Control Of Process Variables Research Articles

Modeling and prediction of flotation performance using support vector regression

Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR), is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

Process control strategies for flexible operation of post-combustion CO2 capture plants

With increasing penetration of intermittent renewable energy into the electricity grid, one can expect thermal power plants to be required to operate in a more dynamic fashion, with more frequent departures from design point operation. However, the application of optimal control strategies can offer solutions to these operational challenges, associated with the integration of the power plant with the capture plant. In this paper a process control strategy is developed in order to select the optimal control variables for a PCC process. In addition, economically efficient control structures for operation of a post-combustion capture process with minimum energy requirements for coal and natural gas power plant are designed. The results have shown that with an appropriate and well-tuned control strategy, it is possible to maintain critical parameters, such as the degree of CO2 capture, at the desired set-point, even during periods of significant fluctuation in the power plant load and even if based on simple and well established control technologies, such as PID, avoids the need for more risky solutions such as adding solvent storage tanks to the process.

Process control model for growth rate of molecular beam epitaxy of MgO (111) nanoscale thin films on 6H-SiC (0001) substrates

Magnesium oxide (MgO) is a good candidate for an interface layer in multifunctional metal-oxide nanoscale thin-film heterostructures due to its high breakdown field and compatibility with complex oxides through O bonding. In this research, molecular beam epitaxy (MBE) is used to deposit 10 nm to 15 nm MgO single-crystal films on silicon carbide with hexagonal polytype 6H (6H-SiC) to serve as an interface layer for effective integration of functional oxides. In this work, the effect of MBE process control variables on the growth rate of the MgO film measured in nanometers per minute is investigated. Experiments are conducted at various process conditions and the resulting MgO film growth rate at each combination of process conditions is measured. The process control variables studied are the substrate temperature (100 °C – 300 °C), magnesium source temperature (328 °C – 350 °C), plasma intensity (0 mV – 550 mV), and percentage oxygen on the starting surface of 6H-SiC substrate (9 % – 13 %) after the substrate is prepared by high-temperature hydrogen etching. The film thickness is computed using the effective attenuation length (EAL) of silicon photoelectron peak intensity as measured by x-ray photoelectron spectroscopy (XPS). The film thickness is converted to growth rate by dividing it with the duration of film growth. Using the experimental data, a neural network model is developed to estimate growth rate for any given process variable combination. From this neural network model, multiple replications of data were generated to conduct a 3-level full factorial design of experiments and response surface-based analysis. The study reveals that the plasma intensity has the most significant influence on growth rate. The results indicate that growth rate is relatively low on high-quality substrates with √3 × √3 R30° reconstructed 6H-SiC (0001) surface with optimum oxygen content (approximately 10 %); in contrast, the growth rate is relatively high on substrates with high surface roughness and excessive oxygen on the starting substrate surface.

Multivariate Analysis of Hemicelluloses in Bleached Kraft Pulp Using Infrared Spectroscopy.

The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation. Here we show that ATR FT-IR with discrete wavelet transform (DWT) and standard normal variate (SNV) spectral preprocessing offers a convenient means for the qualitative and quantitative analysis of hemicelluloses in bleached kraft pulp and alkaline treated kraft pulp. The pulp samples investigated include bleached softwood kraft pulps, bleached hardwood kraft pulps, and their mixtures, as obtained from Canadian industry mills or blended in a lab, and bleached kraft pulp samples treated with 0-6% NaOH solutions. In the principal component analysis (PCA) of these spectra, we find the potential both to differentiate all pulps on the basis of hemicellulose compositions and to distinguish bleached hardwood pulps by species. Partial least squares (PLS) multivariate analysis gives a 0.442 wt% root mean square errors of prediction (RMSEP) for the prediction of xylan content and 0.233 wt% RMSEP for the prediction of mannan content. These data all support the idea that ATR FT-IR has a great potential to rapidly and accurately predict the content of xylan and mannan for bleached kraft pulps (softwood, hardwood, and their mixtures) in industry. However, the prediction of xylan and mannan concentrations presented a difficulty for pulp samples with modified cellulose crystalline structure.

