Fast pyrolysis of biomass in a circulating fluidised bed reactor

The objective of this work was to design, construct, test and operate a novel circulating fluid bed fast pyrolysis reactor system for production of liquids from biomass. The novelty lies in incorporating an integral char combustor to provide autothermal operation. A reactor design methodology was devised which correlated input parameters to process variables, namely temperature, heat transfer and gas/vapour residence time, for both the char combustor and biomass pyrolyser. From this methodology a CFB reactor was designed with integral char combustion for 10 kg/h biomass throughput. A full-scale cold model of the CFB unit was constructed and tested to derive suitable hydrodynamic relationships and performance constraints. Early difficulties encountered with poor solids circulation and inefficient product recovery were overcome by a series of modifications. A total of 11 runs in a pyrolysis mode were carried out with a maximum total liquids yield of 61.50% wt on a maf biomass basis, obtained at 500°C and with 0.46 s gas/vapour residence time. This could be improved by improved vapour recovery by direct quenching up to an anticipated 75 % wt on a moisture-and-ash-free biomass basis. The reactor provides a very high specific throughput of 1.12 - 1.48 kg/hm2 and the lowest gas-to-feed ratio of 1.3 - 1.9 kg gas/kg feed compared to other fast pyrolysis processes based on pneumatic reactors and has a good scale-up potential. These features should provide significant capital cost reduction. Results to date suggest that the process is limited by the extent of char combustion. Future work will address resizing of the char combustor to increase overall system capacity, improvement in solid separation and substantially better liquid recovery. Extended testing will provide better evaluation of steady state operation and provide data for process simulation and reactor modeling.

The variant rpoS allele of S. enteritidis strain 27655R does not affect virulence in a chick model nor constitutive curliation but does generate a cold-sensitive phenotype

Adherence of pathogenic Escherichia coli and Salmonella spp. to host cells is in part mediated by curli fimbriae which, along with other virulence determinants, are positively regulated by RpoS. Interested in the role and regulation of curli (SEF17) fimbriae of Salmonella enteritidis in poultry infection, we tested the virulence of naturally occurring S. enteritidis PT4 strains 27655R and 27655S which displayed constitutive and null expression of curli (SEF17) fimbriae, respectively, in a chick invasion assay and analysed their rpoS alleles. Both strains were shown to be equally invasive and as invasive as a wild-type phage type 4 strain and an isogenic derivative defective for the elaboration of curli. We showed that the rpoS allele of 27655S was intact even though this strain was non-curliated and we confirmed that a S. enteritidis rpoS::strr null mutant was unable to express curli, as anticipated. Strain 27655R, constitutively curliated, possessed a frameshift mutation at position 697 of the rpoS coding sequence which resulted in a truncated product and remained curliated even when transduced to rpoS::strr. Additionally, rpoS mutants are known to be cold-sensitive, a phenotype confirmed for strain 27655R. Collectively, these data indicated that curliation was not a significant factor for pathogenesis of S. enteritidis in this model and that curliation of strains 27655R and 27655S was independent of RpoS. Significantly, strain 27655R possessed a defective rpoS allele and remained virulent. Here was evidence that supported the concept that different naturally occurring rpoS alleles may generate varying virulence phenotypic traits.

Failure analysis of cold-formed steel sections

The purlin-sheeting system has been the subject of numerous theoretical and experimental investigations over the past 30 years, but the complexity of the problem has led to great difficulty in developing a sound and general model. The primary aim of the thesis is to investigate the failure behaviours of cold-formed zed and channel sections for use in purlin-sheeting systems. Both the energy method and finite strip method are used to develop an approach to investigate cold-formed zed and channel section beams with partial-lateral restraint from the metal sheeting when subjected to a uniformly distributed transverse load. The stress analysis of cold-formed zed and channel section beams with partially-lateral restraint from the metal sheeting when subjected to a uniformly distributed transverse load is investigated firstly by using the analytical model based on the energy method in which the restraint actions of the sheeting are modelled by using two springs representing the translational and rotational restraints. The numerical results have showed that the two springs have significantly different influences on the stresses of the beams. The influence of the two springs has also been found to depend on the anti-sag bar and the position of the loading line. A novel method is presented for analysing the elastic local buckling behaviour of cold-formed zed and channel section beams with partial-lateral restraint from metal sheeting when subjected to a uniformly distributed transverse load, which is carried out by inputting the cross sectional stresses with the largest compressive stress into the finite strip analysis. By using the presented novel method, individual influences of warning stress, partially lateral restraints from the sheeting and the dimensions of the cross section and position of the loading line on the buckling behaviour are investigated.

Global optimisation of cold-formed steel sections

In this thesis, standard algorithms are used to carry out the optimisation of cold-formed steel purlins such as zed, channel and sigma sections, which are assumed to be simply supported and subjected to a gravity load. For zed, channel and sigma section, the local buckling, distortional buckling and lateral-torsional buckling are considered respectively herein. Currently, the local buckling is based on the BS 5950-5:1998 and EN 1993-1-3:2006. The distortional buckling is calculated by the direct strength method employing the elastic distortional buckling which is calculated by three available approaches such as Hancock (1995), Schafer and Pekoz (1998), Yu (2005). In the optimisation program, the lateral-torsional buckling based on BS 5950-5:1998, AISI and analytical model of Li (2004) are investigated. For the optimisation program, the programming codes are written for optimisation of channel, zed and sigma beam. The full study has been coded into a computer-based analysis program (MATLAB).

Computer simulation for tube-making by the cold roll-forming process

The conventional design of forming rolls depends heavily on the individual skill of roll designers which is based on intuition and knowledge gained from previous work. Roll design is normally a trial an error procedure, however with the progress of computer technology, CAD/CAM systems for the cold roll-forming industry have been developed. Generally, however, these CAD systems can only provide a flower pattern based on the knowledge obtained from previously successful flower patterns. In the production of ERW (Electric Resistance Welded) tube and pipe, the need for a theoretical simulation of the roll-forming process, which can not only predict the occurrence of the edge buckling but also obtain the optimum forming condition, has been recognised. A new simulation system named "CADFORM" has been devised that can carry out the consistent forming simulation for this tube-making process. The CADFORM system applied an elastic-plastic stress-strain analysis and evaluate edge buckling by using a simplified model of the forming process. The results can also be visualised graphically. The calculated longitudinal strain is obtained by considering the deformation of lateral elements and takes into account the reduction in strains due to the fin-pass roll. These calculated strains correspond quite well with the experimental results. Using the calculated strains, the stresses in the strip can be estimated. The addition of the fin-pass roll reduction significantly reduces the longitudinal compressive stress and therefore effectively suppresses edge buckling. If the calculated longitudinal stress is controlled, by altering the forming flower pattern so it does not exceed the buckling stress within the material, then the occurrence of edge buckling can be avoided. CADFORM predicts the occurrence of edge buckling of the strip in tube-making and uses this information to suggest an appropriate flower pattern and forming conditions which will suppress the occurrence of the edge buckling.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.