Purpose – The purpose is to present a Generic Model that considers the machining process's resource simulation into account as well as the operator variables. The operator variables are associated with the operations they carry out, the time required, and the ideal number of operators. This Generic Model will serve as a framework for future initiatives for industry and academia, by including resource simulation for machining operations. Design/Methodology/Approach - The simulation model addresses the optimal resource allocation to support efficient machine utilization. The resource in this simulation model is composed of human operators, working across several machining operations of similar specifications, with all of the machines arranged in parallel. The resulting simulation model presented in this thesis is generic in its organization. Findings - The developed Generic Model incorporates material-flow and logistics, tool-ware and machine failure, product-tool interdependence, and other related factors pertaining to the operator task allocation and the number of operators. Further, the optimization of process resources has been presented through results obtained pertaining to machine utilization, productivity and operator distance travelled. Research implications - Several industries are working towards overcoming the challenges pertaining to the resource allocation in the manufacturing process and the requirement for optimizing. As a result, simulation provides a less expensive option for making resource allocation decisions and testing the chosen options. The presented Generic Model will serve as a framework for future initiatives including resource simulation and allocation for machining operations and operators. Originality/Value- The model presented in this thesis, will therefore allow production planners and managers to allocate the necessary resources and observe the outcomes before they are implemented on the shop floor. The Generic Model adds value by allowing for future adjustments in model resources and parameters based on system requirements.

Batch reactor is an essential unit operation in almost all batch-processing industries. Different types of reaction schemes (such as series, parallel and complex) and different order of model complexity (short-cut, detailed, etc. ) result in different sets of model equations and computer coding of all possible sets of model equations is cumbersome and time consuming. In this work, therefore, a general computer program (GBRM - General Batch Reactor Model) is developed to generate all possible sets of equations automatically and as required. GBRM is tested for different types of reaction schemes and for different order of model complexity and its flexibility is demonstrated. The above GBRM computer program is lodged with Dr. I. M. Mujtaba. One of the challenges in batch reactors is to ensure desired performance of individual batch reactor operations. Depending on the requirement and the objective of the process, optimisation in batch reactors leads to different types of optimisation problems such as maximum conversion, minimum time and maximum profit problem. The reactor temperature, jacket temperature and jacket flow rate are the main control variables governing the process and these are optimised to ensure maximum benefit. In this work, an extensive study on mainly conventional batch reactor optimisation is carried out using GBRM coupled with efficient DAEs (Differential and Algebraic Equations) solver, CVP (Control Vector Parameterisation) technique and SQP (Successive Quadratic Programming) based optimisation technique. The safety, environment and product quality issues are embedded in the optimisation problem formulations in terms of constraints. A new approach for solving optimisation problem with safety constraint is introduced. All types of optimisation problems mentioned above are solved off-line, which results to optimal operating policies. The off-line optimal operating policies obtained above are then implemented as set points to be tracked on-line and various types of advanced controllers are designed for this purpose. Both constant and dynamic set points tracking are considered in designing the controllers. Here, neural networks are used in designing Direct Inverse and Inverse-Model-Based Control (IMBC) strategies. In addition, the Generic Model Control (GMC) coupled with on-line neural network heat release estimator (GMC-NN) is also designed to track the optimal set points. For comparison purpose, conventional Dual Mode (DM) strategy with PI and PID controllers is also designed. Robustness tests for all types of controllers are carried out to find the best controller. The results demonstrate the robustness of GMC-NN controller and promise neural controllers as potential robust controllers for future. Finally, an integrated framework (BATCH REACT) for modelling, simulation, optimisation and control of batch reactors is proposed.

