This thesis deals with the design of a container carrier with interchangeable superstructures up to 3000 kg of technical weight. The introduction renders a critical review of similar devices and consecutive review of the device in terms of legislative matters. A dynamic simulation was used to calculate the loaded states. The outcome of the simulation was used to set the border conditions, which were then used for strength analysis calculations using finite element method (FEM). Blueprints of the final design are a part of the thesis.

The power industry has undergone significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structures have been reformed into competitive markets in pursuit of increased efficiency in electricity production and utilization. However, along with market deregulation, power systems presently face severe challenges. One is power system stability, a problem that has attracted widespread concern because of severe blackouts experienced in the USA, the UK, Italy, and other countries. Another is that electricity market operation warrants more effective planning, management, and direction techniques due to the ever expanding large-scale interconnection of power grids. Moreover, many exterior constraints, such as environmental protection influences and associated government regulations, now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more advanced power system data analysis methods are required in the deregulated, market-oriented environment. At the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. In this thesis, the reported research is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: a newly proposed quantum-inspired particle swarm optimization and self-adaptive learning scheme for radial basis function neural networks; online wavelet denoising techniques; electricity regional reference price forecasting methods in the electricity market; and power system security assessment approaches for deregulated markets, including fault analysis, voltage profile prediction under contingencies, and machine learning based load shedding scheme for voltage stability enhancement. Evolutionary algorithms (EAs) inspired by biological evolution mechanisms have had great success in power system stability analysis and operation planning. Here, a new quantum-inspired particle swarm optimization (QPSO) is proposed. Its inspiration stems from quantum computation theory, whose mechanism is totally different from those of original EAs. The benchmark data sets and economic load dispatch research results show that the QPSO improves on other versions of evolutionary algorithms in terms of both speed and accuracy. Compared to the original PSO, it greatly enhances the searching ability and efficiently manages system constraints. Then, fuzzy C-means (FCM) and QPSO are applied to train radial basis function (RBF) neural networks with the capacity to auto-configure the network structures and obtain the model parameters. The benchmark data sets test results suggest that the proposed training algorithms ensure good performance on data clustering, also improve training and generalization capabilities of RBF neural networks. Wavelet analysis has been widely used in signal estimation, classification, and compression. Denoising with traditional wavelet transforms always exhibits visual artefacts because of translation-variant. Furthermore, in most cases, wavelet denoising of real-time signals is actualized via offline processing which limits the efficacy of such real-time applications. In the present context, an online wavelet denoising method using a moving window technique is proposed. Problems that may occur in real-time wavelet denoising, such as border distortion and pseudo-Gibbs phenomena, are effectively solved by using window extension and window circle spinning methods. This provides an effective data pre-processing technique for the online application of other data analysis approaches. In a competitive electricity market, price forecasting is one of the essential functions required of a generation company and the system operator. It provides critical information for building up effective risk management plans by market participants, especially those companies that generate and retail electrical power. Here, an RBF neural network is adopted as a predictor of the electricity market regional reference price in the Australian national electricity market (NEM). Furthermore, the wavelet denoising technique is adopted to pre-process the historical price data. The promising network prediction performance with respect to price data demonstrates the efficiency of the proposed method, with real-time wavelet denoising making feasible the online application of the proposed price prediction method. Along with market deregulation, power system security assessment has attracted great concern from both academic and industry analysts, especially after several devastating blackouts in the USA, the UK, and Russia. This thesis goes on to propose an efficient composite method for cascading failure prevention comprising three major stages. Firstly, a hybrid method based on principal component analysis (PCA) and specific statistic measures is used to detect system faults. Secondly, the RBF neural network is then used for power network bus voltage profile prediction. Tests are carried out by means of the “N-1” and “N-1-1” methods applied in the New England power system through PSS/E dynamic simulations. Results show that system faults can be reliably detected and voltage profiles can be correctly predicted. In contrast to traditional methods involving phase calculation, this technique uses raw data from time domains and is computationally inexpensive in terms of both memory and speed for practical applications. This establishes a connection between power system fault analysis and cascading analysis. Finally, a multi-stage model predictive control (MPC) based load shedding scheme for ensuring power system voltage stability is proposed. It has been demonstrated that optimal action in the process of load shedding for voltage stability during emergencies can be achieved as a consequence. Based on above discussions, a framework for analysing power system voltage stability and ensuring its enhancement is proposed, with such a framework able to be used as an effective means of cascading failure analysis. In summary, the research reported in this thesis provides a composite framework for power system data analysis in a market environment. It covers advanced techniques of computational intelligence and machine learning, also proposes effective solutions for both the market operation and the system stability related problems facing today’s power industry.

