Business process reengineering (BPR) has been considered as one of the key approaches to increasing the competitive edge of many modern enterprises. Many big enterprises have taken diversified degree of reengineering to their business processes. The importance of understanding the existing business processes and evaluating the new business processes before they are actually deployed is commonly recognized. Without careful examination of the existing and new business processes, the change in business process. In this research, we look into the business process analysis issues under the scope of BPR. We first examine various models for business processes. As each model is invented with a purpose, e.g., for identifying the critical path in a factory manufacturing environment, for automating workflow in an office environment, etc., they may not be completely suitable for business process analysis. We try to identify the requirements of business process analysis and propose a model to meet these requirements. We finally design and implement a business process analyzer. This business process analyzer use our proposed business process model and is able to answer the queries from the BPR team expressed by our proposed query language.

Complex reaction kinetics of the solid acid alkylation of isobutane with butenes over a proton-exchanged Y-zeolite has been modeled at the elementary step level. Starting with a computer algorithm that generated the reaction network based on the fundamentals of the carbenium ion chemistry, the formation of over 100+ product species has been modeled in order to gain understanding of the underlying phenomena leading to rapid catalyst deactivation and product selectivity shifts observed in experimental runs. An experimental investigation of the solid acid alkylation process was carried out in a fixed bed catalytic reactor operating with an excess of isobutane under isothermal conditions at moderate temperatures (353-393 K) in liquid phase. Experimental data varying with run-time for a set of butene space-times and reaction temperatures were collected for parameter estimation purposes. A kinetic model was formulated in terms of rate expressions at the elementary step level including a rigorous modeling of deactivation through site coverage. The single event concept was applied to each rate coefficient at the elementary step level to achieve a significant reduction in the number of model parameters. Based on the identification of structural changes leading to the creation or destruction of symmetry axes and chiral centers in an elementary step, formulae have been developed for the calculation of the number of single events. The Evans-Polanyi relationship and the concept of stabilization energy were introduced to account for energy levels in surface-bonded carbenium ions. A novel functional dependency of the stabilization energy with the nature of the carbenium ion and the carbon number was proposed to account for energy effects from the acid sites on the catalyst. Further reductions in the number of parameters and simplification of the equations for the transient pseudohomogeneous one-dimensional plug-flow model of the reactor were achieved by means of thermodynamic constraints. Altogether, the single event concept, the Evans-Polanyi relationship, the stabilization energy approach and the thermodynamic constraints led to a set of 14 parameters necessary for a complete description of solid acid alkylation at the elementary step level.

The successful development and implementation of business information systems requires an integrated approach which includes the seamless design of both the business processes and the information systems supporting the business processes. Therefore, several frameworks and modeling methods have been developed for an integrated modeling of the entire enterprise with respect to both organizational and information systems aspects. Due to the architecture of most existing business information systems, these approaches were usually based on traditional software development paradigms rather than on object-orientation. On the other hand, object-oriented modeling methods used to cover only aspects which are close to implementation, but not the business processes. Currently, however, these two worlds are mov-ing closer together because there are several benefits using business process models during object-oriented software development. This paper describes an approach for integrating business process and object-oriented modeling methods. With this approach, it is possible to model the relevant aspects of a company’s business processes and its object-oriented information systems without the need for switching between different modeling paradigms or for trans-lating between different modeling languages.

Manufacturers address the distinct operational objectives of product innovation and manufacturing efficiency by having separate fabrication facilities (“fabs”) for development and manufacturing. Additionally, the industrial manufacture of a semiconductor product proceeds through several stages of production. These are typically a research and development (R&D) stage, a ramping stage, and a manufacturing stage. These production stages are distributed over the different fabs. These differences in fabrication environment and stage of production result in differences in the characteristics of production of a semiconductor product over its manufacturing lifetime. Some examples of these differences are device yield, breadth of processing conditions, throughput, number of reaction chambers operating in parallel, metrology, and data collection. These differences are reflected in the data available in the fab databases. This research explores the use of a neural network modeling and genetic algorithm optimization method with these different datasets. The focus is on a high-aspect-ratio etch process across the different fabs and production stages. Models are built from process input variables to post-process metrology, and from process input variables to yield metrics. In the latter case, there can be tens of processes occurring between the model input and output variables. I demonstrate the usefulness and industrial application of neural network process modeling and genetic algorithm recipe optimization by performing a reaction chamber matching exercise on a manufacturing line. The performance of a reaction chamber can deviate from target, either in terms of its post-process metrology or its associated yield metrics. The method developed herein generated an optimized recipe that brought the outlying behavior of a chamber closer to target and closer to that of the other chambers (“chamber matching”). This is one of many possible applications. It was chosen because it demonstrates both the fidelity of the process models and the effectiveness of the optimization algorithm.