Multiobjective Optimization in Micromachining of Aluminum (Al 7075) using Taguchi Technique

In present scenario, the demand for precise micro components in the field of aerospace, mechanical, optics, electronics and biotechnology is increasing. This current work aims for optimization in micromilling of Al 7075 material using Taguchi robust technique. This work is carried out under three stages; experimental work, modelling and multiobjective optimization. Initially, micromilling experiments are carried out by constructing taguchi L 9 orthogonal array keeping spindle speed, feed rate and depth of cut as controlled process variables, while results are analyzed using MINITAB 15 software for the responses Metal removing rate and Surface roughness. Regression Model replicating MRR and Surface roughness is created. To optimize all responses, Taguchi signal to noise ratio is used. Finally, analysis of variance (ANOVA) is done and multiobjective optimization results are predicted. It was found that for the optimized conditions of MRR and surface roughness spindle speed should be kept 2600 rpm, feed rate 600 mm/min and depth of cut should be in the range of 0.07 mm. From ANOVA it can be anticipated that feed rate is the most influencing factor which contributes 65% followed by spindle speed 18% and depth of cut 10%. It can be said that metal removing rate is adversely affected by feed rate while surface roughness is influenced by spindle speed.

An integrated evolutionary approach for simultaneous optimization of laser weld bead characteristics

The selection of optimal welding parameters in any welding process significantly improves the quality, production rate, and cost of a component. The weld bead characteristics such as bead width, depth of penetration, and heat-affected zone are the prominent factors for evaluating the performance of a welded joint. The work presents a novel evolutionary multi-objective optimization approach to derive the optimal laser welding conditions for the weld bead geometrical parameters. The welding experiments were conducted with the consideration of pulse frequency, pulse width, welding speed, and pulse energy as the process-control variables to evaluate the weld bead characteristics. Empirical models for the bead characteristics were developed in terms of the input variables using response surface methodology. The individual and interactive effects of the variables on the responses were also analyzed. As the influence of control variables on the bead characteristics is conflicting in nature, the problem is formulated as a multi-objective optimization problem to simultaneously optimize the output parameters. The aim is to simultaneously minimize the bead width, maximize the depth of penetration, and minimize the heat-affected zone. An efficient evolutionary algorithm called non-dominated sorting genetic algorithm-II was applied to derive the set of Pareto-optimal solutions. The derived optimal process responses were confirmed with the experimental values. The proposed integrated methodology can be applied to any welding process to automate the process conditions in computer-integrated manufacturing environment.

Multiobjective optimization of injection molding using a calibrated predictor based on physical and simulated data

This paper presents a method for improving injection molding processes having competing performance measures using multiobjective optimization. The procedure uses calibrated predictors that combine physical and simulated data to estimate the values of the performance measures. After the predictors are built, the values of the selected performance measures are estimated at a grid of process control variables, and a set of predicted Pareto solutions is identified using nondominance criteria. A refinement of the original Pareto solutions is obtained by predicting the performance measures at a finer grid of the process variables near the original Pareto set. Finally, as validation, a subset of these solutions is evaluated on the physical process. A case study with three performance measures is presented to show how the calibrated predictors allow the injection molding manufacturer to identify the processing conditions that optimize a process having competing objectives. POLYM. ENG. SCI., 57:248–257, 2017. © 2016 Society of Plastics Engineers

Prediction of Bead Geometry for Flux Bounded TIG Welding of AA 2219-T87 Aluminum Alloy

To improve penetration capability of Tungsten Inert Gas (TIG) process, Flux Bounded TIG (FBTIG) is investigated in aluminum alloy AA 2219 T87. Characterization of this process is essential to predict and control the weld bead quality. For characterization, design of experiments based on central composite rotatable design is employed for the experiments. Response Surface Methodology (RSM) is used to develop a mathematical model to correlate the bead geometry with controllable process variables. The model developed is checked for its adequacy by regression by using the [Formula: see text]-test and Analysis of Variance (ANOVA). By backward elimination method, reduced model is obtained from the full model. The process parameters are optimized to get desired weld bead geometry. The model is validated by confirmatory tests. The main and interaction effects of the process variables on bead geometry are presented in graphical form. The results are useful to predict the weld bead geometry for different combinations of process variables.

Кореляційний аналіз рівня фізичної підготовленості, фізичного стану курсантів та успішності проходження льотної практики

Purpose: to estimate the density of the correlation between the measures of the quality of students in summer training. Material and Methods: the monitoring of cadets at the stages of flight training, the method of correlation analysis. Results: It was found that for the assessment of the nature and extent of communication between all pairs of managerial and controlled process variables control the quality of training of pilots, should be, along with the correlation statistical analysis of grade distribution laws studied random variables and statistical hypothesis testing of their belonging to a particular type. Conclusions: with a high degree of reliability of the result obtained on the weak relationship between the level of physical fitness, the physical condition of the students and the successful passing of cadets flight experience

High-Intensity Ultrasound to Improve Physical and Functional Properties of Lipids.

