Changes In Process Conditions Research Articles

Online tuning of predictor weights for relevant data selection in just-in-time-learning

Just-in-Time-Learning (JITL) is one of the most frequently used adaptive methods in data-based soft sensor design for chemical processes. While JITL is an effective method to combat concept drifts, samples selected via similarity metric in a Euclidean space consisting of a large number of equally-weighted predictors may diminish the performance of the JITL, due to curse of dimensionality and the varying degrees of nonlinearity between the predictor and response variables. Algorithms involving offline tuning of predictor weights were developed to tackle this issue, but changes in process conditions may depreciate a currently used set of weights for measuring similarity. In the current study, an adaptive method is developed for adjusting predictor weights in the Euclidean space used in measuring similarity between samples, named JITL via Online Weighted Euclidean Distance (JITL-OWED). JITL-OWED mainly consists of four steps: i) Relevant data is selected from an online weighted Euclidean space, and multiple models using different weights in their similarity measures are simultaneously constructed. ii) Control charts are used to detect changes in the prediction accuracy of the multiple models constructed online, hence triggering new subset searches. iii) A small number of search steps is used in subset selection, akin to early stopping in gradient search, with the “modest” aim of moving towards a local minimum for feasible online implementation. Exponentially Weighted Moving Average (EWMA) filtering is employed to stabilize the results from feature searches. iv) Final predictions are obtained via stacking multiple models. Employing JITL-OWED on one synthetic and four publicly available real datasets shows that online predictor weighting may indeed improve the prediction accuracy of the traditional JITL up to 80%, and JITL-OWED is both easy to implement and tune.

A New Monte Carlo Simulation of Low-Density-Polyethylene Polymerization for Prediction of its Microstructure as a Result of Various Process Conditions

In the petrochemical industries, a variety of low density polyethylene (LDPE) grades are produced from a single reactor line with changes in process conditions and/or materials. Herein the microstructure parameters, for example, monomer conversion, average molecular weight (), long chain branches (LCB) and short chain branches (SCB), are calculated for various process conditions in the polymerization process such as chain transfer agent (CTA) type and concentration, CTA feed position, initiator feed position, temperature profile, and residence time in the reactor, all based on industry data. Our results showed the CTA with a high rate constant makes three times more LCB but decreases the SCB by 13% relative to the CTA with a low rate constant. Also, for the same CTA type, decreased if the CTA concentration was increased. Injection of initiators at the beginning of the process had no significant effect on the SCB but increased the LCB by 54%. The new equations presented here to predict the microstructure of LDPE produced in tubular reactors resulted in good agreement with reported experimental values. The equations advantage is that the industrial data of the LDPE polymerization can be analyzed with high accuracy and speed to obtain the LDPE microstructure.

The Influence of Anodic Alumina Coating Nanostructure Produced on EN AW-5251 Alloy on Type of Tribological Wear Process

The article presents the influence of the anodic alumina coating nanostructure produced on aluminum alloy EN AW-5251 on the type of tribological wear process of the coating. Oxide coatings were produced electrochemically in a ternary electrolyte by the DC method. Analysis of the nanostructure of the coating was performed using ImageJ 1.50i software on micrographs taken with a scanning electron microscope (SEM). Scratch tests of the coatings were carried out using a Micron-Gamma microhardness tester. The scratch marks were subjected to surface geometric structure studies with a Form TalySurf 2 50i contact profiler. Based on the studies, it was found that changes in the manufacturing process conditions (current density, electrolyte temperature) affect changes in the coating thickness and changes in the anodic alumina coating nanostructure (quantity and diameter of nanofibers), which in turn has a significant impact on the type of tribological wear. An increase in the density of the anodizing current from 1 to 4 A/dm2 causes an increase in the diameter of the nanofibers from 75.99 ± 7.7 to 124.59 ± 6.53 nm while reducing amount of fibers from 6.6 ± 0.61 to 3.8 ± 0.48 on length 1 × 103 nm. This affects on a change in the type of tribological wear from grooving to micro-cutting.

