Variation Of Process Variables Research Articles

Direct projection to latent variable space for fault detection

Partial least squares (PLSs) often require many latent variables (LVs) T to describe the variations in process variables X correlated with quality variables Y, which are obtained via the traditional nonlinear iterative PLS (NIPALS) optimal solution based on (X, Y). Total projection to latent structures (T-PLSs) performs further decomposition to extract LVs Ty directly related to Y from T, which are obtained by the PCA optimal solution based on the predicted value of Y. Inspired by T-PLS, combined with practical process characteristics, two fault detection approaches are proposed in this paper to solve problems encountered by T-PLS. Without the NIPALS, (X, Y) are projected into the latent variable space determined by main variations of Y directly. Furthermore, the structure and characteristics of several modified methods in statistical analysis are studied based on calculation procedures of solving PCA, PLS and T-PLS optimization problems, and the geometric significance of the T-PLS model is demonstrated in detail. Simulation analysis and case studies both indicate the effectiveness of the proposed approaches.

A new method for the continuous production of single dosed controlled release matrix systems based on hot-melt extruded starch: Analysis of relevant process parameters and implementation of an in-process control

PurposeIn the present study, we evaluated a novel processing technique for the continuous production of hot-melt extruded controlled release matrix systems. A cutting technique derived from plastics industry, where it is widely used for cutting of cables and wires was adapted into the production line. Extruded strands were shaped by a rotary fly cutter. Special focus is laid on the development of a process analytical technology by evaluating signals obtained from the servo control of the rotary fly cutter. The intention is to provide a better insight into the production process and to offer the ability to detect small variations in process-variables. Materials and methodsA co-rotating twin-screw extruder ZSE 27 HP–PH from Leistritz (Nürnberg, Germany) was used to plasticize the starch; critical extrusion parameters were recorded. Still elastic strands were shaped by a rotary fly-cutter type Dynamat 20 from Metzner (Neu-Ulm, Germany). Properties of the final products were analyzed via digital image analysis to point out critical parameters influencing the quality. Important aspects were uniformity of diameter, height, roundness, weight, and variations in the cutting angle. Stability of the products was measured by friability tests and by determining the crushing strength of the final products. Drug loading studies up to 70% were performed to evaluate the capacity of the matrix and to prove the technological feasibility. Changes in viscosities during API addition were analyzed by a Haake Minilab capillary rheometer. X-ray studies were performed to investigate molecular structures of the matrices. ResultsExternal shapes of the products were highly affected by die-swelling of the melt. Reliable reproducibility concerning uniformity of mass could be achieved even for high production rates (>2500cuts/min). Both mechanical strength and die-swelling of the products could be linked to the ratio of amylose to amylopectin. Formulations containing up to 70% of API could still be processed. Viscosity measurements revealed the plasticizing effect caused by API addition. Dissolution data proved the suitability of extruded starch matrices as a sustained release dosage form. Monitoring of consumed energies during the cutting process could be linked to changes in viscosity. The established PAT system enables the detection of small variations in material properties and can be an important tool to further improve process stability.

Effect of Ag Addition on the Microstructure and Properties of Cu-5Cr Base Alloys

Small amount of Ag was added to high conductivity Cu-5%Cr alloy in order to increase the strength without sacrificing the conductivity. The typical microstructure of thermo-mechanically processed specimen mainly consists of Cu matrix and relatively large Cr phase. Although this typical microstructure was not changed with varied process variables, both the micro-hardness and conductivity were significantly affected by them. The electrical conductivity of alloy was slightly decreased by the Ag addition, but the micro-hardness could be enhanced by it. TEM analyses indicated that very fine Cr precipitates were formed in addition to the relatively large Cr phase in the Cu matrix. It is suggested that the properties of Cu-5Cr-xAg alloys can be optimized by carefully controlling the precipitation of the fine precipitates.

