Essential scientific mapping of the value chain of thermochemical converted second-generation bio-fuels

As the largest contributor to renewable energy, biomass (especially lignocellulosic biomass) has significant potential to address atmospheric emission and energy shortage issues. The bio-fuels derived from lignocellulosic biomass are popularly referred to as second-generation bio-fuels. To date, several thermochemical conversion pathways for the production of second-generation bio-fuels have shown commercial promise; however, most of these remain at various pre-commercial stages. In view of their imminent commercialization, it is important to conduct a profound and comprehensive comparison of these production techniques. Accordingly, the scope of this review is to fill this essential knowledge gap by mapping the entire value chain of second-generation bio-fuels, from technical, economic, and environmental perspectives. This value chain covers i) the thermochemical technologies used to convert solid biomass feedstock into easier-to-handle intermediates, such as bio-oil, syngas, methanol, and Fischer-Tropsch fuel; and ii) the upgrading technologies used to convert intermediates into end products, including diesel, gasoline, renewable jet fuels, hydrogen, char, olefins, and oxygenated compounds. This review also provides an economic and commercial assessment of these technologies, with the aim of identifying the most adaptable technology for the production of bio-fuels, fuel additives, and bio-chemicals. A detailed mapping of the carbon footprints of the various thermochemical routes to second-generation bio-fuels is also carried out. The review concludes by identifying key challenges and future trends for second-generation petroleum substitute bio-fuels.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

CLLU1 expression has prognostic value in chronic lymphocytic leukemia after first-line therapy in younger patients and in those with mutated IGHV genes.

CLLU1, located at chromosome 12q22, encodes a transcript specific to chronic lymphocytic leukemia and has potential prognostic value. We assessed the value of CLLU1 expression in the LRF CLL4 randomized trial. Samples from 515 patients with chronic lymphocytic leukemia were collected immediately before the start of treatment. After RNA extraction and cDNA synthesis, CLLU1 expression was assessed by quantitative polymerase chain reaction. In total, 247 and 268 samples were identified as having low and high CLLU1 expression, respectively. The median follow-up was 88 months. High CLLU1 expression was significantly correlated with unmutated IGHV genes, ZAP-70 and CD38 positivity, and absence of 13q deletion (all r>0.2, P

A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value.

To obtain a comprehensive genomic profile of presenting multiple myeloma cases we performed high-resolution single nucleotide polymorphism mapping array analysis in 114 samples alongside 258 samples analyzed by U133 Plus 2.0 expression array (Affymetrix). We examined DNA copy number alterations and loss of heterozygosity (LOH) to define the spectrum of minimally deleted regions in which relevant genes of interest can be found. The most frequent deletions are located at 1p (30%), 6q (33%), 8p (25%), 12p (15%), 13q (59%), 14q (39%), 16q (35%), 17p (7%), 20 (12%), and 22 (18%). In addition, copy number-neutral LOH, or uniparental disomy, was also prevalent on 1q (8%), 16q (9%), and X (20%), and was associated with regions of gain and loss. Based on fluorescence in situ hybridization and expression quartile analysis, genes of prognostic importance were found to be located at 1p (FAF1, CDKN2C), 1q (ANP32E), and 17p (TP53). In addition, we identified common homozygously deleted genes that have functions relevant to myeloma biology. Taken together, these analyses indicate that the crucial pathways in myeloma pathogenesis include the nuclear factor-κB pathway, apoptosis, cell-cycle regulation, Wnt signaling, and histone modifications. This study was registered at http://isrctn.org as ISRCTN68454111.