The next generation of risk-based inspection for coal seam gas wells

The coal seam gas (CSG) industry presents some unique mechanical integrity and assurance challenges, in particular: (1) the large well count (thousands), many of which are not identical (in design, process, equipment and layout), and hence have varying inspection requirements; (2) the geographic spread of wells within a field; (3) relying heavily on inspection to confirm equipment condition; (4) significant water and sand production; and (5) rapidly changing process conditions over the life of the well. One particular challenge for many operators is the potential for loss of containment in well-site pressure equipment from very high erosion rates. This, combined with the large asset count, makes physical inspection of each component to ensure equipment condition a challenging and costly task. So, what can a business do to develop an adaptive maintenance strategy to address changing process conditions, allowing them to intervene proactively before a failure occurs and maximising the opportunity to lengthen inspection intervals and reducing inspection costs on low-risk assets? One CSG operator, in conjunction with a leading integrity management service provider, has developed and implemented a dynamic digital twin of the physical condition of their assets by combining live production, monitoring and inspection data, allowing the current and future condition of equipment to be modelled and a risk-based inspection (RBI) strategy that is evergreen and reacts to the changing process conditions. The system built on the NEXUS Integrity Centre platform uses several different predictive models to prioritise assets and components for inspection using live process data and a consistent RBI approach across all static pressure equipment, essentially providing a live digital representation of the current asset condition, the essence of a digital twin. The system provides a prioritised list of assets requiring inspection before the planned maintenance strategy while also highlighting opportunities to extend inspection intervals and thus take advantage of the potential reduction in on-going maintenance costs.

Nonconductive Noncharging Composites: Tunable and Stretchable Materials for Adaptive Prevention of Charging by Contact Electrification.

Static charge generated by contact electrification can cause a wide range of undesirable consequences in our lives and in industry (e.g., adhesion of particles on surfaces, damage to electronics, and explosions). It has, however, been challenging to develop methods to prevent charging due to the vast types of materials that charge easily by contact electrification and the frequent changes in process and environmental conditions. The most common method is to use conductive materials for dissipating charge away; however, it is ineffective for many circumstances. Here, we propose a general and effective materials framework that involves a two-level consideration for preparing noncharging materials: (1) the variation of the proportion of a two-material composite and (2) the extent of stretching the composite material. This materials strategy is achieved by infusing particles within a stretchable bulk material. Importantly, the preparation of the noncharging surface for (1) is based on a novel fundamental mechanism that involves combining an appropriate amount of a material (e.g., the particles) that tends to charge positively with another material (e.g., the bulk material) that tends to charge negatively. This mechanism does not rely on conductivity; both the contacting materials naturally prevent the generation of static charge even when only nonconductive materials are involved. When the composite material is stretchable, the change in proportion of the surface coverage of the particles allows the charging response to be changed. Therefore, the variation in composition and stretching provide a wide two-dimensional parameter space for achieving noncharging response for the vast range of contacting materials that are used in industry and our lives. In addition, stretchability allows the composite material to flexibly adapt to changes in process and environmental conditions. This stretchable composite material was also demonstrated to be capable of preventing the adhesion of particles and separating particles of different materials.

Development of an Electric Arc Furnace Simulator Based on a Comprehensive Dynamic Process Model

A simulator and an algorithm for the automatic creation of operation charts based on process conditions were developed on the basis of an existing comprehensive electric arc furnace process model. The simulator allows direct user input and real-time display of results during the simulation, making it usable for training and teaching of electric arc furnace operators. The automatic control feature offers a quick and automated evaluation of a large number of scenarios or changes in process conditions, raw materials, or equipment used. The operation chart is adjusted automatically to give comparable conditions at tapping and allows the assessment of the necessary changes in the operating strategy as well as their effect on productivity, energy, and resource consumption, along with process emissions.

Development of a Machine Learning Based Fast Running Model to Determine Rapidly the Process Conditions in Drawing Process

This study proposes a fast running model that interconnects input and output data for a single-pass cold bar drawing process through the use of Artificial Neural Network (ANN) and automatically generated a large volume of elastic-plastic finite element (FE) analysis results. The prediction accuracy of the FE analysis was verified by comparing the FE analysis with measurements from a drawing experiment. A Python-based script that automatically controls ABAQUS was coded to sequentially produce output data that varies according to the input data, which is a combination of 18 grades of steel and 1,000 process conditions. The ANN was trained using input and output data, and then a nine-dimensional fast running model was developed. The fast running model predicted the values of output variables (drawing force, strain at the center, strain on the surface, accumulated damage at the center, contact pressure, and the fracture (or non-fracture) of the material) in 0.1 second no matter how the mechanical properties of the steels and process conditions change. With this fast running model, engineers in the drawing industry can easily determine or modify the process conditions to improve productivity and product quality even when a grade of steel that has never been employed before is drawn.

Synthesis, characterization and investigation of catalytic properties of metal-substituted (M = Mg2+, Zn2+ and Na+) calcium hydroxyapatite

Calcium hydroxyapatite (Ca10(PO4)6(OH)2,CHAp) has unique characteristics and can be used as artificial material for the recovering of bones and teeth, for drug delivery systems, as adsorbent and catalyst. The specific chemical, structural and morphological properties of calcium hydroxyapatite are highly sensitive to the changes in chemical composition and processing conditions. Various kinds of transition metal and other cations can be readily accommodated into the apatite framework based on their large cation exchangeability. The traces of metal ions introduced in apatite structure can affect the lattice parameters, crystallinity, dissolution kinetics and other physical properties of apatite. The aim of this study was to find more economically accessible metal-substituted calcium hydroxyapatite–based catalyst. In this paper, the sol-gel and co-precipitation synthesis methods were used for the preparation of metal-substituted (M = Mg2+, Zn2+ and Na+) calcium hydroxyapatite (Ca10(PO4)6(OH)2, CHAp) samples. The synthesized powders were characterized by powder X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and gas chromatography-mass spectrometry (GC-MS). The CHAp:M samples prepared by co-precipitation method were almost single-phase at different concentrations of substituents, while the sol-gel-derived CHAp:M specimens contained additional impurity phases. Metal-containing CHAp samples were tested as catalysts in a solvent-free synthesis of 2-adamantylidene(phenyl)amine. It was demonstrated that the zinc-containing CHAp improved the yield of Schiff base.

Machine Learning Based Predictive Model for AFP-Based Unidirectional Composite Laminates

Manufacturing of composites using automated fiber placement (AFP) is a complex process that involves large number of processing conditions and variables. Improper selection of these parameters adversely affects the quality and integrity of the manufactured laminates. Thus, it is important to develop a predictive model that can assess how changes in critical process conditions alter the outputs of the manufacturing process. The goal of this investigation is to learn the complex behavior of composites by developing an intelligent model, which can subsequently be used for the prediction of various characteristics of the composites. However, manufacturing of AFP composites is both expensive and time-consuming and therefore the available data samples are less, from the prospective of machine learning, which leads to the small data learning problem. This article first solves this problem through virtual sample generation, and then a neural network based predictive model is developed to accurately learn the complex relationships between various processing parameters in AFP.

Dynamics of Coke-Oven Gas Formation from Brown Coal under Changes in Process Conditions

The process of thermolysis of brown coal from the Shurab deposit was analyzed by comparing the reaction kinetics and the yield of coke-oven gas under changes in process conditions over a range of 300–1000°C with a temperature step of 100°C. Among the eight coke-oven gas fractions obtained, the highest yield corresponded to a temperature regime of 700°C.

The sweet smell of sweat and cabbage

The aroma profile of dark chocolate has been elucidated and could be used as a benchmark to study the effect of changes in recipe, processing and storage conditions on its flavour.

Effect of Temperature on Production Intracellular and Extracellular Invertase by Potential Indigenous Strain Kluyveromyces Marxianus Using Sugarcane Molasses

This study is focused on enzymolgy regarding enzyme purification technique and application of thermotolerant specie Kluyveromyces marxianus yeast in bio reactions for the research and optimization of fermentation temperature for intracellular and extracellular enzyme production. Fermentation studies were carried out in shake flask level to optimize intracellular and extracellular enzyme production by changing process conditions like temperature range from (30 to 55 0 C), the pH (5.5), speed (350). The substrate type sugar cane molasses 15% added as a carbon and ammonium sulphate (0.75%) as nitrogen source. The optimized fermentation temperature was found 45 o C, at pH 5.5, and rpm was 350. The production of intracellular invertase was (890µmoles/min/g) while the extracellular (120µmoles/min/g) Kluyveromyces marxianus was greater efficiency as compared to other specie because of its metabolic activity, which express more heat stability and Invertase activty upto 65 0 C. Keywords: Yeast, Fermentation, K.marxianus DOI : 10.7176/JNSR/9-9-02 Publication date :May 31 st 2019

Online non-contact quality control of noodle dough using ultrasound

The development of a non-contract ultrasonic technique is reported for online quality control of noodle dough without compromising food safety during production. The technique uses airborne ultrasound to measure mechanical properties of the dough without touching or deforming it; thus, food safety cannot be endangered during processing by contamination arising from direct contact with monitoring equipment. Measured mechanical properties are related to the texture of the final product, thereby providing information during processing that is relevant to product quality. The technique was first assessed in laboratory experiments and then tested online during production runs on a pilot plant noodle sheeting line. It was found that differences in mechanical properties of noodle doughs resulting from changes in formulation or processing conditions can be distinguished, and that flaws arising from thickness fluctuations in noodle sheets can be detected. These offline and online experiments show the feasibility of using non-contact ultrasound for real-time hygienic quality monitoring of noodles at critical control points in the sheeting process, thereby providing rapid feedback for improvements in process design and control.

