Monitoring process mean and variability with one non-central chi-square chart

Traditionally, an (X) over bar -chart is used to control the process mean and an R-chart to control the process variance. However, these charts are not sensitive to small changes in process parameters. A good alternative to these charts is the exponentially weighted moving average (EWMA) control chart for controlling the process mean and variability, which is very effective in detecting small process disturbances. In this paper, we propose a single chart that is based on the non-central chi-square statistic, which is more effective than the joint (X) over bar and R charts in detecting assignable cause(s) that change the process mean and/or increase variability. It is also shown that the EWMA control chart based on a non-central chi-square statistic is more effective in detecting both increases and decreases in mean and/or variability.

The Variable Sample Size (X)over-bar Chart with Estimated Parameters

The VSS X chart, dedicated to the detection of small to moderate mean shifts in the process, has been investigated by several researchers under the assumption of known process parameters. In practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS chart when the process parameters are estimated, we compare them in the case where the process parameters are assumed known and we propose specific optimal control chart parameters taking the number of Phase I samples into account.

Preliminary results concerning the VSS X- chart with unknow in-control parameters

The VSS X- chart is known to perform better than the traditional X- control chart in detecting small to moderate mean shifts in the process. Many researchers have used this chart in order to detect a process mean shift under the assumption of known parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS X- control chart when the process parameters are estimated and we compare them in the case where the process parameters are assumed known. We draw the conclusion that these performances are quite different when the shift and the number of samples used during the phase I are small. ©2010 IEEE.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

Separation of satured and unsaturated fatty acids from palm fatty acids distillates in continuous multistage countercurrent columns with supercritical carbon dioxide as solvent: a process design methodology

In this work the separation of multicomponent mixtures in counter-current columns with supercritical carbon dioxide has been investigated using a process design methodology. First the separation task must be defined, then phase equilibria experiments are carried out, and the data obtained are correlated with thermodynamic models or empirical functions. Mutual solubilities, Ki-values, and separation factors aij are determined. Based on this data possible operating conditions for further extraction experiments can be determined. Separation analysis using graphical methods are performed to optimize the process parameters. Hydrodynamic experiments are carried out to determine the flow capacity diagram. Extraction experiments in laboratory scale are planned and carried out in order to determine HETP values, to validate the simulation results, and to provide new materials for additional phase equilibria experiments, needed to determine the dependence of separation factors on concetration. Numerical simulation of the separation process and auxiliary systems is carried out to optimize the number of stages, solvent-to-feed ratio, product purity, yield, and energy consumption. Scale-up and cost analysis close the process design. The separation of palmitic acid and (oleic+linoleic) acids from PFAD-Palm Fatty Acids Distillates was used as a case study.

Use of uniform designs in combination with neural networks for viral infection process development

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.

Nanoparticle preparation process using novel microjet reactor technology for enhancing dissolution rates of poorly water soluble drugs

In this study a novel method MicroJet reactor technology was developed to enable the custom preparation of nanoparticles. rnDanazol/HPMCP HP50 and Gliclazide/Eudragit S100 nanoparticles were used as model systems for the investigation of effects of process parameters and microjet reactor setup on the nanoparticle properties during the microjet reactor construction. rnFollowing the feasibility study of the microjet reactor system, three different nanoparticle formulations were prepared using fenofibrate as model drug. Fenofibrate nanoparticles stabilized with poloxamer 407 (FN), fenofibrate nanoparticles in hydroxypropyl methyl cellulose phthalate (HPMCP) matrix (FHN) and fenofibrate nanoparticles in HPMCP and chitosan matrix (FHCN) were prepared under controlled precipitation using MicroJet reactor technology. Particle sizes of all the nanoparticle formulations were adjusted to 200-250 nm. rnThe changes in the experimental parameters altered the system thermodynamics resulting in the production of nanoparticles between 20-1000 nm (PDI<0.2) with high drug loading efficiencies (96.5% in 20:1 polymer:drug ratio).rnDrug releases from all nanoparticle formulations were fast and complete after 15 minutes both in FaSSIF and FeSSIF medium whereas in mucodhesiveness tests, only FHCN formulation was found to be mucoadhesive. Results of the Caco-2 studies revealed that % dose absorbed values were significantly higher (p<0.01) for FHCN in both cases where FaSSIF and FeSSIF were used as transport buffer.rn

Proposal on How To Conduct a Biopharmaceutical Process Failure Mode and Effect Analysis (FMEA) as a Risk Assessment Tool

