A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Mathematical Optimization Applied to Thermal and Electrical Energy Systems

This Thesis aims at building and discussing mathematical models applications focused on Energy problems, both on the thermal and electrical side. The objective is to show how mathematical programming techniques developed within Operational Research can give useful answers in the Energy Sector, how they can provide tools to support decision making processes of Companies operating in the Energy production and distribution and how they can be successfully used to make simulations and sensitivity analyses to better understand the state of the art and convenience of a particular technology by comparing it with the available alternatives. The first part discusses the fundamental mathematical background followed by a comprehensive literature review about mathematical modelling in the Energy Sector. The second part presents mathematical models for the District Heating strategic network design and incremental network design. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues, minimizing infrastructure and operational costs and taking into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed with particular attention to instances characterized by big networks dimensions. The third part is devoted to the development of linear programming models for optimal battery operation in off-grid solar power schemes, with consideration of battery degradation. The key contribution of this work is the inclusion of battery degradation costs in the optimisation models. As available data on relating degradation costs to the nature of charge/discharge cycles are limited, we concentrate on investigating the sensitivity of operational patterns to the degradation cost structure. The objective is to investigate the combination of battery costs and performance at which such systems become economic. We also investigate how the system design should change when battery degradation is taken into account.

Optimization of molecular and crystalline forms of drugs, agrochemicals, pesticides in relation to activity, bioavailability, patentability and to the fabrication of polymorphs, solvates, co-crystals with green chemistry methods

This doctorate was funded by the Regione Emilia Romagna, within a Spinner PhD project coordinated by the University of Parma, and involving the universities of Bologna, Ferrara and Modena. The aim of the project was: - Production of polymorphs, solvates, hydrates and co-crystals of active pharmaceutical ingredients (APIs) and agrochemicals with green chemistry methods; - Optimization of molecular and crystalline forms of APIs and pesticides in relation to activity, bioavailability and patentability. In the last decades, a growing interest in the solid-state properties of drugs in addition to their solution chemistry has blossomed. The achievement of the desired and/or the more stable polymorph during the production process can be a challenge for the industry. The study of crystalline forms could be a valuable step to produce new polymorphs and/or co-crystals with better physical-chemical properties such as solubility, permeability, thermal stability, habit, bulk density, compressibility, friability, hygroscopicity and dissolution rate in order to have potential industrial applications. Selected APIs (active pharmaceutical ingredients) were studied and their relationship between crystal structure and properties investigated, both in the solid state and in solution. Polymorph screening and synthesis of solvates and molecular/ionic co-crystals were performed according to green chemistry principles. Part of this project was developed in collaboration with chemical/pharmaceutical companies such as BASF (Germany) and UCB (Belgium). We focused on on the optimization of conditions and parameters of crystallization processes (additives, concentration, temperature), and on the synthesis and characterization of ionic co-crystals. Moreover, during a four-months research period in the laboratories of Professor Nair Rodriguez-Hormedo (University of Michigan), the stability in aqueous solution at the equilibrium of ionic co-crystals (ICCs) of the API piracetam was investigated, to understand the relationship between their solid-state and solution properties, in view of future design of new crystalline drugs with predefined solid and solution properties.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.

Sedation and weaning from mechanical ventilation: effects of process optimization outside a clinical trial

PURPOSE: We studied the effects of reorganization and changes in the care process, including use of protocols for sedation and weaning from mechanical ventilation, on the use of sedative and analgesic drugs and on length of respiratory support and stay in the intensive care unit (ICU). MATERIALS AND METHODS: Three cohorts of 100 mechanically ventilated ICU patients, admitted in 1999 (baseline), 2000 (implementation I, after a change in ICU organization and in diagnostic and therapeutic approaches), and 2001 (implementation II, after introduction of protocols for weaning from mechanical ventilation and sedation), were studied retrospectively. RESULTS: Simplified Acute Physiology Score II (SAPS II), diagnostic groups, and number of organ failures were similar in all groups. Data are reported as median (interquartile range).Time on mechanical ventilation decreased from 18 (7-41) (baseline) to 12 (7-27) hours (implementation II) (P = .046), an effect which was entirely attributable to noninvasive ventilation, and length of ICU stay decreased in survivors from 37 (21-71) to 25 (19-63) hours (P = .049). The amount of morphine (P = .001) and midazolam (P = .050) decreased, whereas the amount of propofol (P = .052) and fentanyl increased (P = .001). Total Therapeutic Intervention Scoring System-28 (TISS-28) per patient decreased from 137 (99-272) to 113 (87-256) points (P = .009). Intensive care unit mortality was 19% (baseline), 8% (implementation I), and 7% (implementation II) (P = .020). CONCLUSIONS: Changes in organizational and care processes were associated with an altered pattern of sedative and analgesic drug prescription, a decrease in length of (noninvasive) respiratory support and length of stay in survivors, and decreases in resource use as measured by TISS-28 and mortality.