A supercavitating vehicle, a next-generation underwater vehicle capable of changing the paradigm of modern marine warfare, exploits supercavitation as a means to reduce drag and achieve extremely high submerged speeds. In supercavitating flows, a low-density gaseous cavity entirely envelops the vehicle and as a result the vehicle is in contact with liquid water only at its nose and partially over the afterbody. Hence, the vehicle experiences a substantially reduced skin drag and can achieve much higher speed than conventional vehicles. The development of a controllable and maneuvering supercavitating vehicle has been confronted with various challenging problems such as the potential instability of the vehicle, the unsteady nature of cavity dynamics, the complex and non-linear nature of the interaction between vehicle and cavity. Furthermore, major questions still need to be resolved regarding the basic configuration of the vehicle itself, including its control surfaces, the control system, and the cavity dynamics. In order to answer these fundamental questions, together with many similar ones, this dissertation develops an integrated simulation-based design tool to optimize the vehicle configuration subjected to operational design requirements, while predicting the complex coupled behavior of the vehicle for each design configuration. Particularly, this research attempts to include maneuvering flight as well as various operating trim conditions directly in the vehicle configurational optimization. This integrated approach provides significant improvement in performance in the preliminary design phase and indicates that trade-offs between various performance indexes are required due to their conflicting requirements. This dissertation also investigates trim conditions and dynamic characteristics of supercavitating vehicles through a full 6 DOF model. The influence of operating conditions, and cavity models and their memory effects on trim is analyzed and discussed. Unique characteristics are identified, e.g. the cavity memory effects introduce a favorable stabilizing effect by providing restoring fins and planing forces. Furthermore, this research investigates the flight envelope of a supercavitating vehicle, which is significantly different from that of a conventional vehicle due to different hydrodynamic coefficients as well as unique operational conditions.

Fresh water resources in many of the world's oil producing regions, such as western Texas, are scarce, while produced water from oil wells is plentiful, though unfit for most applications due to high salinity and other contamination. Disposing of this water is a great expense to oil producers. This research seeks to advance a technology developed to treat produced water by reverse osmosis and other means to render it suitable for agricultural or industrial use, while simultaneously reducing disposal costs. Pilot testing of the process thus far has demonstrated the technology's capability to produce good-quality water, but process optimization and control were yet to be fully addressed and are focuses of this work. Also, the use of renewable resources (wind and solar) are analyzed as potential power sources for the process, and an overview of reverse osmosis membrane fouling is presented. A computer model of the process was created using a dynamic simulator, Aspen Dynamics, to determine energy consumption of various process design alternatives, and to test control strategies. By preserving the mechanical energy of the concentrate stream of the reverse osmosis membrane, process energy requirements can be reduced several fold from that of the current configuration. Process control schemes utilizing basic feedback control methods with proportional-integral (PI) controllers are proposed, with the feasibility of the strategy for the most complex process design verified by successful dynamic simulation. A macro-driven spreadsheet was created to allow for quick and easy cost comparisons of renewable energy sources in a variety of locations. Using this tool, wind and solar costs were compared for cities in regions throughout Texas. The renewable energy resource showing the greatest potential was wind power, with the analysis showing that in windy regions such as the Texas Panhandle, wind-generated power costs are approximately equal to those generated with diesel fuel.

