The development and optimisation of a fast pyrolysis process for bio-oil production

A two-tier study is presented in this thesis. The first involves the commissioning of an extant but at the time, unproven bubbling fluidised bed fast pyrolysis unit. The unit was designed for an intended nominal throughput of 300 g/h of biomass. The unit came complete with solids separation, pyrolysis vapour quenching and oil collection systems. Modifications were carried out on various sections of the system including the reactor heating, quenching and liquid collection systems. The modifications allowed for fast pyrolysis experiments to be carried out at the appropriate temperatures. Bio-oil was generated using conventional biomass feedstocks including Willow, beechwood, Pine and Miscanthus. Results from this phase of the research showed however, that although the rig was capable of processing biomass to bio-oil, it was characterised by low mass balance closures and recurrent operational problems. The problems included blockages, poor reactor hydrodynamics and reduced organic liquid yields. The less than optimal performance of individual sections, particularly the feed and reactor systems of the rig, culminated in a poor overall performance of the system. The second phase of this research involved the redesign of two key components of the unit. An alternative feeding system was commissioned for the unit. The feed system included an off the shelf gravimetric system for accurate metering and efficient delivery of biomass. Similarly, a new bubbling fluidised bed reactor with an intended nominal throughput of 500g/h of biomass was designed and constructed. The design leveraged on experience from the initial commissioning phase with proven kinetic and hydrodynamic studies. These units were commissioned as part of the optimisation phase of the study. Also as part of this study, two varieties each, of previously unreported feedstocks namely Jatropha curcas and Moringa olifiera oil seed press cakes were characterised to determine their suitability as feedstocks for liquid fuel production via fast pyrolysis. Consequently, the feedstocks were used for the production of pyrolysis liquids. The quality of the pyrolysis liquids from the feedstocks were then investigated via a number of analytical techniques. The oils from the press cakes showed high levels of stability and reduced pH values. The improvements to the design of the fast pyrolysis unit led to higher mass balance closures and increased organic liquid yields. The maximum liquid yield obtained from the press cakes was from African Jatropha press cake at 66 wt% on a dry basis.

An application of decision process modelling to manufacturing system design

As levels of investment in advanced manufacturing systems increase, effective project management becomes ever more critical. This paper demonstrates how the model proposed by Mintzberg, Raisinghani and Theoret in 1976, which structures complicated strategic decision processes, can be applied to the design of new production systems for both descriptive and analytical research purposes. This paper sets a detailed case study concerning the design and development of an advanced manufacturing system within the Mintzberg decision model and so breaks down the decision sequence into constituent parts. It thus shows how a structured model can provide a framework for the researcher who wishes to study decision episodes in the design of manufacturing facilities in greater depth.

Measuring the operational performance of intensive care units using the analytic hierarchy process approach

Purpose - The purpose of this study is to develop a performance measurement model for service operations using the analytic hierarchy process approach. Design/methodology/approach - The study reviews current relevant literature on performance measurement and develops a model for performance measurement. The model is then applied to the intensive care units (ICUs) of three different hospitals in developing nations. Six focus group discussions were undertaken, involving experts from the specific area under investigation, in order to develop an understandable performance measurement model that was both quantitative and hierarchical. Findings - A combination of outcome, structure and process-based factors were used as a foundation for the model. The analyses of the links between them were used to reveal the relative importance of each and their associated sub factors. It was considered to be an effective quantitative tool by the stakeholders. Research limitations/implications - This research only applies the model to ICUs in healthcare services. Practical implications - Performance measurement is an important area within the operations management field. Although numerous models are routinely being deployed both in practice and research, there is always room for improvement. The present study proposes a hierarchical quantitative approach, which considers both subjective and objective performance criteria. Originality/value - This paper develops a hierarchical quantitative model for service performance measurement. It considers success factors with respect to outcomes, structure and processes with the involvement of the concerned stakeholders based upon the analytic hierarchy process approach. The unique model is applied to the ICUs of hospitals in order to demonstrate its effectiveness. The unique application provides a comparative international study of service performance measurement in ICUs of hospitals in three different countries. © Emerald Group Publishing Limited.