L'industrie est aujourd'hui, comme elle a toujours été, une pierre angulaire de l'économie pour chaque pays développé. Avoir une base solide en entreprises industrielles est très important parce qu’elles poussent et stimulent tous les autres secteurs de l'économie, et offrent également une grande variété d'emplois qui apporte des bonnes conditions de vie dans de nombreux secteurs de la société. L’augmentation de la concurrence mondiale, l’évolution rapide du marché, la nécessité de créer des entreprises stables avec des usines rentables obligent la mise en oeuvre d’une démarche globale prenant en compte à la fois les aspects techniques, économiques, logistiques et sociétaux lors de la conception d’un système de production innovant. L’objectif de cette thèse est de contribuer au développement d’un concept innovant de Systèmes de Production Reconfigurables et Agiles (SPRA) permettant de s'adapter rapidement et efficacement aux exigences imposées du marché, des clients, de la technologie des procédés, de l’environnement et de la société afin que l’entreprise soit dynamique, compétitive et rentable. Dans ces travaux de thèse, la proposition d'un modèle générique et la caractérisation de ce nouveau type de système de production ont été décrits en utilisant le langage de modélisation des systèmes complexes (SysML : Systems Modeling Language). Ensuite, nous avons développé un processus de reconfiguration qui représente une démarche à suivre pour concevoir et implanter une nouvelle configuration. De plus, un pilotage opérationnel adapté au SPRA a été introduit. Enfin, quelques travaux développés au cours de cette thèse ont été partiellement déployés sur un démonstrateur industriel au sein de la plate-forme AIP-PRIMECA Auvergne. / Industry is, today as it has always been, a cornerstone of the economy for any developed country. Having a strong manufacturing base is very important because it impels and stimulates all the other sectors of the economy. It provides a wide variety of job, which bring higher standards of living to many sectors of society, and builds a strong middle class. Increasing global competition, rapid changes in the marketplace and the need to create stable companies with profitable plants require the implementation of a global approach, taking into account technical, economic, logistic and societal aspects in the design of an innovative manufacturing system. The aim of this dissertation is to contribute to the development of an innovative concept of Reconfigurable and Agile Manufacturing Systems (RAMS) to adapt quickly and effectively to the requirements imposed by markets, customers, technology processes, the environment and society, to ensure that the enterprise is dynamic, competitive and profitable. In this thesis work, the characterization and proposal of a generic model for this new type of manufacturing system have been described using the language of complex systems modeling (SysML: Systems Modeling Language). We have developed a reconfiguration process that represents the approach to follow in the design and implementation of a new configuration. In addition, the operational control of a RAMS has been introduced. Finally, some works developed in this thesis have been partially deployed on an industrial demonstrator within the AIP-PRIMECA Auvergne organisation.

Dans un contexte de personnalisation de masse, la configuration concourante du produit et de son processus d’obtention constituent un défi industriel important : de nombreuses options ou alternatives, de nombreux liens ou contraintes et un besoin d’optimisation des choix réalisés doivent être pris en compte. Ce problème est intitulé O-CPPC (Optimization of Concurrent Product and Process Configuration). Nous considérons ce problème comme un CSP (Constraints Satisfaction Problem) et l’optimisons avec des algorithmes évolutionnaires. Un état de l’art fait apparaître : i) que la plupart des travaux de recherche sont illustrés sur des exemples spécifiques à un cas industriel ou académique et peu représentatifs de la diversité existante ; ii) un besoin d’amélioration des performances d’optimisation afin de gagner en interactivité et faire face à des problèmes de taille plus conséquente. En réponse au premier point, ces travaux de thèse proposent les briques d’un modèle générique du problème O-CPPC. Ces briques permettent d’architecturer le produit et son processus d’obtention. Ce modèle générique est utilisé pour générer un benchmark réaliste pour évaluer les algorithmes d’optimisation. Ce benchmark est ensuite utilisé pour analyser la performance de l’approche évolutionnaire CFB-EA. L’une des forces de cette approche est de proposer rapidement un front de Pareto proche de l’optimum. Pour répondre au second point, une amélioration de cette méthode est proposée puis évaluée. L’idée est, à partir d’un premier front de Pareto approximatif déterminé très rapidement, de demander à l’utilisateur de choisir une zone d’intérêt et de restreindre la recherche de solutions