Innovative electrotechnological systems to provide the temperature modes of technological pipelines

Introduction: the systems of thermal effects on thermo-dependent, viscous and highly viscous liquids under conditions of the Arctic and the Extreme North are considered. Low efficiency and danger of heating systems based on burned hydrocarbons, heated liquids and steam are shown. Electrothermal heating systems used to maintain thermo-dependent fluids in a fluid state are considered. The evaluation of the effectiveness of the application of the most common electrothermal system — heating cables (tapes). The most effective electrothermal system based on induction technologies has been determined. Materials and methods: considered methods of thermal exposure to maintain the fluid properties of thermo-dependent fluids at low extreme temperatures. Results: presents an induction heating system and options for its implementation in the Extreme North and the Arctic. Conclusions: induction heating system to minimize loss of product quality, improve the system performance under changing process conditions, eliminate fire product, to reduce the influence of the human factor.

Effects of Shear Rate during Energetic Material Processing on Reactivity

Energetic materials are often processed at high rates of deformation as colloidal slurries and then cured. The slurries are non‐Newtonian colloidal solutions that exhibit changes in microstructure with variations in applied flow. This study shows that changes to microstructure due to applied flow affect the reactivity of energetic thin films. Energetic thin films of identical composition and geometry are prepared with different applied shear rates, which produce variations in the film microstructure by segregating smaller particles toward surfaces. Results show that films exhibit significant gains in flame speed with increasing shear rate. The differences in flame speed are linked to variations in microstructure. Specifically, densification of smaller particles near a boundary promote increased flame speeds. However, when particles become segregated, larger particles tend to contribute less to the overall reaction because they burn slowly compared to the smaller particles. When segregated, the larger particles may not be adding chemical energy to the reaction front because propagation is dominated by the more ignition sensitive smaller particles. This study demonstrates explicit changes in reactivity arising from changes in processing conditions that affect microstructure. Controlling applied shear rate introduces a new approach to regulating energetic material reactivity when processed using extrusion‐based advanced manufacturing techniques.

Combined Spatially Resolved Optical Emission Imaging and Modeling Studies of Microwave-Activated H2/Ar and H2/Kr Plasmas Operating at Powers and Pressures Relevant for Diamond Chemical Vapor Deposition.

Microwave (MW) activated H2/Ar (and H2/Kr) plasmas operating under powers and pressures relevant to diamond chemical vapor deposition have been investigated experimentally and by 2-D modeling. The experiments return spatially and wavelength resolved optical emission spectra of electronically excited H2 molecules and H and Ar(/Kr) atoms for a range of H2/noble gas mixing ratios. The self-consistent 2-D( r, z) modeling of different H2/Ar gas mixtures includes calculations of the MW electromagnetic fields, the plasma chemistry and electron kinetics, heat and species transfer and gas-surface interactions. Comparison with the trends revealed by the spatially resolved optical emission measurements and their variations with changes in process conditions help guide identification and refinement of the dominant plasma (and plasma emission) generation mechanisms and the more important Ar-H, Ar-H2, and H-H2 coupling reactions. Noble gas addition is shown to encourage radial expansion of the plasma, and thus to improve the uniformity of the H atom concentration and the gas temperature just above the substrate. Noble gas addition in the current experiments is also found to enhance (unwanted) sputtering of the copper base plate of the reactor; the experimentally observed increase in gas phase Cu* emission is shown to correlate with the near substrate ArH+ (and KrH+) ion concentrations returned by the modeling, rather than with the relatively more abundant H3+ (and H3O+) ions.

