Adaptation Of Mathematical Model Research Articles

Development of a novel pyrolysis-gas chromatography/mass spectrometry method for the analysis of poly(lactic acid) thermal degradation products

Thermal degradation of PLA is a complex process since it comprises many simultaneous reactions. The use of analytical techniques, such as differential scanning calorimetry (DSC) and thermogravimetry (TGA), yields useful information but a more sensitive analytical technique would be necessary to identify and quantify the PLA degradation products. In this work the thermal degradation of PLA at high temperatures was studied by using a pyrolyzer coupled to a gas chromatograph with mass spectrometry detection (Py-GC/MS). Pyrolysis conditions (temperature and time) were optimized in order to obtain an adequate chromatographic separation of the compounds formed during heating. The best resolution of chromatographic peaks was obtained by pyrolyzing the material from room temperature to 600°C during 0.5s. These conditions allowed identifying and quantifying the major compounds produced during the PLA thermal degradation in inert atmosphere. The strategy followed to select these operation parameters was by using sequential pyrolysis based on the adaptation of mathematical models. By application of this strategy it was demonstrated that PLA is degraded at high temperatures by following a non-linear behaviour. The application of logistic and Boltzmann models leads to good fittings to the experimental results, despite the Boltzmann model provided the best approach to calculate the time at which 50% of PLA was degraded. In conclusion, the Boltzmann method can be applied as a tool for simulating the PLA thermal degradation.

Model-based diagnosis of systems emphasizing a holistic approach

Diagnosis of technical systems is very important both from the technical and economic point of view. Monitoring and diagnosis start with the observation of symptoms, which depend on the fast time dynamics and on the system's evolution based on the slow time coordinate. Model-based diagnosis can be performed by symptoms directly and by a holistic mathematical model of the entire system decomposed into subsystems, whereas the submodels of the related subsystems can be of different accuracies dependent on their goals. If these models are adjusted at every lifetime required for condition assessment of the operating system/system in service, and if the models are verified and validated, then they represent the best knowledge base available for diagnosis. Adaptation of mathematical models uses system identification tools which can produce verified, validated and usable mathematical models. These models serve for fault detection, localization, assessment and cause finding, for trend prediction and decisionmaking for further use of the system. Consequently, the procedure described combined with an extended knowledge base of the limit states etc. of the system can be seen as an intelligent diagnostic method.