Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well.

Polystyrene (PS) is an important commodity polymer. In its most commonly used form, PS is a high molecular weight linear polymer, typically produced through free-radical polymerization, which is a well understood and robust process. This process produces a high molecular weight, clear thermoplastic that is hard, rigid and has good thermal and melt flow properties for use in moldings, extrusions and films. However, polystyrene produced through the free radical process has a very broad molecular weight distribution, which can lead to poor performance in some applications. To this end, nitroxide-mediated radical polymerization (NMRP) can synthesize materials with a much more consistently defined molecular architecture as well as relatively low polydispersity than other methods. NMRP involves radical polymerization in the presence of a nitroxide mediator. This mediator is usually of the form of a stable radical which can bind to and disable the growing polymer chain. This will “tie up” some of the free radicals forming a dynamic equilibrium between active and dormant species, through a reversible coupling process. NMRP can be conducted through one of two different processes: (1) The bimolecular process, which can be initiated with a conventional peroxide initiator (i.e. BPO) but in the presence of a stable nitroxide radical (i.e. TEMPO), which is a stable radical that can reversibly bind with the growing polymer radical chain, and (2) The unimolecular process, where nitroxyl ether is introduced to the system, which then degrades to create both the initiator and mediator radicals. Based on previous research in the group, which included experimental investigations with both unimolecular and bimolecular NMRP under various conditions, it was possible to build on an earlier model and come up with an improved detailed mechanistic model. Additionally, it was seen that certain parameters in the model had little impact on the overall model performance, which suggested that their removal would be appropriate, also serving to reduce the complexity of the model. Comparisons of model predictions with experimental data both from within the group and the general literature were performed and trends verified. Further work was done on the development of an additionally reduced model, and on the testing of these different levels of model complexity with data. The aim of this analysis was to develop a model to capture the key process responses in a simple and easy to implement manner with comparable accuracy to the complete models. Due to its lower complexity, this substantially reduced model would me a much likelier candidate for use in on-line applications. Application of these different model levels to the model-based D-optimal design of experiments was then pursued, with results compared to those generated by a parallel Bayesian design project conducted within the group. Additional work was done using a different optimality criterion, targeted at reducing the amount of parameter correlation that may be seen in D-optimal designs. Finally, conclusions and recommendations for future work were made, including a detailed explanation of how a model similar to the ones described in this paper could be used in the optimal selection of sensors and design of experiments.

Problems of requirements which are identified in the earlier phase of a software development project can deeply affect the success of the project. Thus studies which aim to decrease these problems are crucial. Automation is foreseen to be one of the possible solutions for decreasing or removing some of the problems originating from requirements. This study focuses on the development and implementation of an automated tool that will generate requirements in natural language from business process models. In this study, The benefits of the tool are discussed, and the tool is compared with other software requirement s related tools with respect to their functionality. The developed tool has been tested within a large military project and the results of using the tool are presented.

