Kinetic Parameter Estimation from Spectroscopic Data for a Multi-Stage Solid-Liquid Pharmaceutical Process

Laboratory and process measurements from spectroscopic instruments are ubiquitous in pharma processes, and directly using the data can pose a number of challenges for kinetic model building. Moreover, scaling up from laboratory to industrial level requires predictive models with accurate parameter values. This means that process identification implies not only kinetic parameter estimation but also the identification of the absorbing species and estimation of variances for both the data and parameters. The recently developed, open-source toolkit KIPET (Short, M.; Schenk, C.; Thierry, D.; Rodriguez, J. S.; Biegler, L. T.; Garcı́a-Muñoz, S. Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, 2019, 47, 299; Schenk, C.; Short, M.; Thierry, D.; Rodriguez, J. S.; Biegler, L. T.; Garcı́a-Muñoz, S.; Chen, W. Comput. Chem. Eng.2020, 134, 106716) addresses these topics and provides an alternative to standard parameter estimation packages, in particular for spectroscopic data problems. Moreover, batch processes commonly used in the chemical and pharmaceutical industries involve multiple stages to carry out synthesis operations in a step-by-step manner, often dealing with heterogeneous mixtures, wide operating temperatures, and constant additions and removals of product and waste. For such cases novel modeling approaches are required, as the structure of the kinetic model may vary with time, with model switches that are state dependent. This study presents a new modeling approach and methodology that deals with these practical issues. In developing kinetic models, it approximates the solid dissolution process and deals with multiple stages with different reactor temperatures. Moreover, variances, parameters, concentration, and absorbance profiles are estimated for the process stages using the approach presented by Chen et al. (Chen, W.; Biegler, L. T.; Garcı́a Muñoz, S. J. Chemom.2016, 30, 506). The application of these developed concepts results in realistic profiles as well as reliable kinetic parameter values. The outcomes of this work show that KIPET is a useful toolkit for dealing with pharmaceutical processes with capabilities for dealing with challenging kinetic modeling problems.Funded by Pfizer Inc

3-D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle: III. Thermochemical tomography in the Western-Central U.S.

Acknowledgments We are indebted to F. Darbyshire and J. von Hunen for useful comments on earlier versions of this work. This manuscript benefited from thorough and constructive reviews by W. Levandowski and an anonymous reviewer. We also thank J. Connolly, M. Sambridge, B. Kennett, S. Lebedev, B. Shan, U. Faul, and M. Qashqai for insightful discussions about, and contributions to, some of the concepts presented in this paper. The work of J.C.A. has been supported by two Australian Research Council Discovery grants (DP120102372 and DP110104145). Seismic data are from the IRIS DMS. D.L.S. acknowledges support from NSF grant EAR-135866. This is contribution 848 from the ARC Centre of Excellence for Core to Crust Fluid Systems (http://www.ccfs.mq.edu.au) and 1106 in the GEMOC Key Centre (http://www.gemoc.mq.edu.au).Peer reviewedPublisher PD

A Description of Multiscale Modeling for the Head-Disk Interface Focusing on Bottom-Level Lubricant and Carbon Overcoat Models

The challenges in designing future head disk interface (HDI) demand efficient theoretical modeling tools with flexibility in investigating various combinations of perfluoropolyether (PFPE) and carbon overcoat (COC) materials. For broad range of time and length scales, we developed multiscale/multiphysical modeling approach, which can bring paradigm-shifting improvements in advanced HDI design. In this paper, we introduce our multiscale modeling methodology with an effective strategic framework for the HDI system. Our multiscale methodology in this paper adopts a bottom to top approach beginning with the high-resolution modeling, which describes the intramolecular/intermolecular PFPE-COC degrees of freedom governing the functional oligomeric molecular conformations on the carbon surfaces. By introducing methodology for integrating atomistic/molecular/mesoscale levels via coarse-graining procedures, we investigated static and dynamic properties of PFPE-COC combinations with various molecular architectures. By bridging the atomistic and molecular scales, we are able to systematically incorporate first-principle physics into molecular models, thereby demonstrating a pathway for designing materials based on molecular architecture. We also discussed future materials (e.g., graphene for COC, star-like PFPEs) and systems (e.g., heat-assisted magnetic recording (HAMR)) with higher scale modeling methodology, which enables the incorporation of molecular/mesoscale information into the continuum scale models

Accurate solution of Bayesian inverse uncertainty quantification problems combining reduced basis methods and reduction error models

Computational inverse problems related to partial differential equations (PDEs) often contain nuisance parameters that cannot be effectively identified but still need to be considered as part of the problem. The objective of this work is to show how to take advantage of a reduced order framework to speed up Bayesian inversion on the identifiable parameters of the system, while marginalizing away the (potentially large number of) nuisance parameters. The key ingredients are twofold. On the one hand, we rely on a reduced basis (RB) method, equipped with computable a posteriori error bounds, to speed up the solution of the forward problem. On the other hand, we develop suitable reduction error models (REMs) to quantify in an inexpensive way the error between the full-order and the reduced-order approximation of the forward problem, in order to gauge the effect of this error on the posterior distribution of the identifiable parameters. Numerical results dealing with inverse problems governed by elliptic PDEs in the case of both scalar parameters and parametric fields highlight the combined role played by RB accuracy and REM effectivity