Ansätze zur Verbesserung von Qualität und Wirtschaftlichkeit bei generativen Verfahren durch Optimierung des Pre-Processes

Mittels generativer Fertigung ist es heute möglich die, Entwicklungszeit und Ferti-gungsdauer von Prototypen, Produkten und Werkzeugen zu verkürzen. Neben dieser Zeitersparnis sind die im Vergleich zu konventionellen Fertigungsverfahren unwe-sentlichen Geometriebeschränkungen für den Anwender von besonderem Interesse. Dieses Alleinstellungsmerkmal der generativen Fertigung macht es möglich auch komplexe Geometrie wirtschaftlich herzustellen. Voraussetzung für eine wirtschaftli-che und fehlerminimierte Fertigung ist hierbei eine möglichst optimale Prozessvorbe-reitung (Pre-Processing). Dabei sind insbesondere die Schritte der Bauteilorientie-rung, der Stützkonstruktionserzeugung, der Schichtzerlegung sowie der Bauraum-ausnutzung von Interesse. Auch wenn diese Punkte wesentlich zur Qualität und Wirtschaftlichkeit beitragen, sind die Erkenntnisse für den unerfahrenen Anwender nur unzureichend dokumentiert, wodurch eine möglichst effiziente Fertigung zu-nächst ausgeschlossen werden kann. Anhand unterschiedlicher Beispiele sollen dem Anwender hier die Möglichkeiten zur Optimierung dieser Pre-Processing Schritte er-läutert werden. In diesem Rahmen werden die aktuellen Forschungsergebnisse des Lehrstuhls Rechnereinsatz in der Konstruktion, Institut für Produkt Engineering der Universität Duisburg-Essen in Bezug auf die Optimierung der Bauteilorientierung, der variablen Schichtzerlegung und der Optimierung der Bauraumausnutzung vorgestellt.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.

Business Process Optimization in Madrid City Council.

While designing systems and products requires a deep understanding of influences that achieve desirable performance, the need for an efficient and systematic decision-making approach drives the need for optimization strategies. This paper provides the motivation for this topic as well as a description of applications in Computing Center of Madrid city Council. Optimization applications can be found in almost all areas of engineering. Typical problems in process, working with a database, arise in query design, entity model design and concurrent processes. This paper proposes a solution to optimize a night process dealing with millions of records with an overall performance of about eight times in computation time.

Proceso metodológico para optimizar la toma de decisiones en el proyecto de envolventes arquitectónicas multicapa = Methodological process for the optimization of the decision making in multilayer building envelope projects