Business process modelling has gained widespread acceptance, particularly in large IT-enabled business projects. It is applied as a process design and management technique across all project lifecycle phases. While there has been much research on process modelling, there has been little attention on 'how to' conduct process modelling effectively, or on the evaluation of process modelling initiatives and outcomes. This study addresses this gap by deriving a process modelling success model that contains both the success factors (independent variables) and success dimensions (dependent variables) of process modelling. The study employs a multi-method approach, blending both qualitative and quantitative research methods. The research design commenced with a comprehensive literature review, which includes the first annotated bibliography in process modelling research. A multiple case study approach was used to build the conceptual process modelling success model which resulted in a model with eleven (11) success factors (namely Modeller Expertise, Team Structure, Project Management, User Competence, User Participation, Management Support, Leadership, Communication, Modelling Tool, Modelling Language and Modelling Methodology), two (2) moderating variables (namely Process Complexity and Project Importance) and five (5) process modelling success dimensions (namely Modeller Satisfaction, Model Quality, User Satisfaction, Model Use and Modelling Impact). This conceptual model was then operationalised and tested across a global sample, with an online survey instrument. 290 valid responses were received. The constructs were analysed seeking a parsimonious, valid and reliable model. The statistical analysis of this phase assisted in deriving the final process modelling success model. The dependent variables of this model consisted of three (3) contextual success factors (namely Top Management Support, Project Management and Resource Availability), two (2) Modelling specific success factors (namely Modelling Aids and Modeller Expertise), and two (2) moderating variables (namely Importance and Process Complexity). The dependent variable; Process Modelling Success (PMS) was derived with three (3) success measurement dimensions (namely Model Quality, Process Impacts and Process Efficiency). All resulting success factors proved to have a significant role in predicting process modelling success. Interaction effects with the moderating variables (Importance and Process Complexity) proved to exist with Top Management Support (TMS) and Resource Availability (RA). A close analysis to their interaction relationship illustrated that Importance (IMP) moderated the relationship between Top Management Support (TMS) and Process Modelling Success (PMS) in a linear manner and that Process Complexity (PC) moderated the relationship between Resource Availability (RA) and Process Modelling Success (PMS), also in a linear manner. This is the first reported study with empirical evidence on process modelling success. The progressive outcomes of this study have been readily accepted by the practitioner and academic community, with 16 published internationalrefereed- conference papers [including best paper award at the Pacific Asian Conference on Information Systems (PACIS 2004)], 2 journal publications, and over 5 major industry presentations made upon invitation.

The graphical modelling of processes is of growing popularity and high relevance to organisations that seek to document, analyse and improve their business operations. This research investigates the phenomenon of continued user acceptance of the grammars that are used to build process models. It develops and tests a theory that can be used to explain and predict why users would opt to continue working with certain grammars in their process modelling efforts. This study builds on established theories, including the Technology Acceptance Model, Expectation-Confirmation Theory, Task-Technology Fit Theory and Representation Theory. These theories suggest that end users typically strive for tools that are useful and easy to use, which confirm their expectations through firsthand utility, and which match task requirements and individual abilities. Representation theory suggests that modelling grammars should be complete and clear in their capabilities to represent real-world domains. The research model has been designed by combining conceptual studies of acceptance and continuance theories with a representational analysis of the BPMN grammar, which is a recently ratified industry standard for process modelling and thereby of high practical relevance to process modelling practice. It further incorporates findings from nineteen semi-structured interviews with process modellers in Australia. The research model has been tested and validated by means of a web-based survey with 590 process modellers world-wide. This thesis contributes to the body of knowledge in a number of ways: First, it presents an empirically validated model of the factors determining a user's intention to continue using a process modelling grammar. Second, it measures the impact that grammar characteristics as well as user and task characteristics have on user evaluations of a process modelling grammar. Third, it presents empirical evidence on the consequences that perceived representational deficiencies entail on user perceptions of a process modelling grammar.

This thesis presents the work conducted during a three year research project in the field of Control Systems and Biomaterials Engineering. The findings are presented over seven chapters, starting with a thorough literature review of the existing methods and key technologies, and following through by highlighting the existing problems with the current methods and how they have been overcome. The data is presented in tables, figures and photographs to enhance understanding and clarification. The research focuses on two relatively new manufacturing methods in the field of Tissue Engineering. Both of the methods are used for creating materials for regeneration of human and animal tissue, with the aim of replacing the current surgical methods. The methods are viewed from a control systems perspective and improvements have been made with the implementation of new technologies and methods. Additionally, further advancements are presented on the theoretical modelling field of control systems, where the shortfalls of existent modelling methods are highlighted and solutions proposed.