While hollow glass microspheres are commonly used in syntactic foam, their abrasive and brittle properties usually result in poor processability and have adverse effects on the foam performance. Therefore, a number of attempts have been made in the industry to replace hollow glass microspheres with polymeric foamed microspheres. Among many choices, expandable thermoplastic (ETP) microspheres filled syntactic foam has shown its high potential to become a novel class of engineering materials, especially for lightweight structural applications. However, conventional processing techniques for syntactic foam usually experience difficulties such as high processing viscosity, low loading of foam fillers, and ineffective microsphere expansion. To address these emerging issues, a microwave expansion process to produce thermoset-matrix syntactic foam containing thermoplastic foam beads was developed in this thesis work. In this process, unexpanded ETP microspheres were directly foamed in uncured thermoset matrix via microwave heating. Expandable polystyrene (EPS) microspheres and epoxy resin were chosen as a model material system. The resin viscosity and specific microwave energy are found to be the two primary control parameters determining the process window. Mechanical characterization showed that the syntactic foam can outweigh neat polymer in lightweight structural applications and was effectively toughened by foamed EPS. Furthermore, the microwave expansion process was found to be capable of molding syntactic foam parts of relatively sophisticated geometry with smooth surfaces. In order to broaden its impact, the microwave expansion process was extended to produce composite EPS foam. This process converts an expandable suspension into a composite foam with a honeycomb-like barrier structure. The suspension viscosity was found to highly influence the foam morphology. Results from mechanical tests showed that the existence of the barrier structure can considerably improve the mechanical performance of the composite foam. Fire-retardation tests demonstrated that the barrier structure can effectively stop the fire path into the foam, suppress toxic smoke generation, and maintain foam structure integrity. A general formulation was developed to model the EPS expansion to optimize the microwave expansion process. A semi-analytical solution was first obtained based on the case of a single bubble expansion in an infinite matrix. The dimensionless bubble radius and pressure are defined and found to be as exponential functions of dimensionless expansion time. The semi-analytical solution can qualitatively predict the radial expansion of EPS microsphere observed in a real-time experiment. To have an accurate prediction, a numerical solution was obtained to the model that couples the nucleation and expansion of multiple bubbles in a finite matrix. The results show that the numerical solution can quantitatively predict the radial expansion of EPS. A parameter sensitivity study was performed to examine the effect of each parameter over the expansion process.

This thesis considers the requirement engineering process from the small scale industries point of view. It begins with a brief introduction to software requirements and then proceeds to a detailed study of the requirement engineering process. The later part of the thesis considers Business Process Modeling and how it helps in understanding any business in a better way and how conceptual model helps in extracting business requirements from any particular business scenario. The Object Oriented Technique is used for building the conceptual model. The thesis concludes with a case study. / text

Molecular contaminants on the surface of nanoparticles (NPs) are critical in determining the environmental safety and health (ESH) impacts of NPs. In order to characterize the surface properties that relate to adsorption and desorption interactions, a method has been developed for studying the dynamic interactions of adsorbing species on NP samples. The results are analyzed using a process simulator to determine fundamental properties such as capacity, affinity, rate expressions, and activation energies of NP interactions with contaminants. The method is illustrated using moisture as a representative model compound and particles of SiO₂, HfO₂, and CeO₂, which are three oxides used in semiconductor manufacturing. The effect of particle size and temperature on the surface properties of porous oxide NPs was investigated. Infrared spectra peaks corresponding to the stretching vibration of water molecules were monitored by in-site Fourier transform infrared (FTIR) spectroscopy. These are related to the moisture concentration on the surface of NPs. A transient multilayer model was developed to represent the fundamental steps in the process. The thermal stability of adsorbed species and the strength of bonding to the surface were evaluated by determining the activation energies of the various steps. The results indicate that the surface interaction parameters are dependent on species, temperature, and particle size. SiO₂ has the highest adsorption capacity and therefore is most prone to the adsorption of moisture and similar contaminants. However, the affinity of the NPs for H₂O retention is highest for CeO₂ and lowest for SiO₂. As temperature decreases, NPs exhibit a higher saturated moisture concentration and are more prone to the adsorption of moisture and similar contaminants. Furthermore, smaller NPs have a higher saturated surface concentration and a slower response to purging and desorption. Factors contributing to the environmental and health impact of NPs (extent of surface coverage, capacity, and activation energy of retention) have been investigated during this study. The second objective of this study is to develop a method to measure and control the contamination in ultra-high-purity (UHP) gas delivery systems. Modern semiconductor manufacturing plants have very stringent specifications for the moisture content at the point-of-use, usually below several parts per billion (ppb). When the gas delivery system gets contaminated, a significant amount of purge time is required for recovery of the background system. Therefore, it is critical for high-volume semiconductor manufacturers to reduce purge gas usage as well as purge time during the dry-down process. A method consisting of experimental research and process simulations is used to compare steady-state purge (SSP) process of constant pressure and flow rate with the pressure-cycle purge (PCP) process of cyclic pressure and flow rate at a controlled interval. The results show that the PCP process has significant advantages over the SSP process under certain conditions. It can reduce the purge time and gas usage when the gas purity at point-of-use is the major concern. The process model is validated by data congruent with the experimental results under various operating conditions and is useful in conducting parametric studies and optimizing the purge process for industrial applications. The effect of key operational parameters, such as start time of PCP process as well as choice of PCP patterns has been studied.