Application of dynamic models to estimate greenhouse gas emission by wastewater treatment plants of the pulp and paper industry

Greenhouse gas (GHG) emission in wastewater treatment plants of the pulp-and-paper industry was estimated by using a dynamic mathematical model. Significant variations were shown in the magnitude of GHG generation in response to variations in operating parameters, demonstrating the limited capacity of steady-state models in predicting the time-dependent emissions of these harmful gases. The examined treatment systems used aerobic, anaerobic, and hybrid-anaerobic/aerobic-biological processes along with chemical coagulation/flocculation, anaerobic digester, nitrification and denitrification processes, and biogas recovery. The pertinent operating parameters included the influent substrate concentration, influent flow rate, and temperature. Although the average predictions by the dynamic model were only 10 % different from those of steady-state model during 140 days of operation of the examined systems, the daily variations of GHG emissions were different up to ± 30, ± 19, and ± 17 % in the aerobic, anaerobic, and hybrid systems, respectively. The variations of process variables caused fluctuations in energy generation from biogas recovery by ± 6, ± 7, and ± 4 % in the three examined systems, respectively. The lowest variations were observed in the hybrid system, showing the stability of this particular process design.

Characterization of Bioelectrochemical Fuel Cell Fabricated With Agriculture Wastes and Surface Modified Electrode Materials

A bioelectrochemical fuel was fabricated with pretreated and fermented rice husks. The fuel was characterized with variation of process variables by determination of chemical oxygen demand (COD) which is a measure of the oxygen equivalent of electrochemically oxidizable organic fuel to produce electrical energy. The electrodes of the cell were made with nanoporous pure Al coated with platinum, platinum-ruthenium, and platinum-ruthenium-carbon. Anodization parameters were optimized by studying E-I characteristics in sulfuric and oxalic acids with variation of concentration and temperature. Pore size on the order of 30–50 nm was obtained by a two stage anodization. The performance of the cell was evaluated by determining open circuit potential, E-I characteristics, polarization studies, and cyclic voltammetry. A steady onload potential of 600–800 mV was obtained with current density on the order of 15–25 mA/cm2. High power density of 10–15 mW/cm2 has been obtained with electrode materials coated with Pt + Ru or Pt + Ru + C. The performance of coating on nanoporous structure was greatly reflected in the polarization studies, which showed a huge reduction of polarization resistance and increase of exchange current density by many times, the effect being more for anode with anodic solution, fermented rice husk, than with cathode with phosphate buffer cathodic solution. The surface morphology examined by SEM, showed nanodeposits of Pt, Pt-Ru, and the presence of carbon like structure. XRD peaks clearly reveal presence of Pt, Pt-Ru, and carbon.

Modelling for surface roughness in cylindrical grinding

In the present study, response surface methodology (RSM) is used to investigate the relationships and parametric interactions between the three controllable variables and surface roughness in cylindrical grinding. Experiments are conducted on mild steel specimens by varying process variables infeed, longitudinal feed and work speed. The surface roughness has been measured using contact stylus method. Surface roughness parameter ‘Ra’ is used to evaluate surface characteristics of the machined specimens. To model the proposed second-order polynomial model for surface roughness the Box Behnken experimental design has been used to estimate the model coefficients of the three factors, which are believed to influence the surface roughness in cylindrical grinding process. The response is modelled using a response surface model based on experimental results. The significant coefficients have been obtained by performing analysis of variance (ANOVA) at 5% level of significance. From the ANOVA, it is found that process parameters do not have much significant effect on the surface roughness parameter (Ra), then response surface methodology’s response optimiser is used to developing the model for prediction. The predicted result shows considerable improvement in surface roughness.