Orientador: Antonio Carlos Luz Lisbôa / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-25T13:00:35Z (GMT). No. of bitstreams: 1 Moratori_CarlaCristina_M.pdf: 1930284 bytes, checksum: 22ba74088d3da83b74b5036991a5b939 (MD5) Previous issue date: 2014 / Resumo: O xisto é uma rocha compacta de origem sedimentar, formada por componentes orgânicos complexos distribuídos numa matriz mineral. A matéria orgânica, chamada querogênio, é liberada sob aquecimento em temperaturas acima de 350 °C em reatores conhecidos como retortas, como óleo e gás. Este óleo quando refinado produz destilados com as mesmas características encontradas nos de petróleo, sendo considerado uma fonte de energia alternativa. A energia contida no xisto é, em termos mundiais, maior que a contida no carvão e muito maior que a contida nas reservas de petróleo convencional. Grande parte do carbono orgânico existente na Terra está presente sob a forma de combustíveis fósseis e a maior parte destes encontra-se no xisto. Para o projeto dos reatores de leito móvel para pirólise é necessário o conhecimento de várias etapas, inclusive sobre os balanços térmicos entre as partículas de xisto e a corrente gasosa. Estas trocas de calor podem ser avaliadas por modelos matemáticos e solucionadas por métodos numéricos. São estudados diferentes modelos ¿ temperatura ambiente fixa, partícula sem gradiente térmico e partícula com gradiente térmico, ambas dentro da retorta - buscando aproximar à situação vivenciada na realidade do processo onde as partículas de xisto apresentam heterogeneidade na temperatura em seu interior devido ao seu tamanho e a troca realizada com o gás. Para complementar o estudo, é feita uma análise de sensibilidade paramétrica - calor específico do xisto e do gás, condutividade térmica e o coeficiente de transferência de calor - para os diferentes modelos construídos / Abstract: Oil shale is a sedimentary rock, made up of organic material scattered throughout a mineral matrix. The organic matter, named kerogen, is released under heating at temperatures above 350 ºC, producing, upon condensation, oil and gas. The oil may be refined to produce similar derivatives to petroleum ones, therefore constituting an alternative energy source. Oil shale is by far the most abundant source of energy worldwide, more than coal, oil sand and petroleum. A large amount of organic carbon on Earth is locked in fossil fuels and most of it into oil shale reserves. The erection of oil shale processing facilities requires the design of retorts that house moving beds in which heat and mass are transferred between gaseous streams and shale. These exchanges may be calculated by heat and mass transfer balances, solved usually by numerical methods. This study analyzes the heat transfer between the gas stream and shale under various circumstances concerning the gas temperature and shale temperature, such as the development of a temperature gradient within the largest shale particles. It also carries out a parametric sensitivity analysis to investigate the effect of some parameters that affect the heat transfer, such as shale and gas specific heats, shale thermal conductivity and heat transfer coefficient / Mestrado / Engenharia Química / Mestra em Engenharia Química

Abstract The aim of this work is to improve the overall robustness in equation-oriented chemical engineering simulation work. Because the performance of locally convergent solving methods is strongly dependent on a favourable initial guess, bounded homotopy methods were investigated as a way to enlarge the domain of convergence. Bounded homotopies make it possible to keep the homotopy path inside a feasible problem domain. Thus the fatal errors possibly caused by unfeasible variable values in thermodynamic subroutines can be avoided. To enable the utilization of a narrow bounding zone, modifications were proposed for bounded homotopies. The performance of the modifications was studied with simple test problems and several types of distillation systems in the MATLAB environment. The findings illustrate that modified bounded homotopies with variables mapping make it possible to bound the homotopy path strictly to run inside a feasible problem domain. The homotopy path can be tracked accurately and flexibly also inside a narrow bounding zone. It was also noticed that by utilizing the concept of bounding the homotopy path with respect to the homotopy parameter, the possibility of approaching starting point and solution multiplicities is increased in cases where the traditional problem-independent homotopy method fails. The concept aims to connect separate homotopy path branches thus offering a trackable path with real space arithmetic. Even though the modified bounded homotopies were found to overcome several challenges often encountered with traditional problem-independent homotopy continuation methods, alone they are not enough to guarantee that the solution is approached from an arbitrary starting point. Therefore, problem-tailored solving procedures were implemented in the consideration of complex column configurations. Problem-tailored solving procedures aim to offer feasible consecutive sub-problems and thus direct the solving towards the state distribution that fulfils exact product purity specifications. As a whole, the modified bounded homotopies and problem-tailored solving procedures were found to improve the overall robustness of an equation-oriented solving approach. Thus the threshold for designing and implementing complex process systems such as complex distillation configurations for practical use could be lowered.

