Carl Duchesne

Multivariate analysis and optimization of process variable trajectories for batch processes

Abstract A new methodology for analyzing batch and semi-batch process variable trajectories is proposed in this paper for process development and optimization. It is aimed at identifying trajectory features such as cumulative effects and time-specific effects of process variables on final product quality. A new pathway multi-block PLS algorithm, valid under the assumption of linear and additive effects, is proposed to efficiently incorporate information provided by intermediate quality measurements, which help in identifying time-specific effects. Extraction of trajectory features is illustrated using designed experiments on a fundamental simulation model for SBR emulsion copolymerization. The methodology is shown to provide information useful for improving final product quality through trajectory modifications. It is also shown that intermediate quality measurements can significantly reduce the number of batch runs necessary for feature extraction (than when only final product quality is available).

Jackknife and bootstrap methods in the identification of dynamic models

Abstract A new criterion based on a Jackknife or a Bootstrap statistic is proposed for identifying non-parsimonious dynamic models (FIR, ARX). It is applicable for selecting the number of components in latent variable regression methods or the constraining parameter in regularized least squares regression methods. These meta parameters are used to overcome ill-conditioning caused by model over-parameterization, when fitted using prediction error or least squares methods. In all cases studied, using PLS for parameter estimation, the proposed criterion led to the selection of better models, in the mean square error sense, than when selected via cross-validation. The methodology also provides approximate confidence intervals for the model parameters and the step and impulse response of the system.

Establishing multivariate specification regions for incoming materials

We present a methodology for establishing meaningful multivariate specification regions on incoming materials or components. The approach uses multivariate PLS regression models to extract information from databases and to relate the properties of incoming materials, and the process variables, to the quality measures on the customers product. The specification regions are multivariate in nature and are defined in the latent variable space of the PLS model. However, they are easy to implement in practice, and they can provide much insight into what constitutes acceptable material. The approach is illustrated using a simulated process, and is then used to establish multivariate specification regions for polymer resin properties in an industrial film process.

A hyperspectral imaging sensor for on-line quality control of extruded polymer composite products

Abstract This study examines the ability of chemometrics methods, namely multivariate image analysis (MIA) and Grey Level Co-occurrence Matrix analysis (GLCM), to extract meaningful information from visible and near-infrared spectral images of extruded wood/plastic composite materials for predicting spatio-temporal variations in their properties. The samples were produced under varying process and feed conditions according to designed experiments. Mechanical properties of the samples were measured using standard analytical methods both during steady-state and dynamic transition periods. A Bootstrap-PLS regression technique was first used for selecting the spectral bands (i.e. wavelengths) that were the most highly correlated with the material properties. In a second step, a more parsimonious PLS regression model was built between the spectral and textural features extracted from the lower dimensional spectral images and the corresponding quality properties of each sample. The imaging sensor was able to simultaneously monitor 7 properties in both steady-state operation and during transitions.

A fluorophore-tagged RGD peptide to control endothelial cell adhesion to micropatterned surfaces.

The long-term patency rates of vascular grafts and stents are limited by the lack of surface endothelialisation of the implanted materials. We have previously reported that GRGDS and WQPPRARI peptide micropatterns increase the endothelialisation of prosthetic materials in vitro. To investigate the mechanisms by which the peptide micropatterns affect endothelial cell adhesion and proliferation, a TAMRA fluorophore-tagged RGD peptide was designed. Live cell imaging revealed that the micropatterned surfaces led to directional cell spreading dependent on the location of the RGD-TAMRA spots. Focal adhesions formed within 3 h on the micropatterned surfaces near RGD-TAMRA spot edges, as expected for cell regions experiencing high tension. Similar levels of focal adhesion kinase phosphorylation were observed after 3 h on the micropatterned surfaces and on surfaces treated with RGD-TAMRA alone, suggesting that partial RGD surface coverage is sufficient to elicit integrin signaling. Lastly, endothelial cell expansion was achieved in serum-free conditions on gelatin-coated, RGD-TAMRA treated or micropatterned surfaces. These results show that these peptide micropatterns mainly impacted cell adhesion kinetics rather than cell proliferation. This insight will be useful for the optimization of micropatterning strategies to improve vascular biomaterials.

