Properties of lignin and poly(hydroxybutyrate) blends

The Queensland University of Technology (QUT) allows the presentation of a thesis for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of Seven published/submitted papers and one poster presentation, of which five have been published and the other two are under review. This project is financially supported by the QUTPRA Grant. The twenty-first century started with the resurrection of lignocellulosic biomass as a potential substitute for petrochemicals. Petrochemicals, which enjoyed the sustainable economic growth during the past century, have begun to reach or have reached their peak. The world energy situation is complicated by political uncertainty and by the environmental impact associated with petrochemical import and usage. In particular, greenhouse gasses and toxic emissions produced by petrochemicals have been implicated as a significant cause of climate changes. Lignocellulosic biomass (e.g. sugarcane biomass and bagasse), which potentially enjoys a more abundant, widely distributed, and cost-effective resource base, can play an indispensible role in the paradigm transition from fossil-based to carbohydrate-based economy. Poly(3-hydroxybutyrate), PHB has attracted much commercial interest as a plastic and biodegradable material because some its physical properties are similar to those of polypropylene (PP), even though the two polymers have quite different chemical structures. PHB exhibits a high degree of crystallinity, has a high melting point of approximately 180°C, and most importantly, unlike PP, PHB is rapidly biodegradable. Two major factors which currently inhibit the widespread use of PHB are its high cost and poor mechanical properties. The production costs of PHB are significantly higher than for plastics produced from petrochemical resources (e.g. PP costs $US1 kg-1, whereas PHB costs $US8 kg-1), and its stiff and brittle nature makes processing difficult and impedes its ability to handle high impact. Lignin, together with cellulose and hemicellulose, are the three main components of every lignocellulosic biomass. It is a natural polymer occurring in the plant cell wall. Lignin, after cellulose, is the most abundant polymer in nature. It is extracted mainly as a by-product in the pulp and paper industry. Although, traditionally lignin is burnt in industry for energy, it has a lot of value-add properties. Lignin, which to date has not been exploited, is an amorphous polymer with hydrophobic behaviour. These make it a good candidate for blending with PHB and technically, blending can be a viable solution for price and reduction and enhance production properties. Theoretically, lignin and PHB affect the physiochemical properties of each other when they become miscible in a composite. A comprehensive study on structural, thermal, rheological and environmental properties of lignin/PHB blends together with neat lignin and PHB is the targeted scope of this thesis. An introduction to this research, including a description of the research problem, a literature review and an account of the research progress linking the research papers is presented in Chapter 1. In this research, lignin was obtained from bagasse through extraction with sodium hydroxide. A novel two-step pH precipitation procedure was used to recover soda lignin with the purity of 96.3 wt% from the black liquor (i.e. the spent sodium hydroxide solution). The precipitation process is presented in Chapter 2. A sequential solvent extraction process was used to fractionate the soda lignin into three fractions. These fractions, together with the soda lignin, were characterised to determine elemental composition, purity, carbohydrate content, molecular weight, and functional group content. The thermal properties of the lignins were also determined. The results are presented and discussed in Chapter 2. On the basis of the type and quantity of functional groups, attempts were made to identify potential applications for each of the individual lignins. As an addendum to the general section on the development of composite materials of lignin, which includes Chapters 1 and 2, studies on the kinetics of bagasse thermal degradation are presented in Appendix 1. The work showed that distinct stages of mass losses depend on residual sucrose. As the development of value-added products from lignin will improve the economics of cellulosic ethanol, a review on lignin applications, which included lignin/PHB composites, is presented in Appendix 2. Chapters 3, 4 and 5 are dedicated to investigations of the properties of soda lignin/PHB composites. Chapter 3 reports on the thermal stability and miscibility of the blends. Although the addition of soda lignin shifts the onset of PHB decomposition to lower temperatures, the lignin/PHB blends are thermally more stable over a wider temperature range. The results from the thermal study also indicated that blends containing up to 40 wt% soda lignin were miscible. The Tg data for these blends fitted nicely to the Gordon-Taylor and Kwei models. Fourier transform infrared spectroscopy (FT-IR) evaluation showed that the miscibility of the blends was because of specific hydrogen bonding (and similar interactions) between reactive phenolic hydroxyl groups of lignin and the carbonyl group of PHB. The thermophysical and rheological properties of soda lignin/PHB blends are presented in Chapter 4. In this chapter, the kinetics of thermal degradation of the blends is studied using thermogravimetric analysis (TGA). This preliminary investigation is limited to the processing temperature of blend manufacturing. Of significance in the study, is the drop in the apparent energy of activation, Ea from 112 kJmol-1 for pure PHB to half that value for blends. This means that the addition of lignin to PHB reduces the thermal stability of PHB, and that the comparative reduced weight loss observed in the TGA data is associated with the slower rate of lignin degradation in the composite. The Tg of PHB, as well as its melting temperature, melting enthalpy, crystallinity and melting point decrease with increase in lignin content. Results from the rheological investigation showed that at low lignin content (.30 wt%), lignin acts as a plasticiser for PHB, while at high lignin content it acts as a filler. Chapter 5 is dedicated to the environmental study of soda lignin/PHB blends. The biodegradability of lignin/PHB blends is compared to that of PHB using the standard soil burial test. To obtain acceptable biodegradation data, samples were buried for 12 months under controlled conditions. Gravimetric analysis, TGA, optical microscopy, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), FT-IR, and X-ray photoelectron spectroscopy (XPS) were used in the study. The results clearly demonstrated that lignin retards the biodegradation of PHB, and that the miscible blends were more resistant to degradation compared to the immiscible blends. To obtain an understanding between the structure of lignin and the properties of the blends, a methanol-soluble lignin, which contains 3× less phenolic hydroxyl group that its parent soda lignin used in preparing blends for the work reported in Chapters 3 and 4, was blended with PHB and the properties of the blends investigated. The results are reported in Chapter 6. At up to 40 wt% methanolsoluble lignin, the experimental data fitted the Gordon-Taylor and Kwei models, similar to the results obtained soda lignin-based blends. However, the values obtained for the interactive parameters for the methanol-soluble lignin blends were slightly lower than the blends obtained with soda lignin indicating weaker association between methanol-soluble lignin and PHB. FT-IR data confirmed that hydrogen bonding is the main interactive force between the reactive functional groups of lignin and the carbonyl group of PHB. In summary, the structural differences existing between the two lignins did not manifest itself in the properties of their blends.