With the publication of the quality guideline ICH Q9 "Quality Risk Management" by the International Conference on Harmonization, risk management has already become a standard requirement during the life cycle of a pharmaceutical product. Failure mode and effect analysis (FMEA) is a powerful risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to biopharmaceutical processes brings about some difficulties. The proposal presented here is intended to serve as a brief but nevertheless comprehensive and detailed guideline on how to conduct a biopharmaceutical process FMEA. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. The application for such a biopharmaceutical process FMEA is widespread. It can be useful whenever a biopharmaceutical manufacturing process is developed or scaled-up, or when it is transferred to a different manufacturing site. It may also be conducted during substantial optimization of an existing process or the development of a second-generation process. According to their resulting risk ratings, process parameters can be ranked for importance and important variables for process development, characterization, or validation can be identified. LAY ABSTRACT: Health authorities around the world ask pharmaceutical companies to manage risk during development and manufacturing of pharmaceuticals. The so-called failure mode and effect analysis (FMEA) is an established risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to pharmaceutical processes that use modern biotechnology (biopharmaceutical processes) brings about some difficulties, because those biopharmaceutical processes differ from processes in mechanical and electrical industries. The proposal presented here explains how a biopharmaceutical process FMEA can be conducted. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. With the help of this guideline, different details of the manufacturing process can be ranked according to their potential risks, and this can help pharmaceutical companies to identify aspects with high potential risks and to react accordingly to improve the safety of medicines.

Impact of SLM build parameters on the surface quality

Additive manufacturing by melting of metal powders is an innovative method to create one-offs and customized parts. Branches like dentistry, aerospace engineering and tool making were indicated and the manufacturing methods are established. Besides all the advantages, like freedom of design, manufacturing without a tool and the reduction of time-to-market, there are however some disadvantages, such as reproducibility or the surface quality. The surface quality strongly depends on the orientation of the component in the building chamber, the process parameters which are laser power and exposure time, but also on the so-called “hatch”-strategy, which includes the way the laser exposes the solid areas. This paper deals with the investigation and characterization of the surface quality of generated parts produced by SLM. Main process parameters including part orientation, part size and hatch strategies are investigated and monitored. The outcome is a recommendation of suitable hatch strategies depending on desired part properties. This includes metered values and takes into account process stability and reproducibility.

Chemical kinetic investigation of a commercial batch reactor process

The aim of this investigation was to study the chemical reactions occurring during the batchwise production of a butylated melamine-formaldehyde resin, in order to optimise the efficiency and economics of the batch processes. The batch process models are largely empirical in nature as the reaction mechanism is unknown. The process chemistry and the commercial manufacturing method are described. A small scale system was established in glass and the ability to produce laboratory resins with the required quality was demonstrated, simulating the full scale plant. During further experiments the chemical reactions of methylolation, condensation and butylation were studied. The important process stages were identified and studied separately. The effects of variation of certain process parameters on the chemical reactions were also studied. A published model of methylolation was modified and used to simulate the methylolation stage. A major result of this project was the development of an indirect method for studying the condensation and butylation reactions occurring during the dehydration and acid reaction stages, as direct quantitative methods were not available. A mass balance method was devised for this purpose and used to collect experimental data. The reaction scheme was verified using this data. The reactions stages were simulated using an empirical model. This has revealed new information regarding the mechanism and kinetics of the reactions. Laboratory results were shown to be comparable with plant scale results. This work has improved the understanding of the batch process, which can be used to improve product consistency. Future work has been identified and recommended to produce an optimum process and plant design to reduce the batch time.

A knowledge-based system for process planning in a seamless steel tube plant

The thesis describes the work carried out to develop a prototype knowledge-based system 'KBS-SETUPP' to generate process plans for the manufacture of seamless tubes. The work is specifically related to a plant in which hollows are made from solid billets using a rotary piercing process and then reduced to required size and finished properties using the fixed plug cold drawing process. The thesis first discusses various methods of tube production in order to give a general background of tube manufacture. Then a review of the automation of the process planning function is presented in terms of its basic sub-tasks and the techniques and suitability of a knowledge-based system is established. In the light of such a review and a case study, the process planning problem is formulated in the domain of seamless tube manufacture, its basic sub-tasks are identified and capabilities and constraints of the available equipment in the specific plant are established. The task of collecting and collating the process planning knowledge in seamless tube manufacture is discussed and is mostly fulfilled from domain experts, analysing of existing manufacturing records specific to plant, textbooks and applicable Standards. For the cold drawing mill, tube-drawing schedules have been rationalised to correspond with practice. The validation of such schedules has been achieved by computing the process parameters and then comparing these with the drawbench capacity to avoid over-loading. The existing models cannot be simulated in the computer program as such, therefore a mathematical model has been proposed which estimates the process parameters which are in close agreement with experimental values established by other researchers. To implement the concepts, a Knowledge-Based System 'KBS- SETUPP' has been developed on Personal Computer using Turbo- Prolog. The system is capable of generating process plans, production schedules and some additional capabilities to supplement process planning. The system generated process plans have been compared with the actual plans of the company and it has been shown that the results are satisfactory and encouraging and that the system has the capabilities which are useful.