Analytical techniques for the study of soft contact lens spoilation

Soft contact lens wear has become a common phenomenon in recent times. The contact lens when placed in the eye rapidly undergoes change. A film of biological material builds up on and in the lens matrix. The long term wear characteristics of the lens ultimately depend on this process. With time distinct structures made up of biological material have been found to build up on the lens. A fuller understanding of this process and how it relates to the lens chemistry could lead to contact lenses that are better tolerated by the eye. The tear film is a complex biological fluid, it is this fluid that bathes the lens during wear. It is reasonable to suppose that it is material derived from this source that accumulates on the lens. To understand this phenomenon it was decided to investigate the make up and conformation of the protein species that are found on and in the lens. As inter individual variations in tear fluid composition have been found it is important to be able to study the proteins on a single lens. Many of the analytical techniques used in bio research are not suitable for this study because of the lack of sensitivity. Work with poly acrylamide electrophoresis showed the possibility of analyzing the proteins extracted from a single lens. The development of a biotin avidin electro-blot and an enzyme linked aniibody electro-blot, lead to the high sensitivity detection and identification of the proteins present. The extraction of proteins from a lens is always incomplete. A method that analyses the proteins in situ would be a great advancement. Fourier transform infra red microscopy was developed to a point where a thin section of a contact lens could yield information about the proteins present and their conformation. The three dimensional structure of the gross macroscopic structures termed white spots was investigated using confocal laser microscopy.

Collaborative alliances and organisational innovation : a study of the innovation process

Despite the voluminous studies written about organisational innovation over the last 30-40 years our understanding of this phenomenon continues to be inconsistent and inconclusive (Wolfe, 1994). An assessment of the theoretical and methodological issues influencing the explanatory utility of many studies has led scholars (e.g. Slappendel, 1996) to re-evaluate the assumptions used to ground studies. Building on these criticisms the current study contributes to the development of an interactive perspective of organisational innovation. This work contributes empirically and theoretically to an improved understanding of the innovation process and the interaction between the realm of action and the mediating effects of pre-existing contingencies i.e. social control, economic exchange and the communicability of knowledge (Scarbrough, 1996). Building on recent advances in institutional theory (see Barley, 1986; 1990; Barley and Tolbert, 1997) and critical theory (Morrow, 1994, Sayer, 1992) the study aims to demonstrate, via longitudinal intensive research, the process through which ideas are translated into reality. This is significant because, despite a growing recognition of the implicit link between the strategic conduct of actors and the institutional realm in organisational analysis, there are few examples that theorise and empirically test these connections. By assessing an under researched example of technology transfer; the government's Teaching Company Scheme (TCS) this project provides a critique of the innovation process that contributes to theory and our appreciation of change in the UK government's premier technology transfer scheme (QR, 1996). Critical moments during the translation of ideas illustrate how elements that are linked to social control, economic exchange and communicability mediate the innovation process. Using analytical categories i.e. contradiction, slippage and dysfunctionality these are assessed in relation to the actions (coping strategies) of programme members over a two-year period. Drawing on Giddens' (1995) notion of the duality of structure this study explores the nature of the relationship between the task environment and institutional environment demonstrating how and why knowledge is both an enabler and barrier to organisational innovation.

An evaluation of task analysis techniques for industrial process control

The concept of a task is fundamental to the discipline of ergonomics. Approaches to the analysis of tasks began in the early 1900's. These approaches have evolved and developed to the present day, when there is a vast array of methods available. Some of these methods are specific to particular contexts or applications, others more general. However, whilst many of these analyses allow tasks to be examined in detail, they do not act as tools to aid the design process or the designer. The present thesis examines the use of task analysis in a process control context, and in particular the use of task analysis to specify operator information and display requirements in such systems. The first part of the thesis examines the theoretical aspect of task analysis and presents a review of the methods, issues and concepts relating to task analysis. A review of over 80 methods of task analysis was carried out to form a basis for the development of a task analysis method to specify operator information requirements in industrial process control contexts. Of the methods reviewed Hierarchical Task Analysis was selected to provide such a basis and developed to meet the criteria outlined for such a method of task analysis. The second section outlines the practical application and evolution of the developed task analysis method. Four case studies were used to examine the method in an empirical context. The case studies represent a range of plant contexts and types, both complex and more simple, batch and continuous and high risk and low risk processes. The theoretical and empirical issues are drawn together and a method developed to provide a task analysis technique to specify operator information requirements and to provide the first stages of a tool to aid the design of VDU displays for process control.