Applicability domains and accuracy of prediction of soft sensor models

AbstractSoft sensors are used to estimate process variables that are difficult to measure online. However, the predictive accuracy gradually decreases with changes in the state of chemical plants. Regression models can be updated, but if the model is updated with abnormal data, the predictive ability deteriorates. In practice, when the prediction error of an objective variable exceeds a threshold, an abnormal situation is detected. However, no effective method exists to decide this threshold. We have proposed a method to estimate the relationships between applicability domains and the accuracy of prediction of soft sensor models quantitatively. The larger the distances to models (DMs), the lower the estimated accuracy of prediction. Hence, the model between DMs and accuracy can separate variations in process variables and y‐analyzer fault. This method was applied to real industrial data. The fault detection ability of the proposed method was better than that of the traditional one. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Contribution to safety of immunoglobulin and albumin from virus partitioning and inactivation by cold ethanol fractionation: a data collection from Plasma Protein Therapeutics Association member companies

Virus removal by partitioning into different fractions during cold ethanol fractionation has been described by several authors, demonstrating that cold ethanol fractionation can provide significant contribution to virus removal, even in those cases where virus removal is limited and must be supported by additional measures for virus inactivation during further processing. Plasma Protein Therapeutics Association (PPTA) member companies collected and evaluated 615 studies on virus removal by the steps of the cold ethanol fractionation process. The studies describe the precipitation and separation of Fraction (F)III or FI/III in the immunoglobulin fractionation process and precipitation and separation of FII/III, FI/II/III, and FIV/IV in the albumin fractionation process. The data indicate a significant contribution of cold ethanol fractionation to the overall clearance of a broad spectrum of viruses, at varied process variables such as pH, temperature, and alcohol concentration and demonstrate the robustness of virus removal by the cold ethanol fractionation process. The data presented here support the importance of the partitioning steps for virus safety for immunoglobulins and albumin. However, virus removal by cold ethanol fractionation alone cannot provide viral safety of human albumin and immunoglobulins and therefore must be completed by other virus inactivation and removal procedures.

Influence of Structuring by Abrasive Machining on the Tribological Properties of Workpiece Surfaces

Surface structuring of workpieces has a significant influence on the tribological properties of contact partners and can therefore lead to a significant increase of the life time of components [1]. To identify an appropriate surface structure, analytical and experimental investigations were done at the Institute for Machine Tools and Factory Management (IWF) in order to improve the tribological properties in the contact area appreciably. To validate the theoretical analyses, technological investigations were carried out. 3D-surface structures were created on workpiece surfaces by varying process variables for a high speed 5-axes machine system to generate knowledge for developing defined surface structures by using kinematically modified grinding processes. Initial results show a positive influence of defined surface structures on the performance of contact partners.

Robust Design for Fixture Layout in Multistation Assembly Systems Using Sequential Space Filling Methods

Fixture layout robust design of multistation manufacturing systems aims for an optimal design that enables the dimensional variation of a product insensitive to the variations of process variables in the manufacturing process. The robust design involves a high dimension and complex global optimization problem. Recent advances in stream of variation modeling techniques enable effective formulation of the optimization problem at the system level. However, there is a challenge of computation complexity in terms of searching optimal design parameters in a high dimension, nonconvex, and discontinuous design space. This makes many available algorithms ineffective or even invalid. In this paper, an alternative sequential space filling strategy is proposed, which adopts sampling approaches to search optimal designs. To improve computation efficiency, the search space is sequentially reduced to generate a series of subspaces, and a method is designed to ensure a complete coverage of these subspaces in the original feasible space. In order to validate the proposed method, a floor pan assembly from an automotive body assembly process is modeled, and then the fixture robust design is conducted with the developed methods. To show the effectiveness of the proposed method, genetic algorithm and sequential quadratic programming are also applied in the case study for comparison.

Evolution of Artificial Neural Network (ANN) model for predicting secondary dendrite arm spacing in aluminium alloy casting

Extensive solidification simulations are conducted using finite difference method on an aluminium alloy casting. Orthogonal experimental array layout is considered for running experimental simulations. Microstructural parameter Secondary Dendrite Arm Spacing (SDAS) at three different locations was predicted as response variable, through solidification simulations by varying the process parameters. The input process variables are pouring temperature, insulation on riser and chill volume heat capacity. An Artificial Neural Network (ANN) model was developed to predict the response variable for varied input process variables. Through sensitivity analysis the influence of input process variables on output response was obtained. The results obtained from solidification simulations and ANN model are validated experimentally.

