Advanced decision support through real-time optimization in the process industry

En la industria de procesos se puede obtener un aumento de la eficiencia de las plantas de producción, bien mediante la sustitución de procesos o equipos antiguos por otros más modernos y eficientes, o bien operando de forma más eficiente las instalaciones actuales en lugar de realizar grandes inversiones con tiempos de amortización inciertos. Si nos centramos en esta segunda línea de acción, hoy en día la toma de decisiones es conceptualmente más compleja que en el pasado, debido al rápido crecimiento que ha tenido la tecnología últimamente y a que los sistemas de comunicación han generado un gran número de alternativas entre las que se ha de elegir. Además, una decisión incorrecta o subóptima, con la complejidad estructural de los problemas actuales, a menudo resulta en un aumento de los costes a lo largo de la cadena de producción. A pesar de ello, el uso de sistemas de apoyo a la toma de decisiones (DSS) sigue siendo atípico en las industrias de procesos debido a los esfuerzos que se requieren en términos de desarrollo y mantenimiento de modelos matemáticos y al desafío de formulaciones matemáticas complejas, los exigentes requisitos computacionales y/o la difícil integración con la infraestructura de control o planificación existente. Esta tesis contribuye en la reducción de estas barreras desarrollando formulaciones eficientes para la optimización en tiempo real (RTO) en una planta industrial. En particular, esta tesis busca mejorar la operación de tres secciones interconectadas de una fábrica de producción de fibra de viscosa: una red de evaporación, una de sistema de enfriamiento y una red de recuperación de calor.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

A Modular Approach to Adaptive Reactive Streaming Systems

The latest generations of FPGA devices offer large resource counts that provide the headroom to implement large-scale and complex systems. However, there are increasing challenges for the designer, not just because of pure size and complexity, but also in harnessing effectively the flexibility and programmability of the FPGA. A central issue is the need to integrate modules from diverse sources to promote modular design and reuse. Further, the capability to perform dynamic partial reconfiguration (DPR) of FPGA devices means that implemented systems can be made reconfigurable, allowing components to be changed during operation. However, use of DPR typically requires low-level planning of the system implementation, adding to the design challenge. This dissertation presents ReShape: a high-level approach for designing systems by interconnecting modules, which gives a ‘plug and play’ look and feel to the designer, is supported by tools that carry out implementation and verification functions, and is carried through to support system reconfiguration during operation. The emphasis is on the inter-module connections and abstracting the communication patterns that are typical between modules – for example, the streaming of data that is common in many FPGA-based systems, or the reading and writing of data to and from memory modules. ShapeUp is also presented as the static precursor to ReShape. In both, the details of wiring and signaling are hidden from view, via metadata associated with individual modules. ReShape allows system reconfiguration at the module level, by supporting type checking of replacement modules and by managing the overall system implementation, via metadata associated with its FPGA floorplan. The methodology and tools have been implemented in a prototype for a broad domain-specific setting – networking systems – and have been validated on real telecommunications design projects

Scalable parallel simulation of variably saturated flow

In this thesis we develop highly accurate simulation tools for variably saturated flow through porous media able to take advantage of the latest supercomputing resources. Hence, we aim for parallel scalability to very large compute resources of over 105 CPU cores. Our starting point is the parallel subsurface flow simulator ParFlow. This library is of widespread use in the hydrology community and known to have excellent parallel scalability up to 16k processes. We first investigate the numerical tools this library implements in order to perform the simulations it was designed for. ParFlow solves the governing equation for subsurface flow with a cell centered finite difference (FD) method. The code targets high performance computing (HPC) systems by means of distributed memory parallelism. We propose to reorganize ParFlow's mesh subsystem by using fast partitioning algorithms provided by the parallel adaptive mesh refinement (AMR) library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. Furthermore, we evaluate the scaling performance of the modified version of ParFlow, demonstrating excellent weak and strong scaling up to 458k cores of the Juqueen supercomputer at the Jülich Supercomputing Centre. The above mentioned results were obtained for uniform meshes and hence without explicitly exploiting the AMR capabilities of the p4est library. A natural extension of our work is to activate such functionality and make ParFlow a true AMR application. Enabling ParFlow to use AMR is challenging for several reasons: It may be based on assumptions on the parallel partition that cannot be maintained with AMR, it may use mesh-related metadata that is replicated on all CPUs, and it may assume uniform meshes in the construction of mathematical operators. Additionally, the use of locally refined meshes will certainly change the spectral properties of these operators. In this work, we develop an algorithmic approach to activate the usage of locally refined grids in ParFlow. AMR allows meshes where elements of different size neighbor each other. In this case, ParFlow may incur erroneous results when it attempts to communicate data between inter-element boundaries. We propose and discuss two solutions to this issue operating at two different levels: The first manipulates the indices of the degrees of freedom, While the second operates directly on the degrees of freedom. Both approaches aim to introduce minimal changes to the original ParFlow code. In an AMR framework, the FD method taken by ParFlow will require modifications to correctly deal with different size elements. Mixed finite elements (MFE) are on the other hand better suited for the usage of AMR. It is known that the cell centered FD method used in ParFlow might be reinterpreted as a MFE discretization using Raviart-Thomas elements of lower order. We conclude this thesis presenting a block preconditioner for saddle point problems arising from a MFE on locally refined meshes. We evaluate its robustness with respect to various classes of coefficients for uniform and locally refined meshes

Integrated modeling and analysis methodologies for architecture-level vehicle design.