Biofuels have been identified as one of several GHG emission strategies to reduce the use of fossil fuels in the transport sector. Fast pyrolysis of biomass is one approach to producing second generation biofuels. The bio-oil product of fast pyrolysis can be upgraded into essential gasoline and diesel range products with conventional refinery technologies. Thus, it is important to assess their techno- economic and environmental performance at an early stage prior to commercialisation. This research was conducted with the goal of evaluating and comparing the techno-economic and environmental viability of the production of biofuels from fast pyrolysis of biomass and upgrading of bio-oil via two refinery technologies, viz. hydroprocessing and zeolite cracking. In order to achieve this aim, process models of fast pyrolysis of biomass and bio-oil upgrading via hydroprocessing and zeolite cracking were developed. The fast pyrolysis model was based on multi-step kinetic models. In addition, lumped kinetic models of the hydrodeoxygenation reactions of bio-oil were implemented. The models were verified against experimental measurements with good prediction and formed the foundation for the development of a 72 t/day fast pyrolysis plant model in Aspen Plus®. Several strategies were proposed for the two pathways to enhance energy efficiency and profitability. All in all, the results revealed that the hydroprocessing route is 16% more efficient than the zeolite cracking pathway. Moreover, the hydroprocessing route resulted in a minimum fuel selling price of 15% lower than that from the zeolite cracking pathway. Sensitivity analysis revealed that the techno-economic and environmental performance of the both pathways depends on several process, economic and environmental parameters. In particular, biofuel yield, operating cost and income tax were identified as the most sensitive techno-economic parameters, while changes in nitrogen feed gas to the pyrolysis reactor and fuel yield had the most environmental impact. It was concluded that hydroprocessing is a more suitable upgrading pathway than zeolite cracking in terms of economic viability, energy efficiency, and GHG emissions per energy content of fuel produced.

The power sector needs to be decarbonised by 2050 to meet the global target for greenhouse gas emission reduction and prevent climate change. With fossil fuels expected to play a vital role in the future energy portfolio and high efficiency penalties related to mature CO2 capture technologies, this research aimed at evaluating the efficiency improvements and alternate operating modes of the coal-fired power plants (CFPP) retrofitted with post-combustion CO2 capture. To meet this aim, process models of the CFPPs, chilled ammonia process (CAP) and calcium looping (CaL) were developed in Aspen Plus® and benchmarked against data available in the literature. Also, the process model of chemical solvent scrubbing using monoethanolamine (MEA) was adapted from previous studies. Base-load analysis of the 580 MWel CFPP retrofits revealed that if novel CAP retrofit configurations were employed, in which a new auxiliary steam turbine was coupled with the boiler feedwater pump for extracted steam pressure control, the net efficiency penalty was 8.7–8.8% points. This was close to the 9.5% points in the MEA retrofit scenario. Conversely, CaL retrofit resulted in a net efficiency penalty of 6.7–7.9% points, depending on the fuel used in the calciner. Importantly, when the optimised supercritical CO2 cycle was used instead of the steam cycle for heat recovery, this figure was reduced to 5.8% points. Considering part-load operation of the 660 MWel CFPP and uncertainty in the process model inputs, the most probable net efficiency penalties of the CaL and MEA retrofits were 9.5% and 11.5% points, respectively. Importantly, in the CaL retrofit scenarios, the net power output was found to be around 40% higher than that of the CFPP without CO2 capture and double than that for the MEA retrofit scenario. Such performance of the CaL retrofit scenario led to higher profit than that of the 660 MWel CFPP without CO2 capture, especially if its inherent energy storage capability was utilised. Hence, this study revealed that CaL has the potential to significantly reduce the efficiency and economic penalties associated with mature CO2 capture technologies.