La presente tesis doctoral se enmarca dentro del concepto de la sistematización del conocimiento en arquitectura, más concretamente en el campo de las construcciones arquitectónicas y la toma de decisiones en la fase de proyecto de envolventes arquitectónicas multicapa. Por tanto, el objetivo principal es el establecimiento de las bases para una toma de decisiones informadas durante el proyecto de una envolvente multicapa con el fin de colaborar en su optimización. Del mismo modo que la historia de la arquitectura está relacionada con la historia de la innovación en construcción, la construcción está sujeta a cambios como respuesta a los fracasos anteriores. En base a esto, se identifica la toma de decisiones en la fase de proyecto como el estadio inicial para establecer un punto estratégico de reflexión y de control sobre los procesos constructivos. La presente investigación, conceptualmente, define los parámetros intervinientes en el proyecto de envolventes arquitectónicas multicapa a partir de una clasificación y sistematización de todos los componentes (elementos, unidades y sistemas constructivos) utilizados en las fachadas multicapa. Dicha sistematización se materializa en una hoja matriz de datos en la que, dentro de una organización a modo de árbol, se puede acceder a la consulta de cada componente y de su caracterización. Dicha matriz permite la incorporación futura de cualquier componente o sistema nuevo que aparezca en el mercado, relacionándolo con aquellos con los que comparta ubicación, tipo de material, etc. Con base en esa matriz de datos, se diseña la sistematización de la toma de decisiones en la fase de proyecto de una envolvente arquitectónica, en concreto, en el caso de una fachada. Operativamente, el resultado se presenta como una herramienta que permite al arquitecto o proyectista reflexionar y seleccionar el sistema constructivo más adecuado, al enfrentarse con las distintas decisiones o elecciones posibles. La herramienta se basa en las elecciones iniciales tomadas por el proyectista y se estructura, a continuación y sucesivamente, en distintas aproximaciones, criterios, subcriterios y posibilidades que responden a los distintos avances en la definición del sistema constructivo. Se proponen una serie de fichas operativas de comprobación que informan sobre el estadio de decisión y de definición de proyecto alcanzados en cada caso. Asimismo, el sistema permite la conexión con otros sistemas de revisión de proyectos para fomentar la reflexión sobre la normalización de los riesgos asociados tanto al proprio sistema como a su proceso constructivo y comportamiento futuros. La herramienta proporciona un sistema de ayuda para ser utilizado en el proceso de toma de decisiones en la fase de diseño de una fachada multicapa, minimizando la arbitrariedad y ofreciendo una cualificación previa a la cuantificación que supondrá la elaboración del detalle constructivo y de su medición en las sucesivas fases del proyecto. Al mismo tiempo, la sistematización de dicha toma de decisiones en la fase del proyecto puede constituirse como un sistema de comprobación en las diferentes fases del proceso de decisión proyectual y de definición de la envolvente de un edificio. ABSTRACT The central issue of this doctoral Thesis is founded on the framework of the concept of the systematization of knowledge in architecture, in particular, in respect of the field of building construction and the decision making in the design stage of multilayer building envelope projects. Therefore, the main objective is to establish the bases for knowledgeable decision making during a multilayer building envelope design process, in order to collaborate with its optimization. Just as the history of architecture is connected to the history of innovation in construction, construction itself is subject to changes as a response to previous failures. On this basis, the decisions made during the project design phase are identified as the initial state to establish an strategic point for reflection and control, referred to the constructive processes. Conceptually, this research defines the parameters involving the multilayer building envelope projects, on the basis of a classification and systematization for all the components (elements, constructive units and constructive systems) used in multilayer façades. The mentioned systematization is materialized into a data matrix sheet in which, following a tree‐like organization, the access to every single component and its characterization is possible. The above data matrix allows the future inclusion of any new component or system that may appear in the construction market. That new component or system can be put into a relationship with another, which it shares location, type of material,… with. Based on the data matrix, the systematization of the decision making process for a building envelope design stage is designed, more particularly in the case of a façade. Putting this into practice, it is represented as a tool which allows the architect or the designer, to reflect and to select the appropriate building system when facing the different elections or the different options. The tool is based on the initial elections taken by the designer. Then and successively, it is shaped on the form of different operative steps, criteria, sub‐criteria and possibilities which respond to a different progress in the definition of the building construction system. In order to inform about the stage of the decision and the definition reached by the project in every particular case, a range of operative sheets are proposed. Additionally, the system allows the connection with other reviewing methods for building projects. The aim of this last possibility is to encourage the reflection on standardization of the associated risks to the building system itself and its future performance. The tool provides a helping system to be used during the decision making process for a multilayer façade design. It minimizes the arbitrariness and offers a qualification previous to the quantification that will be done with the development of the construction details and their bill of quantities, that in subsequent project stages will be executed. At the same time, the systematization of the mentioned decision making during the design phase, can be found as a checking system in the different stages of the decision making design process and in the different stages of the building envelope definition.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.

A novel hybrid simulation-optimization approach for the optimal design of multicomponent distillation columns

Póster presentado en Escape 22, European Symposium on Computer Aided Process Engineering, University College London, UK, 17-20 June 2012.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation etc., are all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

An alternative disjunctive optimization model for heat integration with variable temperatures

This paper presents an alternative model to deal with the problem of optimal energy consumption minimization of non-isothermal systems with variable inlet and outlet temperatures. The model is based on an implicit temperature ordering and the “transshipment model” proposed by Papoulias and Grossmann (1983). It is supplemented with a set of logical relationships related to the relative position of the inlet temperatures of process streams and the dynamic temperature intervals. In the extreme situation of fixed inlet and outlet temperatures, the model reduces to the “transshipment model”. Several examples with fixed and variable temperatures are presented to illustrate the model's performance.