Today, organizations often need to modify their business processes to cope with changes in the environment, such as mergers/acquisitions, new government regulations, and new customer demand. Most organizations also have a set of business policies defining the way they conduct their business. Although there has been extensive research on process analysis and design, how to systematically extract workflow models from business policies has not been studied, resulting in a missing link between the specification of business policies and the modeling of business processes.Given that process changes are often determined by executives and managers at the policy level, the aforementioned missing link often leads to inefficient and inaccurate implementation of process changes by business analysts and process designers. We refer to this problem as the policy mismatch problem in business process management. For organizations with large-scale business processes and a large number of business policies, solving the policy mismatch problem is very difficult and challenging.In this dissertation, we attempt to provide a formal link between business policies and workflow models by proposing a logic-based methodology for process analysis and design. In particular, we first propose a Policy-driven Process Design (PPD) methodology to formalize the procedure of extracting workflow models from business policies. In PPD, narrative process policies are parsed into precise information on various workflow components, and a set of process design rules and algorithms are applied to generate workflow models from that information.We also develop a logic-based process modeling language named Unified Predicate Language (UPL). UPL is able to represent all workflow components in a single logic format and provides analytical capability via logic inference and query. We demonstrate UPL's expressive power and analytical ability by applying it to process design and process change analysis. In particular, we use UPL to define and classify process change anomalies and develop algorithms to verify and enforce process consistency.The Policy-driven Process Design, Unified Predicate Language, and process change analysis approach found in this dissertation contribute to business process management research by providing a formal methodology for resolving the policy mismatch problem.

The purpose of this research was to determine if patient journey process modeling could act as a change management tool to support electronic health record (EHR) adoption, at a tertiary-care mental health centre. This research study was based on a pre/post design, which evaluated the attitudes of clinicians??? pre and post implementation of the EHR. A survey was used to assess the attitudes of various healthcare professionals, such as physicians, nurses and a spectrum of allied health disciplines, at various phases of the planning and implementation process. In addition to the surveys, current and future state PaJMa (patient journey modeling architecture) models representing technology use and process flows of all units were created by observational studies, and served as change management tools. These PaJMa models were then presented as part of an intervention that was held in the form of an educational session to highlight the benefits of technology, and to address the common concerns identified from the initial survey results. The centre for mental health sciences facility was used as the case study to apply the PaJMa model and assess its change management functionality. Since, the organization was moving from paper to electronic based patient charts it was an ideal choice for this research. It was predicted that the attitudes and opinions of clinicians towards the EHR implementation, and EHRs in general, would change and become more positive with increased knowledge and education. This in-turn would increase EHR adoption and hence lead to a successful implementation.

Two of the hottest areas in porous materials research in the last decade have been in energy storage, mainly hydrogen and methane, and in carbon capture and sequestration (CCS). Although these topics are intricately linked in terms of our future energy landscape, the specific materials needed to solve these problems must have significantly different properties. High pressure gas storage is most often linked with high surface areas and pore volumes, while carbon capture sorbents require high sorption enthalpies to achieve the needed selectivity. The latter typically involves separating CO2 from mixed gas streams of mostly nitrogen via a temperature swing adsorption (TSA) process. Much of the excitement has arisen because of the potential of metal-organic frameworks (MOFs) and porous polymer networks (PPNs). Both classes of materials have extremely high surface areas (upwards of 4000 m2/g) and can be modified to have specific physical properties, thus enabling high performance materials for targeted applications. This dissertation focuses on the synthesis and characterization of these novel materials for both applications by tuning framework topologies, composition, and surface properties. Specifically, two routes to synthesize a single molecule trap (SMT) highlight the flexibility of MOF design and ability to tune a framework to interact with specifically one guest molecule; computational and experimental evidence of the binding mechanism are shown as well. Furthermore, eight PPNs are synthesized and characterized for post-combustion carbon capture and direct air capture applications. In addition a high-throughput model, grounded in thermodynamics, to calculate the energy penalty associated with the carbon capture step is presented in order to evaluate all materials for TSA applications provide a comparison to the state of the art capture technologies. This includes results of working capacity and energy calculations to determine parasitic loads (per ton of CO2 captured) from readily available experimental data of any material (adsorption isotherms and heat capacities) using a few simple equations. Through various systematic investigations, trends are analyzed to form structure property relationships that will aid future material development.