Nadia Baati
Industrial chemical processes may involve thermal risks as most of the reactions performed are exothermic, the chemicals used are often thermally unstable, and the operating conditions are set to induce high conversion and throughput. Besides the reactive steps, all operations from mixing to storage and from processing to transport involving sensitive chemicals should be conducted under strictly controlled conditions ensuring safe operations. Performing an efficient risk assessment and implementing the proper risk mitigation measures are essential to avoid, or at least reduce, accidents and their potentially disastrous consequences. For optimal design and implementation of safety measures, it is important that these considerations are taken into account at early stages of process development. The required data should be made available at the appropriate time so it can be properly accounted for and efficiently serve the design. Yet, at early design phases, some information may be unavailable due to several reasons: the process design being still in development, some parameters can be unknown; experimental analysis of chemicals could be hindered or impossible due to products availability in required quantities; several alternatives are under investigation which raises the necessary resources (time and material) for experimental tests. Predictions would be the appropriate response to such scenario. The aim of this dissertation is to develop predictive models for two hazardous behaviors of chemicals: explosive sensitivity and thermal stability. For the models to be applicable at early development phases, it is preferable to minimize the information feed requirements, and therefore, structure-based approaches are applied. Two methods were identified: Quantitative Structure-Property Relationships (QSPR) and Group Contributions Method (GCM). The hazardous behaviors are studied through characteristic measurements: the Minimal Ignition Energy (MIE) to represent explosive sensitivity and Differential Scanning Calorimetry (DSC) for thermal stability. These measurements are widely employed in safety studies and deliver necessary information to identify potential hazards. Moreover, their specificities call for predictive models: MIE tests require repetitive analysis and hence are time and material consuming; regarding DSC experiments, experts have noted that they seem to exhibit structurally dependent features, and so far no study has comprehensively investigated this phenomenon. This work presents in a first part the structure-based approaches that are applied and the elements of Data Analysis necessary for developing predictive models and simulating experimental results. Secondly, both experimental analysis are detailed and the important information our models should be able to represent will be exposed. Finally, the third and last part is dedicated to the applications: the obtained predictive models are presented, evaluated and discussed. Most of the initial objectives are met as efficient solutions are proposed, nonetheless, some improvement strategies may also be considered.
EPFL2016