Improved production of industrially relevant recombinant proteins: application of multi-factorial design of experiments and scalable process modelling

The work described in this thesis focuses on the use of a design-of-experiments approach in a multi-well mini-bioreactor to enable the rapid establishments of high yielding production phase conditions in yeast, which is an increasingly popular host system in both academic and industrial laboratories. Using green fluorescent protein secreted from the yeast, Pichia pastoris, a scalable predictive model of protein yield per cell was derived from 13 sets of conditions each with three factors (temperature, pH and dissolved oxygen) at 3 levels and was directly transferable to a 7 L bioreactor. This was in clear contrast to the situation in shake flasks, where the process parameters cannot be tightly controlled. By further optimisating both the accumulation of cell density in batch and improving the fed-batch induction regime, additional yield improvement was found to be additive to the per cell yield of the model. A separate study also demonstrated that improving biomass improved product yield in a second yeast species, Saccharomyces cerevisiae. Investigations of cell wall hydrophobicity in high cell density P. pastoris cultures indicated that cell wall hydrophobin (protein) compositional changes with growth phase becoming more hydrophobic in log growth than in lag or stationary phases. This is possibly due to an increased occurrence of proteins associated with cell division. Finally, the modelling approach was validated in mammalian cells, showing its flexibility and robustness. In summary, the strategy presented in this thesis has the benefit of reducing process development time in recombinant protein production, directly from bench to bioreactor.

A Non-catalytic Deep Desulphurization Process using Hydrodynamic Cavitation

A novel approach is developed for desulphurization of fuels or organics without use of catalyst. In this process, organic and aqueous phases are mixed in a predefined manner under ambient conditions and passed through a cavitating device. Vapor cavities formed in the cavitating device are then collapsed which generate (in-situ) oxidizing species which react with the sulphur moiety resulting in the removal of sulphur from the organic phase. In this work, vortex diode was used as a cavitating device. Three organic solvents (n-octane, toluene and n-octanol) containing known amount of a model sulphur compound (thiophene) up to initial concentrations of 500 ppm were used to verify the proposed method. A very high removal of sulphur content to the extent of 100% was demonstrated. The nature of organic phase and the ratio of aqueous to organic phase were found to be the most important process parameters. The results were also verified and substantiated using commercial diesel as a solvent. The developed process has great potential for deep of various organics, in general, and for transportation fuels, in particular.

Process Gas Infiltration in Inconel 718 Samples during SLM Processing

Argon infiltration is a well-known problem of hot isostatic pressed components. Thus, the argon content is one quality attribute which is measured after a hot isostatic pressing (HIP) process. Since the Selective Laser Melting (SLM) process takes place under an inert argon atmosphere; it is imaginable that argon is entrapped in the component after SLM processing. Despite using optimized process parameters, defects like pores and shrink holes cannot be completely avoided. Especially, pores could be filled with process gas during the building process. Argon filled pores would clearly affect the mechanical properties. The present paper takes a closer look at the porosity in Inconel 718 samples, which were generated by means of SLM. Furthermore, the argon content of the powder feedstock, of samples made by means of SLM, of samples which were hot isostatic pressed after the SLM process, and of conventionally manufactured samples were measured and compared. The results showed an increased argon content in the Inconel 718 samples after SLM processing compared to conventional manufactured samples.

Optical emission spectroscopy as a process-monitoring tool in PECVD of amorphous carbon coatings

Dans ce projet de recherche, le dépôt des couches minces de carbone amorphe (généralement connu sous le nom de DLC pour Diamond-Like Carbon en anglais) par un procédé de dépôt chimique en phase vapeur assisté par plasma (ou PECVD pour Plasma Enhanced Chemical Vapor deposition en anglais) a été étudié en utilisant la Spectroscopie d’Émission Optique (OES) et l’analyse partielle par régression des moindres carrés (PLSR). L’objectif de ce mémoire est d’établir un modèle statistique pour prévoir les propriétés des revêtements DLC selon les paramètres du procédé de déposition ou selon les données acquises par OES. Deux séries d’analyse PLSR ont été réalisées. La première examine la corrélation entre les paramètres du procédé et les caractéristiques du plasma pour obtenir une meilleure compréhension du processus de dépôt. La deuxième série montre le potentiel de la technique d’OES comme outil de surveillance du procédé et de prédiction des propriétés de la couche déposée. Les résultats montrent que la prédiction des propriétés des revêtements DLC qui était possible jusqu’à maintenant en se basant sur les paramètres du procédé (la pression, la puissance, et le mode du plasma), serait envisageable désormais grâce aux informations obtenues par OES du plasma (particulièrement les indices qui sont reliées aux concentrations des espèces dans le plasma). En effet, les données obtenues par OES peuvent être utilisées pour surveiller directement le processus de dépôt plutôt que faire une étude complète de l’effet des paramètres du processus, ceux-ci étant strictement reliés au réacteur plasma et étant variables d’un laboratoire à l’autre. La perspective de l’application d’un modèle PLSR intégrant les données de l’OES est aussi démontrée dans cette recherche afin d’élaborer et surveiller un dépôt avec une structure graduelle.