Enhancing process insight in polymer extrusion by grey box modelling

Due to the complexity and inherent instability in polymer extrusion there is a need for process models which can be run on-line to optimise settings and control disturbances. First-principle models demand computationally intensive solution, while ‘black box’ models lack generalisation ability and physical process insight. This work examines a novel ‘grey box’ modelling technique which incorporates both prior physical knowledge and empirical data in generating intuitive models of the process. The models can be related to the underlying physical mechanisms in the extruder and have been shown to capture unpredictable effects of the operating conditions on process instability. Furthermore, model parameters can be related to material properties available from laboratory analysis and as such, lend themselves to re-tuning for different materials without extensive remodelling work.

Prising open the black box: Critical realism, action research and social work

There is a growing interest in critical realism and its application to social work. This article makes a case for adopting this philosophical position in qualitative social work research. More specifically, it suggests that there is a concordance between critical realist premises and action research with its cyclical inquiry and advancement of social change. This combination of philosophy and method, it is argued, promotes anti-oppressive social work research and illuminates the processes shaping outcomes in programme evaluations. Overall, the article underscores the importance of 'depth' in qualitative inquiry by conceiving the social world in terms of five interlacing, social domains.

T-box 2 represses NDRG1 through an EGR1-dependent mechanism to drive the proliferation of breast cancer cells

T-box 2 (TBX2) is a transcription factor involved in mammary development and is known to be overexpressed in a subset of aggressive breast cancers. TBX2 has previously been shown to repress growth control genes such as p14(ARF) and p21(WAF1/cip1). In this study we show that TBX2 drives proliferation in breast cancer cells and this is abrogated after TBX2 small interfering RNA (siRNA) knockdown or after the expression of a dominant-negative TBX2 protein. Using microarray analysis we identified a large cohort of novel TBX2-repressed target genes including the breast tumour suppressor NDRG1 (N-myc downregulated gene 1). We show that TBX2 targets NDRG1 through a previously undescribed mechanism involving the recruitment of early growth response 1 (EGR1). We show EGR1 is required for the ability of TBX2 to repress NDRG1 and drive cell proliferation. We show that TBX2 interacts with EGR1 and that TBX2 requires EGR1 to target the NDRG1 proximal promoter. Abrogation of either TBX2 or EGR1 expression is accompanied by the upregulation of cell senescence and apoptotic markers. NDRG1 can recapitulate these effects when transfected into TBX2-expressing cells. Together, these data identify a novel mechanism for TBX2-driven oncogenesis and highlight the importance of NDRG1 as a growth control gene in breast tissue. Oncogene (2010) 29, 3252-3262; doi: 10.1038/onc.2010.84; published online 29 March 2010

Dynamic grey-box modeling for online monitoring of extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple-fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers