The impact of architecture on the performance of artificial neural networks

A number of researchers have investigated the impact of network architecture on the performance of artificial neural networks. Particular attention has been paid to the impact on the performance of the multi-layer perceptron of architectural issues, and the use of various strategies to attain an optimal network structure. However, there are still perceived limitations with the multi-layer perceptron and networks that employ a different architecture to the multi-layer perceptron have gained in popularity in recent years, particularly, networks that implement a more localised solution, where the solution in one area of the problem space does not impact, or has a minimal impact, on other areas of the space. In this study, we discuss the major architectural issues affecting the performance of a multi-layer perceptron, before moving on to examine in detail the performance of a new localised network, namely the bumptree. The work presented here examines the impact on the performance of artificial neural networks of employing alternative networks to the long established multi-layer perceptron. In particular, networks that impose a solution where the impact of each parameter in the final network architecture has a localised impact on the problem space being modelled are examined. The alternatives examined are the radial basis function and bumptree neural networks, and the impact of architectural issues on the performance of these networks is examined. Particular attention is paid to the bumptree, with new techniques for both developing the bumptree structure and employing this structure to classify patterns being examined.

Can surface energy measurements predict the impact of catalyst hydrophobicity upon fatty acid esterification over sulfonic acid functionalised periodic mesoporous organosilicas?

Sulfonic acid functionalised periodic mesoporous organosilicas (PrSO3 H-PMOs) with tunable hydrophobicity were synthesised via a surfactant-templating route, and characterised by porosimetry, TEM, XRD, XPS, inverse gas chromatography (IGC) and ammonia pulse chemisorption. IGC reveals that incorporation of ethyl or benzyl moieties into a mesoporous SBA-15 silica framework significantly increases the non-specific dispersive surface energy of adsorption for alkane adsorption, while decreasing the free energy of adsorption of methanol, reflecting increased surface hydrophobicity. The non-specific dispersive surface energy of adsorption of PMO-SO3H materials is strongly correlated with their activity towards palmitic acid esterification with methanol, demonstrating the power of IGC as an analytical tool for identifying promising solid acid catalysts for the esterification of free fatty acids. A new parameter [-ΔGCNP-P], defined as the per carbon difference in Gibbs free energy of adsorption between alkane and polar probe molecules, provides a simple predictor of surface hydrophobicity and corresponding catalyst activity in fatty acid esterification. © 2014 Elsevier B.V.

Reactor design and its impact on performance and products

This chapter discusses engineering design and performance of various types of biomass transformation reactors. These reactors vary in their operating principle depending on the processing capacity and the nature of the desired end product, that is, gas, chemicals or liquid bio-oil. Mass balance around a thermal conversion reactor is usually carried out to identify the degree of conversion and obtain the amount of the various components in the product. The energy balance around the reactors is essential for determining the optimum reactor temperature and the amount of heat required to complete the overall reactions. Experimental and pilot-plant testing is essential for proper reactor design. However, it is common practice to use correlation and valid parameter values in determining the realistic reactor dimensions and configurations. Despite the recent progress in thermochemical conversion technology, reactor performance and scale up potential are the subjects of continuing research.