uniquement sur cette zone. Cette amélioration entraine des gains de temps de calcul importants. / In a context of mass customization, the concurrent configuration of the product and its production process constitute an important industrial challenge: Numerous options or alternatives, numerous links or constraints and a need to optimize the choices made. This problem is called O-CPPC (Optimization of Concurrent Product and Process Configuration). We consider this problem as a CSP (Constraints Satisfaction Problem) and optimize it with evolutionary algorithms. A state of the art shows that: i) most studies are illustrated with examples specific to an industrial or academic case and not representative of the existing diversity; ii) a need to improve optimization performance in order to gain interactivity and face larger problems. In response to the first point, this thesis proposes a generic model of the O-CPPC problem. This generic model is used to generate a realistic benchmark for evaluating optimization algorithms. This benchmark is then used to analyze the performance of the CFB-EA evolutionary approach. One of the strengths of this approach is to quickly propose a Pareto front near the optimum. To answer the second point, an improvement of this method is proposed and evaluated. The idea is, from a first approximate Pareto front, to ask the user to choose an area of interest and to restrict the search for solutions only on this area. This improvement results in significant computing time savings.

Batch reactor is an essential unit operation in almost all batch-processing industries. Different types of reaction schemes (such as series, parallel and complex) and different order of model complexity (short-cut, detailed, etc. ) result in different sets of model equations and computer coding of all possible sets of model equations is cumbersome and time consuming. In this work, therefore, a general computer program (GBRM - General Batch Reactor Model) is developed to generate all possible sets of equations automatically and as required. GBRM is tested for different types of reaction schemes and for different order of model complexity and its flexibility is demonstrated. The above GBRM computer program is lodged with Dr. I. M. Mujtaba. One of the challenges in batch reactors is to ensure desired performance of individual batch reactor operations. Depending on the requirement and the objective of the process, optimisation in batch reactors leads to different types of optimisation problems such as maximum conversion, minimum time and maximum profit problem. The reactor temperature, jacket temperature and jacket flow rate are the main control variables governing the process and these are optimised to ensure maximum benefit. In this work, an extensive study on mainly conventional batch reactor optimisation is carried out using GBRM coupled with efficient DAEs (Differential and Algebraic Equations) solver, CVP (Control Vector Parameterisation) technique and SQP (Successive Quadratic Programming) based optimisation technique. The safety, environment and product quality issues are embedded in the optimisation problem formulations in terms of constraints. A new approach for solving optimisation problem with safety constraint is introduced. All types of optimisation problems mentioned above are solved off-line, which results to optimal operating policies. The off-line optimal operating policies obtained above are then implemented as set points to be tracked on-line and various types of advanced controllers are designed for this purpose. Both constant and dynamic set points tracking are considered in designing the controllers. Here, neural networks are used in designing Direct Inverse and Inverse-Model-Based Control (IMBC) strategies. In addition, the Generic Model Control (GMC) coupled with on-line neural network heat release estimator (GMC-NN) is also designed to track the optimal set points. For comparison purpose, conventional Dual Mode (DM) strategy with PI and PID controllers is also designed. Robustness tests for all types of controllers are carried out to find the best controller. The results demonstrate the robustness of GMC-NN controller and promise neural controllers as potential robust controllers for future. Finally, an integrated framework (BATCH REACT) for modelling, simulation, optimisation and control of batch reactors is proposed. / University Sains Malaysia

