Quantified Uncertainty in Thermodynamic Modeling for Materials Design

Phase fractions, compositions and energies of the stable phases as a function of macroscopic composition, temperature, and pressure (X-T-P) are the principle correlations needed for the design of new materials and improvement of existing materials. They are the outcomes of thermodynamic modeling based on the CALculation of PHAse Diagrams (CALPHAD) approach. The accuracy of CALPHAD predictions vary widely in X-T-P space due to experimental error, model inadequacy and unequal data coverage. In response, researchers have developed frameworks to quantify the uncertainty of thermodynamic property model parameters and propagate it to phase diagram predictions. In previous studies, uncertainty was represented as intervals on phase boundaries (with respect to composition) or invariant reactions (with respect to temperature) and was unable to represent the uncertainty in eutectoid reactions or in the stability of phase regions. In this work, we propose a suite of tools that leverages samples from the multivariate model parameter distribution to represent uncertainty in forms that surpass previous limitations and are well suited to materials design. These representations include the distribution of phase diagrams and their features, as well as the dependence of phase stability and the distributions of phase fraction, composition activity and Gibbs energy on X-T-P location - irrespective of the total number of components. Most critically, the new methodology allows the material designer to interrogate a certain composition and temperature domain and get in return the probability of different phases to be stable, which can positively impact materials design

Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

Dynamic modelling of aqueous two-phase systems to quantify the impact of bioprocess design, operation and variability

Aqueous two-phase extraction (ATPE) is a promising downstream separation technology as an alternative, or addition, to chromatography in the production of biological products. Increasing demand for therapeutic proteins have triggered manufacturers to consider continuous upstream technologies to achieve greater process efficiencies; however, such technologies have an inherent variability, resulting in output streams of varying compositions and properties. It is therefore important to understand how this variability impacts on the downstream separation processes. Exploring all potential sources of variability is challenging due to resource and time constraints, however, the use of targeted mathematical modelling can significantly reduce the need for expensive and time consuming experimentation. In this work, we present a dynamic equilibrium stage process model, and a methodology for prediction of key process parameters from limited experiments, capable of describing ATPE separations under both multi-cycle batch and continuous counter-current modes of operation. The capabilities of the proposed methodology are demonstrated using a case study separation of the enzyme α-amylase from impurities in a PEG 4000–phosphate aqueous two phase system (ATPS) containing NaCl. The model can be used to predict the separation performance of the process, as well as for the investigation of suitable design and operating conditions

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

The certification of the mass fraction of carbon in cementite grains in a Fe-C matrix: IRMM-471

The report describes the production and certification of IRMM-471, a reference material certified for the carbon mass fraction of its cementite (Fe3C) grains. The Fe3C grains are dispersed within an iron pearlite matrix and present an average grain diameter between 20 µm and 50 μm. IRMM-471 has been produced and certified in order to be used as calibrant in electron probe micro-analyser (EPMA) for carbon determination in iron and steel products.JRC.D.2-Standards for Innovation and sustainable Developmen