Polyploid megakaryocytes can complete cytokinesis.

Megakaryocytes (MK) undergo polyploidization through endomitosis, a mitotic process that ends prematurely due to aborted cytokinesis. To better understand this and other events associated with MK differentiation, we performed long-term and large-field live cell imaging of human MKs derived in cord blood (CB) and bone marrow (BM) CD34+ cell cultures. Polyploid level of imaged cells was evaluated using three complementary approaches; cell history, cell size and ploidy correlation and nuclei staining. This system and strategy enabled the direct observation of the development of a large number of MKs (n=4865) and to quantify their fates. The most significant finding of this study is that a considerable proportion of polyploid MKs could complete cytokinesis. This unexpected process gave rise to polyploid daughter cell(s) with normal fates and contributed significantly to the expansion of polyploid MKs. Further analyses revealed that the proliferation rate amongst polyploid MKs was inversely correlated to their ploidy level, and that this phenomenon was much more frequent in CB- than BM-derived MKs. Accordingly, endomitosis was identified as the dominant fate of polyploid BM-MKs, while this was less accentuated for polyploid CB-MKs. These findings explain partially why CB-derived MKs remain in lower ploidy class. In conclusion, this study demonstrates that the development of polyploid MK results from the failure and/or success of cytokinesis and brings a new paradigm to the field of megakaryopoiesis.

Qualitative representation of trends: an alternative approach to process diagnosis and control

The potential for qualitative representation of trends in the context of process diagnosis and control is evaluated in this paper. The technique for qualitative description of the data series is relatively new to the field of process monitoring and diagnosis and is based on the cubic spline wavelet decomposition of the data. It is shown that the assessed qualitative description of trends can be coupled easily with existing process knowledge and does not demand the user to understand the underlying technique in detail, in contrast to, for instance, multivariate techniques in Statistical Process Control. The assessed links can be integrated straightforwardly into the framework of supervisory control systems by means of look-up tables, expert systems or case-based reasoning frameworks. This in turn allows the design of a supervisory control system leading to fully automated control actions. The technique is illustrated by an application to a pilot-scale SBR.

Mechanical, water absorption, and aging properties of polypropylene/flax/glass fiber hybrid composites

This work investigates the mechanical and aging properties of flax/glass fibers reinforced polypropylene hybrid composites. The mechanical properties as a function of reinforcement content show that adding glass fiber to polypropylene/flax composites improves tensile modulus and strength as well as impact resistance and hardness, but has negligible effect on strain at yield and elongation at break. Water uptake at 85℃ and variations in mechanical behavior are determined after water, thermal, and accelerated UV aging tests. The results indicate that glass fibers enhance water resistance of polypropylene/flax composites. Thermal aging at 85℃ reveals that irrespective of filler type and content the composites are thermally resistant. According to accelerated UV aging tests, the presence of glass fiber accelerates the degradation of the polypropylene matrix, but flax fiber can protect the composites. Finally, a partial least-squares model is built to correlate the composite composition to the properties of aged and unaged specimens.

Selection and tuning of a fast and simple phase-contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner.

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

Dynamics And Assessment of Some Control Strategies of a Simulated Industrial Rotary Dryer

ABSTRACT Rotary dryers are widely used for the continuous drying of minerals and chemicals on a large scale. Hot gases are passed parallel to the flowing solid to achieve the desired product moisture content. Because these dryers are energy intensive, it is mandatory to operate them as efficiently as possible to respond to economic pressures. Using a dynamic rotary dryer simulator for mineral concentrate, five control strategies are evaluated and compared. Two control strategies are based on PI controllers and the others use neural network models. Results clearly show that a feedforward action, in conjunction with a PI controller or incorporated within the structure of a neural network model, led to the best performances provided an accurate measurement of the feed moisture content is available.