Software has been subject to change, at all times, in order to make parts of it, for instance, more reusable, better to understand by humans, or to increase efficiency under a certain point of view. Restructurings of existing software can be complex. To prevent developers from doing this manually, they got tools at hand being able to apply such restructurings automatically. These automatic changes of existing software to improve quality while preserving its behaviour is called refactoring. Refactoring is well investigated for programming languages and mature tools exist for executing refactorings in integrated development environments (IDEs). In recent years, the development paradigm of Model-Driven Software Development (MDSD) became more and more popular and we experience a shift in the sense that development artefacts are considered as models which conform metamodels. This can be understood as abstraction, which resulted in the trend that a plethora of new so-called model-based Domain-Specific Languages (DSLs) arose. DSLs have become an integral part in the MDSD and it is obvious that models are subject to change, as well. Thus, refactoring support is required for DSLs in order to prevent users from doing it manually. The problem is that the amount of DSLs is huge and refactorings should not be implemented for new for each of them, since they are quite similar from an abstract viewing. Existing approaches abstract from the target language, which is not flexible enough because some assumptions about the languages have to be made and arbitrary DSLs are not supported. Furthermore, the relation between a strategy which finds model deficiencies that should be improved, a resolving refactoring, and the improved quality is only implicit. Focussing on a particular quality and only detecting those deficiencies deteriorating this quality is difficult, and elements of detected deficient structures cannot be referred to in the resolving refactoring. In addition, heterogeneous models in an IDE might be connected physically or logically, thus, they are dependent. Finding such connections is difficult and can hardly be achieved manually. Applying a restructuring in a model implied by a refactoring in a dependent model must also be a refactoring, in order to preserve the meaning. Thus, this kind of dependent refactorings require an appropriate abstraction mechanism, since they must be specified for dependent models of different DSLs. The first contribution, Role-Based Generic Model Refactoring, uses role models to abstract from refactorings instead of the target languages. Thus, participating structures in a refactoring can be specified generically by means of role models. As a consequence, arbitrary model-based DSLs are supported, since this approach does not make any assumptions regarding the target languages. Our second contribution, Role-Based Quality Smells, is a conceptual framework and correlates deficiencies, their deteriorated qualities, and resolving refactorings. Roles are used to abstract from the causing structures of a deficiency, which then are subject to resolving refactorings. The third contribution, Role-Based Co-Refactoring, employs the graph-logic isomorphism to detect dependencies between models. Dependent refactorings, which we call co-refactorings, are specified on the basis of roles for being independent from particular target DSLs. All introduced concepts are implemented in our tool Refactory. An evaluation in different scenarios complements the thesis. It shows that role models emerged as very powerful regarding the reuse of generic refactorings in arbitrary languages. Role models are suited as an interface for certain structures which are to be refactored, scanned for deficiencies, or co-refactored. All of the presented approaches benefit from it.

The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.

Software has been subject to change, at all times, in order to make parts of it, for instance, more reusable, better to understand by humans, or to increase efficiency under a certain point of view. Restructurings of existing software can be complex. To prevent developers from doing this manually, they got tools at hand being able to apply such restructurings automatically. These automatic changes of existing software to improve quality while preserving its behaviour is called refactoring. Refactoring is well investigated for programming languages and mature tools exist for executing refactorings in integrated development environments (IDEs). In recent years, the development paradigm of Model-Driven Software Development (MDSD) became more and more popular and we experience a shift in the sense that development artefacts are considered as models which conform metamodels. This can be understood as abstraction, which resulted in the trend that a plethora of new so-called model-based Domain-Specific Languages (DSLs) arose. DSLs have become an integral part in the MDSD and it is obvious that models are subject to change, as well. Thus, refactoring support is required for DSLs in order to prevent users from doing it manually. The problem is that the amount of DSLs is huge and refactorings should not be implemented for new for each of them, since they are quite similar from an abstract viewing. Existing approaches abstract from the target language, which is not flexible enough because some assumptions about the languages have to be made and arbitrary DSLs are not supported. Furthermore, the relation between a strategy which finds model deficiencies that should be improved, a resolving