Abstract Water is the essential resource of people for their livelihood and is the foundation for the economy to develop unceasingly. Based on the trend of economic growth, population growth, and the improvement of the quality of life, the demand for water is expected to continuously increase. When the demand for water resources is continuing to increase, burdens are added to the environment and ecology. The severe challenge for human beings is how to promote the sustainable development of economy, society, an ecological environment, and to achieve sustainable use of water resources. The Penghu Island has endured water scarcity for a long time. The government also takes great pains over the water resource problem. The purpose of this study is to establish indicators of a framework for the sustainable development of water resources in Penghu County. This can be used to construct a model of the system dynamics to conduct simulations of various scenarios. After that, we can understand the current situation and problems of the subject of water resources and sustainable development in Penghu County to provide suggestions for the government to make decisions. First, literature should be collected that relates to the indicators of a sustainable development system of water resources, adopting the D-S-R (Driving forces-State-Response¡^indicator framework proposed by the United Nations. We should draw up a water resource sustainable development indicators system which suits the characteristic of the native environment in Penghu County initially, estimating by using Delphi and AHP. Moreover, we will construct a model of the system dynamics and proceed to do the simulation of scenarios. There are 43 indicators in this study which built up the D-S-R water resources sustainable development indicator framework in Penghu County. They belong to 8 different assessment categories, which include watershed conservation and management, groundwater conservation and management, diverse use and development of water resources, allocation and management of water resources, drought and flood mitigation, promotion of water conservation measures, technology research and develop of water resources, personnel training and education about cherishing water resources. Among them, there are 14 driving force indicators, 14 state indicators, and 15 response indicators. According to the dynamic system model constructed in this research, the continuous increase of the population and number of tourists represent the social and economic development of the driving force aspect. When the groundwater is overdrawn, this causes the seawater to invade and it becomes salty. The State aspect is and the people's health and welfare. In the Response aspect, there are 4 strategies regarding the management scenarios, including the control of overdrawn groundwater, building a seawater desalination factory, rational water price adjustment, and the promotion of water conservation measures are drafted. According to the simulation and scenarios, some results were found, such as the rational water price adjustment and promotion of water conservation measures have a limited effect upon slowing down the groundwater overdraw because of the severe water resource shortage in Penghu County. The control of overdrawn groundwater can appropriately decelerate the groundwater being drawn excessively, but can't retard the rise in demand for water. Building a seawater desalination factory can satisfy the continuous rise in demand for water, and have the greatest effect on decelerating the aggravation of the water resource ecology and the quality of the water environment. The strategy of improving the shortage of water resources usually can be executed from two directions: water resources development and economization. This study found that the key points to overcome in order to achieve the sustainable development of water resources in Penghu County are mainly: the destruction of the ecological environment because of deep groundwater overdraw, and the negative influence of setting up a seawater desalination factory on marine ecology resources. The relation is very clear that deep groundwater overdraw causes seawater invasion and the result is salty water. But it's not clear whether the waste water produced from the seawater desalination factory will effect the rich marine resources of Penghu County. The residents, mainly fishermen, still have doubts about building a seawater desalination factory. There should be more thorough analysis and research.