A study on the characteristic of parameters by the response surface method in final wafer polishing

As the level of Si-wafer surface directly affects device line-width capability, process latitude, yield, and throughput in fabrication of microchips, it needs to have ultra precision surface and flatness. Polishing is one of the important processing having influence on the surface roughness in manufacturing of Si-wafers. The surface roughness in final wafer polishing is mainly affected by the many process parameters. For decreasing the surface, the control of polishing parameters is very important. In this paper, the optimum condition selection of ultra precision wafer polishing and the effect of polishing parameters on the surface roughness were evaluated by using central composite designs such as the Box-Behnken method. Moreover, in accordance with variation of process variables, there is a temperature change on pad surface. And so, this paper also researches that this temperature variation affects surface roughness of Si-wafer.

Statistical Modeling and Analysis for Robust Synthesis of Nanostructures

We systematically investigate the best process conditions that ensure synthesis of different types of one-dimensional cadmium selenide nanostructures with high yield and reproducibility. Through a designed experiment and rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a multinomial generalized linear model is proposed and used. The optimum process conditions, which maximize the preceding probabilities and make the synthesis process robust (i.e., less sensitive) to variations in process variables around set values, are derived from the fitted models using Monte Carlo simulations.Cadmium selenide has been found to exhibit one-dimensional morphologies of nanowires, nanobelts, and nanosaws, often with the three morphologies being intimately intermingled within the as-deposited material. A slight change in growth condition can result in a totally different morphology. To identify the optimal process conditions that maximize the yield of each type of nanostructure and, at the same time, make the synthesis process robust (i.e., less sensitive) to variations of process variables around set values, a large number of trials were conducted with varying process conditions. Here, the response is a vector whose elements correspond to the number of appearances of different types of nanostructures. The fitted statistical models would enable nanomanufacturers to identify the probability of transition from one nanostructure to another when changes, even tiny ones, are made in one or more process variables. Inferential methods associated with the modeling procedure help in judging the relative impact of the process variables and their interactions on the growth of different nanostructures. Owing to the presence of internal noise, that is, variation around the set value, each predictor variable is a random variable. Using Monte Carlo simulations, the mean and variance of transformed probabilities are expressed as functions of the set points of the predictor variables. The mean is then maximized to find the optimum nominal values of the process variables, with the constraint that the variance is under control.

Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy

In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of the control path and process flow. This paper describes a data-driven method for identifying the direction of propagation of disturbances using historical process data. The novel concept is the application of transfer entropy, a method based on the conditional probability density functions that measures directionality of variation. It is sensitive to directionality even in the absence of an observable time delay. Its performance is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting. Two industrial case studies demonstrate the method

Advance in fluidized bed coating: An experimental investigation on a performance polymer coating alloy

An experimental analysis of the behaviour of a performance polymer coating alloy in a fluidized bed coating process was led. Major variables affecting coating thickness were analyzed and other variables as part-powder relative motion, part attitude, part location in the bed and bed density closer to the part were found out to deeply influence the performance of the coating process. Consistent trend in coating thickness and in roughness and waviness parameters were achieved varying process variables and a model, based on experimental findings, was stated as first support to coaters to best deal with the most part of operative variables of the coating process.

Prediction of Dissolution Profiles of Acetaminophen Beads Using Artificial Neural Networks

