Integrated Model-Centric Decision Support System for Process Industries

To bring the advances in modeling, simulation and optimization environments (MSOEs), open-software architectures, and information technology closer to process industries, novel mechanisms and advanced software tools must be devised to simplify the definition of complex model-based problems. Synergistic interactions between complementary model-based software tools must be refined to unlock the potential of model-centric technologies in industries. This dissertation presents the conceptual definition of a single and consistent framework for integrated process decision support (IMCPSS) to facilitate the realistic formulation of related model-based engineering problems. Through the integration of data management, simulation, parameter estimation, data reconciliation, and optimization methods, this framework seeks to extend the viability of model-centric technologies within the industrial workplace. The main contribution is the conceptual definition and implementation of mechanisms to ease the formulation of large-scale data-driven/model-based problems: data model definitions (DMDs), problem formulation objects (PFOs) and process data objects (PDOs). These mechanisms allow the definition of problems in terms of physical variables; to embed plant data seamlessly into model-based problems; and to permit data transfer, re-usability, and synergy among different activities. A second contribution is the design and implementation of the problem definition environment (PDE). The PDE is a robust object-oriented software component that coordinates the problem formulation and the interaction between activities by means of a user-friendly interface. The PDE administers information contained in DMD and coordinates the creation of PFOs and PIFs. Last, this dissertation contributes a systematic integration of data pre-processing and conditioning techniques and MSOEs. The proposed process data management system (pDMS) implements such methodologies. All required manipulations are supervised by the PDE, which represents an important advantage when dealing with high volumes of data. The IMCPSS responds to the need for software tools centered in process engineers for which the complexity of using current modeling environments is a barrier for broader application of model-based activities. Consequently, the IMCPSS represents a valuable tool for process industries, as the facilitation of problem formulation is translated into incorporation of plant data in less error-prone manner, maximization of time dedicated to the analysis of processes, and exploitation of synergy among activities based on process models

Verification, Validation, and Solution Quality in Computational Physics: CFD Methods Applied to Ice Sheet Physics

Procedures and methods for veri.cation of coding algebra and for validations of models and calculations used in the aerospace computational fluid dynamics (CFD) community would be ef.cacious if used by the glacier dynamics modeling community. This paper presents some of those methods, and how they might be applied to uncertainty management supporting code veri.cation and model validation for glacier dynamics. The similarities and differences between their use in CFD analysis and the proposed application of these methods to glacier modeling are discussed. After establishing sources of uncertainty and methods for code veri.cation, the paper looks at a representative sampling of veri.cation and validation efforts that are underway in the glacier modeling community, and establishes a context for these within an overall solution quality assessment. Finally, a vision of a new information architecture and interactive scienti.c interface is introduced and advocated

Identification of Material Parameters from Temperature Measurements in Radio Frequency Ablation

The mathematical simulation of the method of radio frequency ablation (RFA) offers an opportunity to improve the success of the RFA. The results of the RFA depend highly on the experience of the radiologist. A simulation will offer a prediction of the results which can be used to adapt the setting and enable a complete destruction of the tumor, e.g. by adapting the probe's position. A good simulation needs as much information of the reality as possible. Especially the material properties pose a challenge since they vary from patient to patient, they can not be measured in vivo and they additionally change during the ablation. The aim of this thesis is to develop a mathematical model for the identification of the material parameters from temperature measurements and apply it to appropriate data sets. At first a minimization problem is formulated, where the difference between the measured temperature and the calculated temperature is minimized with respect to the material parameters. The temperature distribution is calculated with a coupled system of partial differential equations. Different approaches are considered which depend on the diverse modeling of the material parameters. The parameters are modeled as constant values as well as temperature dependent, tissue dependent and also spatially distributed. The advantages and disadvantages of the diverse models are illustrated by the numerical results for the identification with artificial temperature distributions as well as real temperature measurements

PDE–Based Modelling and Control Strategies for Manufacturing Processes

This work aims to design boundary control strategies to solve demand tracking and backlog problems for manufacturing systems in terms of conservation laws coupled with ODEs in different network topologies. The OCPs are investigated in the dispersing and the merging networks. The problems are optimized utilizing open-loop optimal control based on the direct and the indirect approaches. The proposed approaches enable the solution of the OCPs. All of the approaches, in general, reach a local minima with similar behaviour that leads to the steady-state. The results analysis reveals that each method has its own distinct characteristics. The indirect methodology is characterized by excellent accuracy and minimal processing burden; yet, due to the information necessary to compute the gradient, it is a sensitive method. The ease of use and flexibility to any problem distinguishes the direct method. However, this approach takes substantially longer to achieve a solution when compared to the indirect method. Also, the AMPC was introduced to investigate demand tracking and backlog problems in the context of the complex network of production systems. The addressed network includes structures that are dispersing and merging. Furthermore, the appropriate way to handle the parameters of the AMPC for both control and prediction horizons is addressed. Moreover, the proposed AMPC provides for the solutions of demand tracking and backlog problems. In general, AMPC and traditional MPC attain local minima with similar behaviour that leads to steady-state convergence. When compared to a typical MPC, the AMPC's performance shows a considerable reduction in computational time. Additionally, because it provides a mathematical insight into the method's structure, the AMPC allows for great accuracy of optimal solutions. Finally, the AMPC is characterized by its robustness according to perturbation effects