Helicopters have now become an essential part for civil and military activities, for the next few years a significant increase in the use of this mean of transportation is expected. Unlike many fixed-wing aircraft, helicopters have no need to be pressurized due to their operating at low altitudes. The Environmental Control Systems (ECS) commonly used in fixed-wing aircraft are air cycle systems, which use the engine compressor’s bleed flow to function. These systems are integrated in the aircraft from inception. The ECS in helicopters is commonly added subsequently to an already designed airframe and power plant or as an additional development for modern aircraft. Helicopter engines are not designed to bleed air while producing their rated power, due to this a high penalty in fuel consumption is paid by such refitted systems. A detailed study of the different configurations of ECS for rotorcraft could reduce this penalty by determining the required power resulting from each of the system configurations, and therefore recommend the most appropriate one to be implemented for a particular flight path and aircraft. This study presents the conducted analysis and subsequent simulation of the environmental control system in a selected representative rotorcraft: the Bell206L-4. This investigation seeks to optimize the rotorcraft’s power consumption and energy waste; by taking into consideration the cabin heat load. It consequently aims to minimize these penalties, achieving passenger comfort, an optimally moist air for equipment and a reduction in the environmental impact. For the purpose of this analysis a civil aircraft was chosen for a rotary-wing type. This helicopter was analysed with different air-conditioning packs complying with the current airworthiness requirements. These systems were optimized with the inclusion of different environmental control models, and the cabin heat load model, which provided the best air-conditioning for many conditions and mission scopes, thus reducing the high fuel consumption in engines and hence the emission of gases into the environment. Each of the models was computed in the Matlab-simulink® software. Different case studies were carried out by changing aircraft, the system’s configurations and flight parameters. Comparisons between the different systems and sub-systems were performed. The results of these simulations permitted the ECS configuration selection for optimal fuel consumption. Once validated the results obtained through this model were included in Rotorcraft Mission Energy Management Model (RMEM), a tool designed to predict the power requirements of helicopter systems. The computed ECS model shows that favourable reductions in fuel burn may be achievable if an appropriated configuration of ECS is chosen for a light rotorcraft. The results show that the VCM mixed with engine bleed air is the best configuration for the chosen missions. However, this configuration can vary according to the mission and environment.

The power industry has undergone significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structures have been reformed into competitive markets in pursuit of increased efficiency in electricity production and utilization. However, along with market deregulation, power systems presently face severe challenges. One is power system stability, a problem that has attracted widespread concern because of severe blackouts experienced in the USA, the UK, Italy, and other countries. Another is that electricity market operation warrants more effective planning, management, and direction techniques due to the ever expanding large-scale interconnection of power grids. Moreover, many exterior constraints, such as environmental protection influences and associated government regulations, now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more advanced power system data analysis methods are required in the deregulated, market-oriented environment. At the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. In this thesis, the reported research is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: a newly proposed quantum-inspired particle swarm optimization and self-adaptive learning scheme for radial basis function neural networks; online wavelet denoising techniques; electricity regional reference price forecasting methods in the electricity market; and power system security assessment approaches for deregulated markets, including fault analysis, voltage profile prediction under contingencies, and machine learning based load shedding scheme for voltage stability enhancement. Evolutionary algorithms (EAs) inspired by biological evolution mechanisms have had great success in power system stability analysis and operation planning. Here, a new quantum-inspired particle swarm optimization (QPSO) is proposed. Its inspiration stems from quantum computation theory, whose mechanism is totally different from those of original EAs. The benchmark data sets and economic load dispatch research results show that the QPSO improves on other versions of evolutionary algorithms in terms of both speed and accuracy. Compared to the original PSO, it greatly enhances the searching ability and efficiently manages system constraints. Then, fuzzy C-means (FCM) and QPSO are applied to train radial basis function (RBF) neural networks with the capacity to auto-configure the network structures and obtain the model parameters. The benchmark data sets test results suggest that the proposed training algorithms ensure good performance on data clustering, also improve training