Characterizing the hierarchical structures of bioactive sol-gel silicate glass and hybrid scaffolds for bone regeneration

Bone is the second most widely transplanted tissue after blood. Synthetic alternatives are needed that can reduce the need for transplants and regenerate bone by acting as active temporary templates for bone growth. Bioactive glasses are one of the most promising bone replacement/regeneration materials because they bond to existing bone, are degradable and stimulate new bone growth by the action of their dissolution products on cells. Sol-gel-derived bioactive glasses can be foamed to produce interconnected macropores suitable for tissue ingrowth, particularly cell migration and vascularization and cell penetration. The scaffolds fulfil many of the criteria of an ideal synthetic bone graft, but are not suitable for all bone defect sites because they are brittle. One strategy for improving toughness of the scaffolds without losing their other beneficial properties is to synthesize inorganic/organic hybrids. These hybrids have polymers introduced into the sol-gel process so that the organic and inorganic components interact at the molecular level, providing control over mechanical properties and degradation rates. However, a full understanding of how each feature or property of the glass and hybrid scaffolds affects cellular response is needed to optimize the materials and ensure long-term success and clinical products. This review focuses on the techniques that have been developed for characterizing the hierarchical structures of sol-gel glasses and hybrids, from atomicscale amorphous networks, through the covalent bonding between components in hybrids and nanoporosity, to quantifying open macroporous networks of the scaffolds. Methods for non-destructive in situ monitoring of degradation and bioactivity mechanisms of the materials are also included. © 2012 The Royal Society.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Clustering web documents using hierarchical representation with multi-granularity

Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with “false correlation”. In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a twophase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problemresulted from the sparse term-paragraphmatrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerancerough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score.

Intermediate pyrolysis studies of aquatic biomass and potential applications in the BtVB-process

Aquatic biomass is seen as one of the major feedstocks to overcome difficulties associated with 1st generation biofuels, such as competition with food production, change of land use and further environmental issues. Although, this finding is widely accepted only little work has been carried out to investigate thermo-chemical conversion of algal specimen to produce biofuels, power and heat. This work aims at contributing fundamental knowledge for thermo-chemical processing of aquatic biomass via intermediate pyrolysis. Therefore, it was necessary to install and commission an analytical pyrolysis apparatus which facilitates intermediate pyrolysis process conditions as well as subsequent separation and detection of pyrolysates (Py- GC/MS). In addition, a methodology was established to analyse aquatic biomass under intermediate conditions by Thermo-Gravimetric Analysis (TGA). Several microalgae (e.g. Chlamydomonas reinhardtii, Chlorella vulgaris) and macroalgae specimen (e.g. Fucus vesiculosus) from main algal divisions and various natural habitats (fresh and saline water, temperate and polar climates) were chosen and their thermal degradation under intermediate pyrolysis conditions was studied. In addition, it was of interest to examine the contribution of biochemical constituents of algal biomass onto the chemical compounds contained in pyrolysates. Therefore, lipid and protein fractions were extracted from microalgae biomass and analysed separately. Furthermore, investigations of residual algal materials obtained by extraction of high valuable compounds (e.g. lipids, proteins, enzymes) were included to evaluate their potential for intermediate pyrolysis processing. On basis of these thermal degradation studies, possible applications of algal biomass and from there derived materials in the Bio-thermal Valorisation of Biomass-process (BtVB-process) are presented. It was of interest to evaluate the combination of the production of high valuable products and bioenergy generation derived by micro- and macro algal biomass.

Using process-oriented holonic (PrOH) modelling to increase understanding of information systems

Methodologies for understanding business processes and their information systems (IS) are often criticized, either for being too imprecise and philosophical (a criticism often levied at softer methodologies) or too hierarchical and mechanistic (levied at harder methodologies). The process-oriented holonic modelling methodology combines aspects of softer and harder approaches to aid modellers in designing business processes and associated IS. The methodology uses holistic thinking and a construct known as the holon to build process descriptions into a set of models known as a holarchy. This paper describes the methodology through an action research case study based in a large design and manufacturing organization. The scientific contribution is a methodology for analysing business processes in environments that are characterized by high complexity, low volume and high variety where there are minimal repeated learning opportunities, such as large IS development projects. The practical deliverables from the project gave IS and business process improvements for the case study company.

Green supply chain performance measurement using the analytic hierarchy process:a comparative analysis of manufacturing organisations

The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations’ GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain. © 2013 Copyright Taylor and Francis Group, LLC. CORRIGENDUM DOI 10.1080/09537287.2012.751186 In the article ‘Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations’ by Prasanta Kumar Dey and Walid Cheffi, Production Planning & Control, 10.1080/09537287.2012.666859, a third author is added which was not included in the paper as it originally appeared. The third author is Breno Nunes.

Multi-level visualisation using Gaussian process latent variable models

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.