the melting temperature (Tm) of the conjugated polymer additive (P3HT) creates significantly more organized mesophase domains. P3HT blends with PS-PI-PS can also be flow-aligned through processing, which results in an anisotropic structure that could be useful for the generation of anisotropic properties (e.g. conductivity). Moreover, the extent of flow alignment is significantly affected by the P3HT loading in the PS-PI-PS matrix. The work adds insight to the morphological understanding of a complex P3HT and PS-PI-PS polymer blend as conjugated polymer is added to the system. We also provide studies isolating the effect of processing changes aiding in the understanding of the structural changes in this elastomeric conjugated polymer blend.

Digital Twin of the laser-DED process based on a multiscale approach

The ramp-up of new geometries, process parameters, and materials can be enormously time and cost-intensive in Additive Manufacturing. Especially for Laser-Directed Energy Deposition (DED-L), the extreme physical environment at the melt pool results in the need for multiple trial-and-error tests to quantify the process behavior. These tests significantly raise manufacturing expenses. A Digital Twin (DT) of the DED-L process can therefore be of substantial value if the amount of experimental testing is hereby reduced. In the present study, a multiscale DT based on coupling a global and local model has been investigated. The global model simulates the heating of the entire part, whereas the local model represents only a specific region of this global geometry. Using a high-density mesh for the local model enables the simulation of the specific laser-powder interactions and fast-cooling rates typical in DED-L. The results of the global model are used to integrate context awareness about the changing process conditions during the print job into the local model. This process evolvement is impossible to obtain with models of smaller dimensions and is of elemental necessity for accurately simulating multi-clad depositions. The DT was validated on an industrial-grade DED-L machine with in-situ process monitoring capabilities. In all cases, the DT shows a high resemblance with the experimental data and metallographic inspections at a reasonable computational cost.

Evolution of the Deformation‐ and Flow‐Induced Crystallization and Characterization of the Microstructure of a Single Spherulite, Lamella, and Chain of Isotactic Polypropylene

AbstractIsotactic polypropylene (iPP) is a frequently used polymer in industrial processing and scientific research, used as a model material. This review highlights some current and forefront issues in polymer science and engineering. This review alludes to classical deformation schemes to interpret the complicated deformation in isotactic polypropylene as a semicrystalline polymer. Also, due to the complex hierarchical structures of crystallized polymers, the progress on the multiscale and multistage microstructural evolution covering each deformation stage from the initiation of plasticity until failure is mainly concerned, particularly in terms of crystalline morphology changes. Specifically, the characterization of the microstructure evolution of a single spherulite, lamella, and chain of isotactic polypropylene will be emphasized and mentioned. Here, the authors are most interested in the results obtained from stretching (strain‐induced) orientation and crystallization studies accompanied by the in situ scattering and spectroscopy techniques offering a detection window from nano to micrometer scale, which should be able to reflect the microstructural changes at different length scales of interest. During polymer processing, the melts are frequently subjected to shear or/and elongation flow fields, producing flow‐induced molecular chain orientation in the melt. The oriented molecular chains crystallize differently than those encountered under quiescent conditions. Thus, orientation‐induced crystallization has long been an object of intense interest. A vast body of research is reported about the effects of preorientation on the crystallization of various polymers. Among them, iPP is most frequently studied since its diversified structure and morphology are very sensitive to changes in processing conditions and molecular parameters.

Ground granulated iron silicate slag as supplementary cementitious material: Effect of prolonged grinding and granulation temperature

The metallurgical and cement industries contribute significantly to anthropogenic carbon dioxide emissions. Utilizing oxidic by-products from the metallurgical industry as supplementary cementitious materials (SCMs) can improve resource efficiency and reduce emissions from cement production. Iron silicate copper slags have been studied as SCMs, but mainly in systems where Portland cement is used as an activator. There is limited research on the inherent reactivity of the slag under changing processing conditions. The present study offers insight into the effect of granulation temperature and grinding on the inherent reactivity of an industrially produced iron silicate copper slag. The results showed that granulation temperature had an insignificant effect on reactivity, while grinding generated substantial improvements. The latter effect was concluded to stem from the increased specific surface area, increased number of sites for nucleation and growth of hydrates, and changes in the inherent reactivity owing to structural changes induced by the grinding.

Cloud–edge collaborative control for roaster furnace: A hybrid first-principles and data-driven method

Temperature stability control of the roaster furnace is an important yet challenging problem due to the fluctuation of feed compositions. To solve this problem, a first-principles and data-driven cloud–edge collaborative control (CECC) method is proposed. Specifically, through first-principles analysis, the influencing factors of temperature fluctuation are determined, and then the feed fluctuation is divided into two parts: the batch difference (BD) caused by changes in production objectives or process conditions, and the random fluctuation (RF) caused by uncertain factors of component content or ore source mixing process. For the problem of BD, a first-principles model of the roasting process is established by integrating multisource data in the cloud. Through timely response to the batch differences, the appropriate feed rate is sent to the edge controller to address the temperature unstable problem induced by the sudden component batch changes. For the problem of RF, a fuzzy controller based on trend analysis is adopted at the edge to fine-tune the feed rate to further reduce the negative impact of component fluctuation on the temperature. Accordingly, the proposed CECC method can realize stable control of the roaster furnace temperature synergistically. Both the numerical simulation experiment and hardware-in-the-loop platform experiment results verify the effectiveness of the proposed method.

Predictability and robustness of anode biofilm to changing potential in microbial electrolysis system

Microbial electrolysis systems (MES) facilitate the process of using waste for efficient production of hydrogen thus resulting in lower energy costs compared to conventional hydrogen production. However, the stability and robustness of anode-respiring biofilms often limit long-term MES application. In this study, a 10 L rotating disc bioelectrochemical reactor was used to analyze the anodic biofilm under rapidly changing processing conditions, including changes in anode potential and shear force. A low complexity biofilm formed by Shewanella oneidensis and Geobacter sulfurreducens was studied to determine the boundary conditions for achievable current density and species interaction in large-scale applications. Demonstrating its robustness to the applied changes, the biofilm produced a stable current density of 1.2 A m−2 over 1.5 months. Furthermore, a mathematical model was developed to predict the behavior of the system in terms of current output, which may allow automatic user-defined control of sub-processes in MES reactors in the future.

Factors Affecting Nitrous Oxide Emissions from Activated Sludge Wastewater Treatment Plants—A Review

Nitrous oxide (N2O) is a greenhouse gas contributing to ozone layer depletion and climate change. Wastewater treatment plants (WWTPs) contribute significantly to the global anthropogenic N2O emissions. The main factors affecting N2O emissions are the dissolved oxygen concentration (DO), the nitrite accumulation, the rapidly changing process conditions, the substrate composition and COD/N ratio, the pH, and the temperature. Low DO in the nitrification process results in higher N2O emissions, whereas high aeration rate in the nitration/anammox process results in higher N2O production. High DO in the denitrification inhibits the N2O reductase synthesis/activity, leading to N2O accumulation. High nitrite accumulation in both the nitrification and denitrification processes leads to high N2O emissions. Transient DO changes and rapid shifts in pH result in high N2O production. Ammonia shock loads leads to incomplete nitrification, resulting in NO2− accumulation and N2O formation. Limiting the biodegradable substrate hinders complete denitrification, leading to high N2O production. A COD/N ratio above 4 results in 20–30% of the nitrogen load being N2O emissions. Maximum N2O production at low pH (pH = 6) was observed during nitrification/denitrification and at high pH (pH = 8) during partial nitrification. High temperature enhances the denitrification kinetics but produces more Ν2O emissions.