refactoring, and the improved quality is only implicit. Focussing on a particular quality and only detecting those deficiencies deteriorating this quality is difficult, and elements of detected deficient structures cannot be referred to in the resolving refactoring. In addition, heterogeneous models in an IDE might be connected physically or logically, thus, they are dependent. Finding such connections is difficult and can hardly be achieved manually. Applying a restructuring in a model implied by a refactoring in a dependent model must also be a refactoring, in order to preserve the meaning. Thus, this kind of dependent refactorings require an appropriate abstraction mechanism, since they must be specified for dependent models of different DSLs. The first contribution, Role-Based Generic Model Refactoring, uses role models to abstract from refactorings instead of the target languages. Thus, participating structures in a refactoring can be specified generically by means of role models. As a consequence, arbitrary model-based DSLs are supported, since this approach does not make any assumptions regarding the target languages. Our second contribution, Role-Based Quality Smells, is a conceptual framework and correlates deficiencies, their deteriorated qualities, and resolving refactorings. Roles are used to abstract from the causing structures of a deficiency, which then are subject to resolving refactorings. The third contribution, Role-Based Co-Refactoring, employs the graph-logic isomorphism to detect dependencies between models. Dependent refactorings, which we call co-refactorings, are specified on the basis of roles for being independent from particular target DSLs. All introduced concepts are implemented in our tool Refactory. An evaluation in different scenarios complements the thesis. It shows that role models emerged as very powerful regarding the reuse of generic refactorings in arbitrary languages. Role models are suited as an interface for certain structures which are to be refactored, scanned for deficiencies, or co-refactored. All of the presented approaches benefit from it.:List of Figures xv List of Tables xvii List of Listings xix 1. Introduction 1 1.1. Language-Tool Generation Without Consideration Of Time And Space . . . . . 4 1.2. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3. Generic Quality-Aware Refactoring and Co-Refactoring in Heterogeneous Model Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2. Foundations 15 2.1. Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2. Model-Driven Software Development . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1. Levels of Abstraction and Metamodelling . . . . . . . . . . . . . . . . . 17 2.2.2. Model Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3. Role-Based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3. Related Work 23 3.1. Model Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2. Determination of Quality-Related De ciencies . . . . . . . . . . . . . . . . . . . 32 3.2.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3. Co-Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4. Role-Based Generic Model Refactoring 51 4.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2. Specifying Generic Refactorings with Role Models . . . . . . . . . . . . . . . . . 53 4.2.1. Specifying Structural Constraints using Role Models . . . . . . . . . . . 55 4.2.2. Mapping Roles to Language Concepts Using Role Mappings . . . . . . . 57 4.2.3. Specifying Language-Independent Transformations using Refactoring Speci cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.4. Composition of Refactorings . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.3. Preserving Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5. Suggesting Role Mappings as Concrete Refactorings 73 5.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2. Automatic Derivation of Suggestions for Role Mappings with Graph Querying . 74 5.3. Reduction of the Number of Valid Matches . . . . . . . . . . . . . . . . . . . . . 76 5.4. Comparison to Model Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6. Role-Based Quality Smells as Refactoring Indicator 79 6.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2. Correlating Model De ciencies, Qualities and Refactorings . . . . . . . . . . . . 80 6.2.1. Quality Smell Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.2.2. Quality Smell Calculation Repository . . . . . . . . . . . . . . . . . . . . 85 6.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. A Quality Smell Catalogue for Android Applications 89 7.1. Quality Smell Catalogue Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.2. Acquiring Quality Smells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3. Structure-Based Quality Smells—A Detailed Example . . . . . . . . . . . . . . . 92 7.3.1. The Pattern Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.2. Quality Smell: Interruption from Background . . . . . . . . . . . . . . . 