In order to satisfy customer expectations, a ground vehicle must be designed to meet a broad range of performance requirements. A satisfactory vehicle design process implements a set of requirements reflecting necessary, but perhaps not sufficient conditions for assuring success in a highly competitive market. An optimal architecture-level vehicle design configuration is one of the most important of these requirements. A basic layout that is efficient and flexible permits significant reductions in the time needed to complete the product development cycle, with commensurate reductions in cost. Unfortunately, architecture-level design is the most abstract phase of the design process. The high-level concepts that characterize these designs do not lend themselves to traditional analyses normally used to characterize, assess, and optimize designs later in the development cycle. This research addresses the need for architecture-level design abstractions that can be used to support ground vehicle development. The work begins with a rigorous description of hierarchical function-based abstractions representing not the physical configuration of the elements of a vehicle, but their function within the design space. The hierarchical nature of the abstractions lends itself to object orientation - convenient for software implementation purposes - as well as description of components, assemblies, feature groupings based on non-structural interactions, and eventually, full vehicles. Unlike the traditional early-design abstractions, the completeness of our function-based hierarchical abstractions, including their interactions, allows their use as a starting point for the derivation of analysis models. The scope of the research in this dissertation includes development of meshing algorithms for abstract structural models, a rigid-body analysis engine, and a fatigue analysis module. It is expected that the results obtained in this study will move systematic design and analysis to the earliest phases of the vehicle development process, leading to more highly optimized architectures, and eventually, better ground vehicles. This work shows that architecture level abstractions in many cases are better suited for life cycle support than geometric CAD models. Finally, substituting modeling, simulation, and optimization for intuition and guesswork will do much to mitigate the risk inherent in large projects by minimizing the possibility of incorporating irrevocably compromised architecture elements into a vehicle design that no amount of detail-level reengineering can undo

XcalableMP PGAS Programming Language

XcalableMP is a directive-based parallel programming language based on Fortran and C, supporting a Partitioned Global Address Space (PGAS) model for distributed memory parallel systems. This open access book presents XcalableMP language from its programming model and basic concept to the experience and performance of applications described in XcalableMP.　 XcalableMP was taken as a parallel programming language project in the FLAGSHIP 2020 project, which was to develop the Japanese flagship supercomputer, Fugaku, for improving the productivity of parallel programing. XcalableMP is now available on Fugaku and its performance is enhanced by the Fugaku interconnect, Tofu-D. The global-view programming model of XcalableMP, inherited from High-Performance Fortran (HPF), provides an easy and useful solution to parallelize data-parallel programs with directives for distributed global array and work distribution and shadow communication. The local-view programming adopts coarray notation from Coarray Fortran (CAF) to describe explicit communication in a PGAS model. The language specification was designed and proposed by the XcalableMP Specification Working Group organized in the PC Consortium, Japan. The Omni XcalableMP compiler is a production-level reference implementation of XcalableMP compiler for C and Fortran 2008, developed by RIKEN CCS and the University of Tsukuba. The performance of the XcalableMP program was used in the Fugaku as well as the K computer. A performance study showed that XcalableMP enables a scalable performance comparable to the message passing interface (MPI) version with a clean and easy-to-understand programming style requiring little effort

Optimizing Block-Stacking Operations with Relocation

The focus of the dissertation is developing the optimization problem of finding the minimum-cost operational plan of block stacking with relocation as well as devising a solution procedure to solve practical-sized instances of the problem. Assuming changeable row depth instead of permanent row depth, this research is distinguished from conventional block stacking studies. The first contribution of the dissertation is the development of the optimization problem under the assumption of deterministic demand. The problem is modeled using integer programming as a variation of the unsplittable multi-commodity flow problem. To find a good feasible solution of practical-sized instances in reasonable time, we decompose the original problem into a series of generalized assignment problems. In addition, to establish a good lower bound on the optimal objective function value, we apply a relaxation based upon Lagrangean decomposition in which the relaxed problem separates into a set of shortest path problems and a set of binary knapsack problems. The second contribution of the dissertation is the development of the optimization problem under the assumption of stochastic demand. The problem is formulated as a discrete time finite horizon Markov decision process model, incorporating the recursive daily situation of determining the assignment of product lots to storage areas for a day based on uncertain daily demand and observed system information. To tackle computational intractability in solving practical-sized instances, we develop a heuristic solution approach taking an on-line manner by instantly determining an action for a single observed state rather than an off-line manner by predetermining an action for every state