High-intensity ultrasound (HIU) has been used in recent years to change the crystallization behavior of edible lipids. This technique can be used in combination with other processing technologies to tailor lipids' functional properties and broaden their application for various food products. In general, sonication induces crystallization, increases crystallization rate, and generates a harder and more elastic crystalline network characterized by smaller crystals with a sharper melting profile. An important application of HIU is to improve the hardness and elasticity of shortenings that have a low content of saturated fatty acids and are free of trans-fats. This review summarizes recent research that used HIU to change the physical and functional properties of edible lipids and focuses on the importance of controlling processing variables such as sonication power level and duration and crystallization temperature.

Noncontact conductivity and dielectric measurement for high throughput roll-to-roll nanomanufacturing.

Advances in roll-to-roll processing of graphene and carbon nanotubes have at last led to the continuous production of high-quality coatings and filaments, ushering in a wave of applications for flexible and wearable electronics, woven fabrics, and wires. These applications often require specific electrical properties, and hence precise control over material micro- and nanostructure. While such control can be achieved, in principle, by closed-loop processing methods, there are relatively few noncontact and nondestructive options for quantifying the electrical properties of materials on a moving web at the speed required in modern nanomanufacturing. Here, we demonstrate a noncontact microwave method for measuring the dielectric constant and conductivity (or geometry for samples of known dielectric properties) of materials in a millisecond. Such measurement times are compatible with current and future industrial needs, enabling real-time materials characterization and in-line control of processing variables without disrupting production.

A statistical quality monitoring method for plastic injection molding using machine built-in sensors

Majority of the current quality monitoring for injection molding relies on extra sensors assembled within the mold, which not only is restricted by the mold structure but also requires additional costly investment. A challenge in quality monitoring using machine built-in sensors is identifying couplings among different control trajectories and complicated effects on the product quality. In this paper, a statistical quality monitoring method is proposed, using only hydraulic pressure and screw position data obtained from built-in machine sensors. Statistical variables, which are representatives of the product quality in one batch, are first automatically extracted from the original data acquired. They are subsequently monitored by applying the principal component analysis method. Experimental results show that the rate of successful fault detection of the present method reaches 91.48 % at the confidence level 99 %, compared to 3.93 % using the multi-way principal component analysis (MPCA) method. Statistical variables are proved to be more effective and reliable for quality monitoring and fault detection compared with the whole process control variables, which are used in the classical MPCA, owning to a better compliance with the Gaussian distribution.

"I should be doing more for my parent:" Chinese adult children's worry about performance in providing care for their oldest-old parents.

In China, as is the case in most of the world, family is the largest source of support for frail older adults. Confucian filial piety expectations hold children-in particular sons-responsible for the care of aging parents in China. During the course of caregiving, in addition to positive feelings, the caregiver may feel overwhelmed, entrapped, and worried about their performance, which can have negative health consequences. The purpose of this exploratory study is to examine one dimension of caregiver burden "worry about performance" (WaP) and investigate whether variables inspired by Pearlin's Stress Process Model help to explain the variation of WaP, so that at-risk caregivers can be identified and supported. Multiple regression was used to analyze CLHLS data from 895 Chinese adult children providing care for their oldest old parent in eight coastal provinces in China. WaP was measured by two items from the Zarit Burden Inventory. Independent variables representing concepts from Pearlin's Stress Process Model and control variables were included in the models. Compared to a model of control variables only, using independent variables inspired by Pearlin's Stress Process Model increased the ability to explain the variation in WaP by three fold to 14%. The following variables increased WaP: parent's IADL level, the amount of time spent caregiving, being a daughter, reporting an emotionally close relationship with the parent, sharing a residence with the parent. Despite not being able to fully operationalize Pearlin's Model with this dataset, analyzing variables that represent concepts from the model was useful in increasing the ability to explain the variation in WaP. Results suggest that caregiver support directed at daughters may be particularly beneficial.

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile.

Prediction of optimum weld pool geometry of PCTIG welded titanium alloy using statistical design

Mechanical strength of the weldments is not only influenced by the composition of the metals but selection of process parameters and weld bead profile also play a vital role in determining the strength. The relationships between the process parameters and the bead parameters controlling the bead shape are to be established. This is achieved by the development of mathematical expressions, relating the weld bead dimensions to the important process control variables affecting these dimensions. Also, optimization of the process parameters to control and obtain the required shape and quality of weld beads is also made possible with these expressions. The pulsing current parameters on weld pool geometry namely front height, back height, front width and back width of pulsed current tungsten inert gas welding (PCTIG) of titanium alloy was analyzed. Box–Behnken design was used to develop empirical relationships, incorporating pulsed current parameters and weld pool geometry.