Immediate release acetaminophen (APAP) beads with 40% drug loading were prepared using the extrusion-spheronization process. Eighteen batches of beads were prepared based on a full factorial design by varying process variables such as extruder type, extruder screw speed, spheronization speed, and spheronization time. An in vitro dissolution test was carried out using the USP 27 Apparatus II (paddle) method. Artificial Neural Network (ANN) models were developed based on the aforementioned process variables and dissolution data. The trained ANN models were used to predict the dissolution profiles of APAP from the beads, which were prepared with various processing conditions. For training the ANN models, process variables were used as inputs, and percent drug released from APAP beads was used as the output. The dissolution data from one out of 18 batches of APAP beads was selected as the validation data set. The dissolution data of other 17 batches were used to train the ANN models using the ANN software (AI Trilogy®) with two different training strategies, namely, neural and genetic. The validation results showed that the ANN model trained with the genetic strategy had better predictability than the one trained with the neural strategy. The ANN model trained with the genetic strategy was then used to predict the drug release profiles of two new batches of APAP beads, which were prepared with process variables that were not used during the ANN model training process. However, the process variables used to prepare the two new batches of APAP beads were within the confines of the process variables used to prepare the 18 batches. The actual drug release profile of these two batches of APAP beads was similar to the ones predicted by the trained and validated ANN model, as indicated by the high f2 values. Furthermore, the ANN model trained with genetic strategy was also used to optimize process variables to achieve the desired dissolution profiles. These batches of APAP beads were then actually prepared using the process variables predicted by the trained and validated ANN model. The dissolution results showed that the actual dissolution profiles of the APAP beads prepared from the predicted process variables were similar to the desired dissolution profiles.

Process data visualisation with biplots

In this paper a modern methodology of biplots is introduced as a means for monitoring the behaviour of process systems. This sophisticated methodology allows for the projection of high-dimensional data to a low-dimensional subspace that can be visualised by a human operator. The projections are highly graphical in nature, and rich in information regarding variation in process variables, correlations among these variables, as well as class separation, taking into account the multivariate character of the data. Moreover, as is shown by way of two case studies, process disturbances can be visualised and explored quantitatively by superimposing alpha-bags on biplots.

Mechanism of low-temperature θ-CuGa2 phase formation in Cu-Ga alloys by mechanical alloying

The mechanism on the formation of the θ-CuGa2 phase in binary Cu–Ga alloys has been investigated through mechanical alloying (MA) of blended elemental powders by varying process variables such as milling time and milling temperature. The particle size distribution was very broad at the beginning of milling but became narrower as the milling time increased and steady-state equilibrium was reached. The average powder particle size reached a peak value of 270μm at 30min of milling and then continued to decrease gradually to 6μm on milling for 20h. Formation of the θ-CuGa2 phase started to occur even after milling for 2min and was completed after 1h of milling. Melting of Ga was noted in the early stages of milling, probably due to the rise in powder temperature. To discount the possibility that the melting of Ga was responsible for the θ-phase formation, milling was conducted at lower temperatures by dripping liquid nitrogen on to the container. The θ-phase still formed, suggesting that its formation was not related to the melting of Ga and that it was formed by a solid-state reaction even at low temperatures. The compositional homogeneity range of the θ-phase was also extended under the MA conditions. Details of the mechanism of phase formation, as observed by x-ray diffraction and scanning electron microscopy methods, are presented.

Monitoring of metallurgical process plants by using biplots

AbstractAlthough principal component analysis has been applied widely for monitoring plant performance in a broad range of industrial processes, the use of accompanying biplots has not received similar attention. Moreover, principal component analysis is a linear technique that tends to break down when processes exhibit significant nonlinear behavior. In this paper biplot methodology is introduced. This methodology allows for projecting high‐dimensional data to a low‐dimensional subspace that can be visualized by a human operator. It provides management with sophisticated tools, highly graphical in nature, to extract information regarding variation in process variables, correlations among these variables, as well as class separation, taking into account the multidimensional character of the data. Biplot methodology can also be applied to data sets exhibiting nonlinear behavior. As is shown by way of two case studies, operating regions can be quantitatively explored by superimposing alpha bags on biplots, which facilitates the automatic detection and visualization of process disturbances. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2167–2186, 2004

Vertical Etching of Ru Electrodes Using the O<sub>2</sub>/Cl<sub>2</sub> System

In this study, the etching characteristics of ruthenium (Ru) electrode using O2/Cl2 helicon plasmas were investigated. The variation of Ru etch rate, Ru to SiO2 etch selectivity and Ru etching slope with varied process variables were scrutinized. Not only the gas chemistry affected the Ru etching, but also the higher bias power and argon (Ar) bombardment helped to faciliate the Ru etching.