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method.

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

Sequential Fusion Using Correlated Decisions for Controlled Verification Errors

Fusion techniques have received considerable attention for achieving lower error rates with biometrics. A fused classifier architecture based on sequential integration of multi-instance and multi-sample fusion schemes allows controlled trade-off between false alarms and false rejects. Expressions for each type of error for the fused system have previously been derived for the case of statistically independent classifier decisions. It is shown in this paper that the performance of this architecture can be improved by modelling the correlation between classifier decisions. Correlation modelling also enables better tuning of fusion model parameters, ‘N’, the number of classifiers and ‘M’, the number of attempts/samples, and facilitates the determination of error bounds for false rejects and false accepts for each specific user. Error trade-off performance of the architecture is evaluated using HMM based speaker verification on utterances of individual digits. Results show that performance is improved for the case of favourable correlated decisions. The architecture investigated here is directly applicable to speaker verification from spoken digit strings such as credit card numbers in telephone or voice over internet protocol based applications. It is also applicable to other biometric modalities such as finger prints and handwriting samples.

Sequential fusion of decisions from adaptive and random samples for controlled verification of errors

Fusion techniques have received considerable attention for achieving performance improvement with biometrics. While a multi-sample fusion architecture reduces false rejects, it also increases false accepts. This impact on performance also depends on the nature of subsequent attempts, i.e., random or adaptive. Expressions for error rates are presented and experimentally evaluated in this work by considering the multi-sample fusion architecture for text-dependent speaker verification using HMM based digit dependent speaker models. Analysis incorporating correlation modeling demonstrates that the use of adaptive samples improves overall fusion performance compared to randomly repeated samples. For a text dependent speaker verification system using digit strings, sequential decision fusion of seven instances with three random samples is shown to reduce the overall error of the verification system by 26% which can be further reduced by 6% for adaptive samples. This analysis novel in its treatment of random and adaptive multiple presentations within a sequential fused decision architecture, is also applicable to other biometric modalities such as finger prints and handwriting samples.

Sequential fusion of decisions with favorable dependence for controlled verification errors

Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of 'n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ 'AND' and 'OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.

An enhanced mill extraction model

An enhanced mill extraction model has been developed to calculate mill performance parameters and to predict the extraction performance of a milling unit. The model takes into account the fibre suspended in juice streams and calculates filling ratio, reabsorption factor, imbibition coefficient, and separation efficiency using more complete definitions than those used in previous extraction models. A mass balance model is used to determine the fibre, brix and moisture mass flows between milling units so that a complete milling train, including the return stream from the juice screen, is modelled. Model solutions are presented to determine the effect of different levels of fibre in juice and efficiency of fibre separation in the juice screen on brix extraction. The model provides more accurate results than earlier models leading to better understanding and improvement of the milling process.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Robust facial feature extraction and matching

Automated feature extraction and correspondence determination is an extremely important problem in the face recognition community as it often forms the foundation of the normalisation and database construction phases of many recognition and verification systems. This paper presents a completely automatic feature extraction system based upon a modified volume descriptor. These features form a stable descriptor for faces and are utilised in a reversible jump Markov chain Monte Carlo correspondence algorithm to automatically determine correspondences which exist between faces. The developed system is invariant to changes in pose and occlusion and results indicate that it is also robust to minor face deformations which may be present with variations in expression.

Real-time power line extraction from unmanned aerial system video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.