and generalization capabilities of RBF neural networks. Wavelet analysis has been widely used in signal estimation, classification, and compression. Denoising with traditional wavelet transforms always exhibits visual artefacts because of translation-variant. Furthermore, in most cases, wavelet denoising of real-time signals is actualized via offline processing which limits the efficacy of such real-time applications. In the present context, an online wavelet denoising method using a moving window technique is proposed. Problems that may occur in real-time wavelet denoising, such as border distortion and pseudo-Gibbs phenomena, are effectively solved by using window extension and window circle spinning methods. This provides an effective data pre-processing technique for the online application of other data analysis approaches. In a competitive electricity market, price forecasting is one of the essential functions required of a generation company and the system operator. It provides critical information for building up effective risk management plans by market participants, especially those companies that generate and retail electrical power. Here, an RBF neural network is adopted as a predictor of the electricity market regional reference price in the Australian national electricity market (NEM). Furthermore, the wavelet denoising technique is adopted to pre-process the historical price data. The promising network prediction performance with respect to price data demonstrates the efficiency of the proposed method, with real-time wavelet denoising making feasible the online application of the proposed price prediction method. Along with market deregulation, power system security assessment has attracted great concern from both academic and industry analysts, especially after several devastating blackouts in the USA, the UK, and Russia. This thesis goes on to propose an efficient composite method for cascading failure prevention comprising three major stages. Firstly, a hybrid method based on principal component analysis (PCA) and specific statistic measures is used to detect system faults. Secondly, the RBF neural network is then used for power network bus voltage profile prediction. Tests are carried out by means of the “N-1” and “N-1-1” methods applied in the New England power system through PSS/E dynamic simulations. Results show that system faults can be reliably detected and voltage profiles can be correctly predicted. In contrast to traditional methods involving phase calculation, this technique uses raw data from time domains and is computationally inexpensive in terms of both memory and speed for practical applications. This establishes a connection between power system fault analysis and cascading analysis. Finally, a multi-stage model predictive control (MPC) based load shedding scheme for ensuring power system voltage stability is proposed. It has been demonstrated that optimal action in the process of load shedding for voltage stability during emergencies can be achieved as a consequence. Based on above discussions, a framework for analysing power system voltage stability and ensuring its enhancement is proposed, with such a framework able to be used as an effective means of cascading failure analysis. In summary, the research reported in this thesis provides a composite framework for power system data analysis in a market environment. It covers advanced techniques of computational intelligence and machine learning, also proposes effective solutions for both the market operation and the system stability related problems facing today’s power industry.

In dieser Arbeit werden neuartige Flachrohre für die Verwendung als Rückwandverflüssiger in der Haushaltskältetechnik mit numerischen und dynamischen Simulationen sowie Experimenten untersucht. Dabei kommen unterschiedliche überströmte Längen sowie der Einfluss horizontaler Abstände auf den Wärmeübergang durch freie Konvektion zur Betrachtung. Realisiert wird die numerische Strömungssimulation mit der Software Fluent 3.6.26, wobei das RNG-k-epsilon- als Turbulenzmodell und diskrete Ordinaten zur zusätzlichen Modellierung des Strahlungswärmeübergangs verwendet werden. Zur Verifizierung werden experimentelle Untersuchungen mit natürlicher Konvektion durchgeführt. Ebenso kommt ein kompakter Verflüssiger bei erzwungener Konvektion zur experimentellen Analyse. Mit einem neuen Verflüssigermodell wird außerdem ein Haushaltskühlschrank in Modelica 2.2.1 dynamisch simuliert. Diese Arbeit zeigt, dass die Verwendung eines Flachrohrverflüssigers großes Potenzial einer konkurrenzfähigen Alternative zu konventionellen Verflüssigern besitzt. / In this work novel flat tubes used as rear panel condensers in the household refrigeration technology are examined with numerical and dynamic simulations as well as experiments. Therefore different overflowed lengths and the influence of horizontal spacing on the heat transfer by free convection are taken into consideration. The CFD calculations are realized with the software Fluent 3.6.26, where the RNG-k-epsilon turbulence model and discrete ordinates for an additional modelling of radiation heat transfer are applied. For the verification, experimental studies with natural convection are carried out. Likewise, a compact condenser is experimentally analysed in forced convection. With a new model for the liquefier a domestic refrigerator is also dynamically simulated in Modelica 2.2.1. This work shows that the use of a flat tube condenser has a great potential of a competitive alternative to conventional liquefiers.