A change point estimator in monitoring the parameters of a multivariate IMA(1, 1) model

Abstract Modern production process is a very complex structure combined observations whichare correlated with several factors. When the error signal occurs in the process, it isvery dicult to know the root causes of an out-of-control signal because of insucientinformation. However, if we know the time of the change, the system can be controlledmore easily. To know it, we derive a maximum likelihood estimator (MLE) of thechange point in a process when observations are from a multivariate IMA(1,1) processby monitoring residual vectors of the model. In this paper, numerical results show thatthe MLE of change point is e ective in detecting changes in a process.Keywords: Change point, maximum likelihood estimator, multivariate exponentiallyweighted moving average control chart. 1. Introduction Statistical process control (SPC) charts are utilized for the purpose of monitoring for pro-cess changes by nding the special causes of variation. A control chart statistic is comparedto one or more control limits with data are gathered from a process. If the control chartstatistic exceeds a control limit, then special causes exist in the process. Although the qual-ity of products is characterized by several variables correlated in real industrial processes,most previous studies of process have been implemented in one variable process. If qualitycharacteristics are inuenced by several factors, one variable process control chart is usefulno longer. The objective of multivariate SPC charts is nding the special causes of variationlike those of univariate SPC. The charts send a signal, when the mean vector shifted, orwhen the covariance matrix perturbed, or when both the mean vector and the covariancematrix are shifted simultaneously.Hotelling’s T2 charts only take into account the present information of the process, thusbeing little powerful for detecting small changes. To improve eciency in the case of smallchanges in the process, mulitivariate the exponentially weighted moving average (EWMA)control chart was developed. The advantage of the chart is that it can give considerationto the present and past information of the process. Therefore, it is more powerful to detectsmall changes than Hotelling’s T2.

Deactivation and Regeneration of Commercial Type Fischer-Tropsch Co-Catalysts—A Mini-Review

Deactivation of commercially relevant cobalt catalysts for Low Temperature Fischer-Tropsch (LTFT) synthesis is discussed with a focus on the two main long-term deactivation mechanisms proposed: Carbon deposits covering the catalytic surface and re-oxidation of the cobalt metal. There is a great variety in commercial, demonstration or pilot LTFT operations in terms of reactor systems employed, catalyst formulations and process conditions. Lack of sufficient data makes it difficult to correlate the deactivation mechanism with the actual process and catalyst design. It is well known that long term catalyst deactivation is sensitive to the conditions the actual catalyst experiences in the reactor. Therefore, great care should be taken during start-up, shutdown and upsets to monitor and control process variables such as reactant concentrations, pressure and temperature which greatly affect deactivation mechanism and rate. Nevertheless, evidence so far shows that carbon deposition is the main long-term deactivation mechanism for most LTFT operations. It is intriguing that some reports indicate a low deactivation rate for multi-channel micro-reactors. In situ rejuvenation and regeneration of Co catalysts are economically necessary for extending their life to several years. The review covers information from open sources, but with a particular focus on patent literature.

Machining Unidirectional Composites using Single-Point Tools: Analysis of Cutting Forces, Chip Formation and Surface Integrity

The need for high quality machining of composite materials is rising due to the increased utilisation of these materials across several applications. This paper presents experimental findings of orthogonal cutting of unidirectional glass fibre reinforced plastic (UD-GFRP) composites using HSS single-point cutting tools. Key process indicators including cutting forces, chip formation and surface integrity were evaluated. Full factorial design is employed with fibre orientation, depth of cut, cutting speed and rake angle as process control variables. Fibre orientation and depth of cut were found to be the most significant factors affecting the investigated responses. Lower cutting forces and better surface quality were obtained at 0o fibre orientation and lower depth of cut. Cutting at 45o fibre orientation generated extremely damaged surfaces with relatively high average surface roughness values and should be avoided in practical applications.

Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products

Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Δx, (2) control process variables x based on the observed product deviation Δy, and (3) control input product geometry y based on the observed product deviation Δy. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class of product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Experimental investigation and validation of polyhedrons a indicates the promise of predicting and compensating a wide class of products built through 3D printing technology.

English

The objective of this work is to demonstrate the operation of a system that allows monitoring and controlling process variables (PV) such as Temperature, Pressure, Flow and Level Control using Labview. The corresponding PV values are measured and converted into Digital Signals using NI-DAQ and these are controlled in LabView. The Control signal is sent back to the process station via Ni-DAQ to the corresponding Actuators. The main idea behind is to make a real time fully automated control system using LabVIEW.