Synthesis, structure and properties of N-R-2-(5-(5-methyl-1H-pyrazole-3-yl)-4-phenyl-4H-1,2,4-triazole-3-ylthio)acetamides

Heterocyclic compounds remain the most promising group of compounds, through which the new drugs with characteristic list of properties are successfully created. Examples of this systems are 1,2,4-triazole and pyrazole. The presence in one molecule structure of fragments of two different azaheterocycles is synthetically interesting and allows increasing the probability to obtain biologically active substance with a wide spectrum of action. The aim of the work was to optimize the synthesis conditions and investigate the properties of N-R-2-(5-(5-methyl-1 H -pyrazole-3-yl)-4-phenyl-4 H -1,2,4-triazole-3-ylthio)acetamides in case of change of chemical process conditions. Methods and results. Ethyl 5-methyl-1 H -pyrazole-3-carboxylate, which was obtained by known techniques using acetone, diethyl oxalate, sodium methoxide, followed by hydrazine hydrate in equivalent amount, was used as the key starting reagent. The resulting ethyl 5-methyl-1 H -pyrazole-3-carboxylate was used to carry out hydrazinolysis reactions and nucleophilic addition of phenyl isothiocyanate with subsequent alkaline heterocyclicization. The synthesized 5-(5-methyl-1 H -pyrazol-3-yl)-4-phenyl-4 H -1,2,4-triazole-3-thiole was used in alkylation reactions with promising reagents for the design of pharmacophoric fragments. The products of such reaction are N -R-2-(5-(5-methyl-1 H -pyrazole-3-yl)-4-phenyl-4 H -1,2,4-triazole-3-ylthio)acetamides. The structure of the resulting compounds was confirmed by elemental analysis, 1 H NMR spectroscopy, IR spectrophotometry. The individuality of the substances was determined by thin-layer chromatography and chromatographic mass spectrometry. For synthesized compounds, preliminary screening was performed using the PASS On-line ® software and molecular docking. Conclusions. N -R-2-5-(5-methyl-1 H -pyrazole-3-yl)-4-phenyl-4 H -1,2,4-triazole-3-ylthio)acetamides are obtained with high yields and purity, their properties are investigated.

Accelerated testing method to estimate the long‐term hydrostatic strength of semi‐crystalline plastic pipes

The ability to quickly develop predictions of the service lifetime of plastic pipes at different load levels allows designers to choose the best plastic material and design pipe for a specific application. Additionally, it helps material producers to rapidly design, manufacture, test, screen, and modify the base polymeric material. The aim of this study is to introduce a combined experimental and analytical framework to develop accelerated lifetime estimates for semi‐crystalline plastic pipes which is sensitive to the structure, orientation, and morphology changes introduced by changing processing conditions. To accomplish this task, high density polyethylene (HDPE) is chosen as the exemplary base material and custom fixtures are developed to admit tensile and hoop burst tests on the as‐manufactured HDPE pipes. A pressure‐modified Eyring flow equation is employed to predict the rupture lifetime of HDPE pipes using the measured mechanical properties under uniaxial tensile and compression loading in different temperatures and strain rates. The method allows the prediction of pipe service lifetimes in excess of 50 years using experiments conducted over approximately 10 days instead of the traditional 13 months. POLYM. ENG. SCI., 60:879–888, 2020. © 2019 Society of Plastics Engineers

Online Estimation of Missing Data Using Sparse Optimization Techniques With Applications to Classical Control

This paper proposes and describes a method based on sparse optimization techniques for online estimation of missing data. The basic idea of the proposed method is to represent the noise-free signal of interest sparsely in a composite dictionary. The proposed algorithm offers three prime advantages: 1) handles nonlinear processes that are locally linearizable; 2) requires lower computational power than the existing algorithms; and 3) adapts to the changing process conditions by optimally selecting the most relevant feature for that window of time. Results show that the proposed algorithm outperforms the existing neural network-based method. An application of the proposed algorithm to classical (PID) control using measurements with missing data is presented. In this context, an added benefit of the proposed method is that it serves as a feature selection operator for the process, which can be useful in other applications. Numerical (simulation) studies are carried out to study the influence of user-defined parameter in the algorithm and two stochastic factors, namely, the random sampling scheme and noise , on the stability and performance of the closed-loop system. Results show that the closed-loop system with the proposed algorithm is stable and yields a satisfactory performance.

Identification of the targets of hematoporphyrin derivative in lung adenocarcinoma using integrated network analysis

BackgroundHematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma.MethodsRNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using “BioCloud” online tool. Protein–protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software.ResultsA total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated—treated with X-ray—X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429.ConclusionRPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.