L’ADN, support de l’information génétique, est constamment altéré par des agents physiques ou chimiques d’origines endogènes (métabolisme) et exogènes (UV, radiations ionisantes, produits chimiques) dont les effets sont génotoxiques. Ces modifications structurales délétères de l’ADN sont éliminées par de nombreux mécanismes de réparation. Parmi eux, le système de réparation par excision de bases (BER) est initié par les ADN glycosylases qui reconnaissent et éliminent les bases endommagées. Dans certaines stratégies anti-cancéreuses, l’utilisation de la chimiothérapie et la radiothérapie ont pour but la destruction des cellules cancéreuses en altérant leur ADN. Dans ce contexte, les ADN glycosylases réparent l’ADN des cellules traitées et induisent une résistance non désirée au traitement, faisant de ces enzymes des cibles thérapeutiques intéressantes. Le but de ces travaux est d’approfondir la compréhension des mécanismes de réparation des ADN glycosylases de la superfamille structurale Fpg/Nei grâce à la modélisation moléculaire et de pouvoir identifier et concevoir des inhibiteurs de ces enzymes. Les simulations de dynamique moléculaire (DM) nous ont permis d’étudier la « Lesion Capping Loop » (LCL) et de l’associer à la stabilisation de la base endommagée positionnée dans le site actif. Nous avons également étudié les chemins de sortie possibles de la base après coupure par l’enzyme et l’implication de la boucle LCL dans ce phénomène grâce à des simulations de DM ciblée (TMD-1). De plus, les simulations de DM couplées à un protocole d’amarrage moléculaire « aveugle » nous ont permis d’identifier 2 sites de fixations possibles majoritaires pour des petites molécules potentiellement inhibitrices. Un de ces sites correspondant au site actif de hNEIL1 a fait l’objet d’un criblage virtuel d’une partie de la base de molécules Ambinter. Ceci nous a permis d’identifier des molécules potentiellement inhibitrices dont les effets seront prochainement testés in vitro dans l’équipe sur la protéine humaine hNeil1. / The DNA, genetic information support, is frequently damaged by physical or chemical agents from endogenous (cell metabolism) and exogenous (UV, ionizing radiations, chemicals) factors whose effects are genotoxic. These deleterious DNA structural alterations are removed by many DNA repair mechanisms. Among them, the base excision repair (BER) is initiated by DNA glycosylases which recognize and remove damaged bases. In some anti-cancer strategies, the use of chemo- and radiotherapy is aimed to cancerous cells destruction by altering their DNA. In that specific context, DNA glycosylases repair the DNA of treated cells and induce unwanted resistance to treatments, making these enzymes interesting therapeutic targets. The purpose of this work is to deepen the repair mechanism knowledge of Fpg/Nei structural superfamily of DNA glycosylases using molecular modeling and designing inhibitors of these enzymes. Molecular dynamic simulations allowed us to study the « Lesion Capping Loop » (LCL) and to associate its role to substrate stabilization in the enzyme active site. We also studied some possible excision’s product release pathways and LCL implication in this phenomena by targeted molecular dynamic simulations (TMD-1). Furthermore, molecular dynamic simulations coupled to a blind molecular docking protocol allowed us to identify 2 possible main binding sites of potential inhibitiors. One of these binding sites corresponding to the hNEIL1 active site has been the object of a virtual screening of the Greenpharma database. This allowed us to identify potential inhibitors whom effects will be soon tested in vitro on the humain protein hNEIL1.