93 7.4. Quality Smells for Android Applications . . . . . . . . . . . . . . . . . . . . . . 96 7.4.1. Quality Smell: Data Transmission Without Compression . . . . . . . . . 96 7.4.2. Quality Smell: Dropped Data . . . . . . . . . . . . . . . . . . . . . . . . 98 7.4.3. Quality Smell: Durable WakeLock . . . . . . . . . . . . . . . . . . . . . 98 7.4.4. Quality Smell: Internal Use of Getters/Setters . . . . . . . . . . . . . . . 99 7.4.5. Quality Smell: No Low Memory Resolver . . . . . . . . . . . . . . . . . 101 7.4.6. Quality Smell: Rigid AlarmManager . . . . . . . . . . . . . . . . . . . . 101 7.4.7. Quality Smell: Unclosed Closeable . . . . . . . . . . . . . . . . . . . . . 102 7.4.8. Quality Smell: Untouchable . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 8. Role-Based Co-Refactoring in Multi-Language Development Environments 105 8.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8.3. Dependency Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8.3.1. Categories of Model Dependencies . . . . . . . . . . . . . . . . . . . . . 108 8.3.2. When to Determine Model Dependencies . . . . . . . . . . . . . . . . . 110 8.3.3. How to Determine Model Dependencies . . . . . . . . . . . . . . . . . . 111 8.4. Co-Refactoring Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 8.4.1. Specifying Coupled Refactorings with Co-Refactoring Speci cations . . 114 8.4.2. Specifying Bindings for Co-Refactorings . . . . . . . . . . . . . . . . . . 116 8.4.3. Determination of Co-Refactoring Speci cations . . . . . . . . . . . . . . 118 8.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 8.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9. Refactory: An Eclipse Tool For Quality-Aware Refactoring and Co-Refactoring 121 9.1. Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.1. Role Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.2. Refactoring Speci cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 9.1.3. Role Model Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 9.1.4. Refactoring Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 9.1.5. Custom Refactoring Extensions . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.6. Pre- and Post-conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.7. Integration Into the Eclipse Refactoring Framework . . . . . . . . . . . . 130 9.2. Quality Smell Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 9.3. Co-Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 9.3.1. Concrete Syntax of a CoRefSpec . . . . . . . . . . . . . . . . . . . . . . . 138 9.3.2. Expression Evaluation by Using an Expression Language . . . . . . . . . 138 9.3.3. UI and Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 9.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 10. Evaluation 143 10.1. Case Study: Reuse of Generic Refactorings in many DSLs . . . . . . . . . . . . . 143 10.1.1. Threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 10.1.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 10.1.3. Experience Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 10.2. Case Study: Suggestion of Valid Role Mappings . . . . . . . . . . . . . . . . . . 147 10.2.1. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 10.2.2. Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 151 10.3. Proof of Concept: Co-Refactoring OWL and Ecore Models . . . . . . . . . . . . 155 10.3.1. Coupled OWL-Ecore Refactorings . . . . . . . . . . . . . . . . . . . . . 156 10.3.2. Realisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 10.3.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 11. Summary, Conclusion and Outlook 161 11.1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 11.2. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 11.3. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Appendix 169 A. List of Role Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 B. Comparison to Role Feature Model . . . . . . . . . . . . . . . . . . . . . . . . . 171 C. Complete List of Role Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 D. List of all IncPL Patterns for Detecting Quality Smells . . . . . . . . . . . . . . . 176 E. Post-Processor of the Extract CompositeState refactoring for UML State Machines 183 F. Speci cation of the Conference Language . . . . . . . . . . . . . . . . . . . . . . 185 List of Abbreviations 187 Bibliography 191

The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.

This diploma thesis is focused on study of cognitive and communicative phenomena, which appear at solving exercises in semantic and arithmetic environment First-Grade Students' Stepping. These phenomena are revealed by a set of quantitative analysis experiments undertaken with first-grade pupils. Theoretical basis, which make the description of revealed phenomena possible, are generic model theory and semantic embedding of number (Hejný), procept theory (Gray and Tall), mathematical schema building theory (Hejný) and didactic constructivism, structural and semantic conception of number, process, concept, procept.