Implementation of an innovative teaching project in a Chemical Process Design course at the University of Cantabria, Spain

This paper shows the planning, the teaching activities and the evaluation of the learning and teaching process implemented in the Chemical Process Design course at the University of Cantabria, Spain. Educational methods to address the knowledge, skills and attitudes that students who complete the course are expected to acquire are proposed and discussed. Undergraduate and graduate engineers' perceptions of the methodology used are evaluated by means of a questionnaire. Results of the teaching activities and the strengths and weaknesses of the proposed case study are discussed in relation to the course characteristics. The findings of the empirical evaluation shows that the excessive time students had to dedicate to the case study project and dealing with limited information are the most negative aspects obtained, whereas an increase in the students' self-confidence and the practical application of the methodology are the most positive aspects. Finally, improvements are discussed in order to extend the application of the methodology to other courses offered as part of the chemical engineering degree.This work was partially supported with the financial help of the University of Cantabria, 1st and 2nd Teaching Innovation Programs 2011-2012, 2013-2014, Projects Innodesign 1 and 2

Graph-Controlled Insertion-Deletion Systems

In this article, we consider the operations of insertion and deletion working in a graph-controlled manner. We show that like in the case of context-free productions, the computational power is strictly increased when using a control graph: computational completeness can be obtained by systems with insertion or deletion rules involving at most two symbols in a contextual or in a context-free manner and with the control graph having only four nodes.Comment: In Proceedings DCFS 2010, arXiv:1008.127

Siderite micro-modification for enhanced corrosion protection

Production of oil and gas results in the creation of carbon dioxide (CO₂) which when wet is extremely corrosive owing to the speciation of carbonic acid. Severe production losses and safety incidents occur when carbon steel (CS) is used as a pipeline material if corrosion is not properly managed. Currently corrosion inhibitor (CI) chemicals are used to ensure that the material degradation rates are properly controlled; this imposes operational constraints, costs of deployment and environmental issues. In specific conditions, a naturally growing corrosion product known as siderite or iron carbonate (FeCO₃) precipitates onto the internal pipe wall providing protection from electrochemical degradation. Many parameters influence the thermodynamics of FeCO₃ precipitation which is generally favoured at high values of temperatures, pressure and pH. In this paper, a new approach for corrosion management is presented; micro-modifying the corrosion product. This novel mitigation approach relies on enhancing the crystallisation of FeCO₃ and improving its density, protectiveness and mechanical properties. The addition of a silicon-rich nanofiller is shown to augment the growth of FeCO₃ at lower pH and temperature without affecting the bulk pH. The hybrid FeCO₃ exhibits superior general and localised corrosion properties. The findings herein indicate that it is possible to locally alter the environment in the vicinity of the corroding steel in order to grow a dense and therefore protective FeCO₃ film via the incorporation of hybrid organic-inorganic silsesquioxane moieties. The durability and mechanical integrity of the film is also significantly improved

Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control

This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and potentially nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a generic, spatial branch-and-bound algorithm. A new operation, called lifting, is introduced, which refines the control parameterization via a Gram-Schmidt orthogonalization process, while simultaneously eliminating control subregions that are either infeasible or that provably cannot contain any global optima. Conditions are given under which the image of the control parameterization error in the state space contracts exponentially as the parameterization order is increased, thereby making the lifting operation efficient. A computational technique based on ellipsoidal calculus is also developed that satisfies these conditions. The practical applicability of branch-and-lift is illustrated in a numerical example. © 2013 Springer Science+Business Media New York

Quantum mechanical studies of lincosamides

Lincosamides are a class of antibiotics used both in clinical and veterinary practice for a wide range of pathogens. This group of drugs inhibits the activity of the bacterial ribosome by binding to the 23S RNA of the large ribosomal subunit and blocking protein synthesis. Currently, three X-ray structures of the ribosome in complex with clindamycin are available in the Protein Data Bank, which reveal that there are two distinct conformations of the pyrrolidinyl propyl group of the bound clindamycin. In this work, we used quantum mechanical methods to investigate the probable conformations of clindamycin in order to explain the two binding modes in the ribosomal 23S RNA. We studied three lincosamide antibiotics: clindamycin, lincomycin, and pirlimycin at the B3LYP level with the 6-31G** basis set. The focus of our work was to connect the conformational landscape and electron densities of the two clindamycin conformers found experimentally with their physicochemical properties. For both functional conformers, we applied natural bond orbital (NBO) analysis and the atoms in molecules (AIM) theory, and calculated the NMR parameters. Based on the results obtained, we were able to show that the structure with the intramolecular hydrogen bond C=O…H–O is the most stable conformer of clindamycin. The charge transfer between the pyrrolidine-derivative ring and the six-atom sugar (methylthiolincosamide), which are linked via an amide bond, was found to be the dominant factor influencing the high stability of this conformer