Les risques sont inhérents à l’activité industrielle. Les prévoir et les maîtriser sont essentiels pour la conception et la conduite en sécurité des procédés. La réglementation des risques majeurs impose aux exploitants la réalisation d’études de sécurité quantitatives. La stratégie de maîtrise des risques repose sur la pertinence des analyses de risques. En marche dégradée, la dynamique des événements est déterminante pour quantifier les risques. Toutefois, de nos jours cette connaissance est difficilement accessible. Ce travail propose une méthodologie d’analyse de risques quantitative qui combine la méthode HAZOP, le retour d’expérience et la simulation dynamique de dérives de procédés. Elle repose sur quatre grandes étapes : La première étape est l’étude du fonctionnement normal du procédé. Pour cela, le procédé est décrit de façon détaillée. Des études complémentaires de caractérisation des produits et du milieu réactionnel sont menées si nécessaires. Ensuite, le procédé est simulé dynamiquement en fonctionnement normal. Lors de la seconde étape, parmi les dérives définies par l’HAZOP et le retour d’expérience, l’analyste discrimine celles dont les conséquences ne sont pas prévisibles et/ou nécessitent d’être quantifiées. La troisième phase fournit une quantification du risque sur la base de la simulation dynamique des scenarii retenus. Lors de la dernière étape, des mesures de maîtrise des risques sont définies et ajoutées au procédé lorsque le niveau de risque est supérieur au risque tolérable. Le risque résiduel est ensuite calculé jusqu’à l’atteinte de la cible sécurité. Le logiciel Aspen Plus Dynamics est sélectionné. Trois études de cas sont choisies pour démontrer d’une part, la faisabilité de la méthodologie et d’autre part, la diversité de son champ d’application : · la première étude de cas porte sur un réacteur semi-continu siège d’une réaction exothermique. L’oxydation du thiosulfate de sodium par le peroxyde d’hydrogène est choisie. Ce cas relativement simple permet d’illustrer la diversité des causes pouvant être simulées (erreur procédurale, défaut matériel, contamination de produits, …) et la possibilité d’étudier des dérives simultanées (perte de refroidissement du milieu et sous dimensionnement de la soupape de sécurité). · le deuxième cas concerne un réacteur semi-batch dans lequel une réaction exothermique de sulfonation est opérée. Elle est particulièrement difficile à mettre en œuvre car le risque d’emballement thermique est élevé. Cette étude montre l’intérêt de notre approche dans la définition des conditions opératoires pour la conduite en sécurité. · le troisième cas d’étude porte sur un procédé continu de fabrication du propylène glycol composé d’un réacteur et de deux colonnes de distillation en série. L’objectif est ici d’étudier la propagation de dérives le long du procédé. Sur la base du retour d’expérience, deux dérives au niveau du rebouilleur de la première colonne sont étudiées et illustrent les risques de pleurage et d’engorgement. La simulation dynamique illustre la propagation d’une dérive et ses conséquences sur la colonne suivante. / Risks are inherent to industrial activity. Predicting and controlling them is essential to the processes design and safe operation. Quantitative safety studies are imposed by the major hazard regulations. The risk management strategy relies on the relevance of risk analyzes. In degraded conditions, the dynamics of events are decisive for risks quantification. However, nowadays this knowledge is a real challenge. This work proposes a methodology of quantitative risk analysis, which combines the HAZOP method, the lessons learned from previous accidents and the dynamic simulation of process deviations. It is based on four main stages: The first stage is the study of the process normal operation. For this, the process is described in detail. Additional studies to characterize the products and the reaction are carried out if necessary. Then, the process is dynamically simulated in normal operation conditions. During the second step, among all the deviations defined by the HAZOP and lessons learned, the analyst discriminates those whose consequences are not predictable and/or need to be quantified. The third phase provides a risk quantification based on the dynamic simulation of the selected scenarios. In the last step, safety barriers are defined and added to the process when the risk level is greater than the tolerable risk. The residual risk is then calculated until the safety target is reached. Aspen Plus Dynamics software is selected. Three case studies are chosen in order to demonstrate, on the one hand, the feasibility of the methodology and, on the other hand, the diversity of its scope: · the first case study is a semi-continuous reactor with an exothermic reaction study. The oxidation of sodium thiosulfate by hydrogen peroxide is selected. This relatively simple case illustrates the diversity of causes that can be simulated (procedural error, material defect, product contamination …) and the possibility of studying simultaneous deviations (loss of cooling and under sized safety valve for example). · the second case concerns a semi-batch reactor in which an exothermic reaction of sulphonation is carried out. This reaction is particularly difficult to conduct because of the thermal runaway high risk. This study shows our approach’s interest in the definition of the operating conditions for safe operation. · the third case study concerns a continuous process of propylene glycol production. It is composed of a reactor and two distillation columns in series. The objective is to study the propagation of deviations along the process. Based on lessons learned, two deviations in the first column reboiler are studied and illustrate the flooding and weeping risks. Dynamic simulation illustrates the propagation of a deviation and its consequences on the second column