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This study aims to explore and further the knowledge of the topic of composition optimization. The essay aims to examine how a prominent AI called Luminar AI handles composition optimization of images of different kinds in comparison with some of the most common compositional techniques used today. Through this study ten different images were compiled, varying in content and light conditions, to have their compositions be optimized by an AI. A manual variation of the images was also created to serve as a theoretically based foundation for comparison of the compositions. From these different compositions an online form was conducted to gather data about how observers experienced images with different compositions and how they generally felt about them. Four professional photographers were also interviewed, verging in experience and area of expertise. From this data, conclusions regarding how well the AI’s results compared with the manual optimisation of the images was found. Information on, in what regard this type of technology could work today and how well it would be accepted in the field of photography was also debated.

The client is an organization founded and is operational in Sweden, Stockholm. The company focuses on manufacturing of clean tech solutions with its self-developed technology. Their technology generate power from renewable sources. The technology is applied to a substrate which is manufactured inside the factory, today's handling of the substrate must be made more effective. The purpose of the work is to analyze and optimize the handling of substrate in the factory. This includes from substrate manufacturing to storage to the next process in the chain.  The approach to the work began with a background study, preparation of a schedule, development of concepts and concept evaluation. To structure the work, the decision was made to follow the methods developed in the book Six Sigma’s.  The background study gathered information and knowledge about the subject to give a more profound understanding. Market studies were also conducted to find suitable solutions as well as components and products.  Several concepts were developed using brainstorming. The concepts were evaluated intuitively and using Pugh's matrix. The most suitable concept was further developed and dimensioned based on the conditions. Computer models were manufactured for the development and illustration of concepts.  The project resulted in the way the company transfer rolls today is the most optimal with the conditions that exist. The focus was therefore shifted to the development of a paternoster plant for storing rolls. The storage system that was developed holds up to 10 rolls at a time with a weight of 450 kg each. The construction has a maximum height of 3,17 meters, width of 3 meters and a depth of 1,6 meters. The concept will increase the efficiency of the workers by making it easier to sort rolls and reduce the workload.  There are several possibilities for improvement for optimizing the substrate handling in the factory and these are discussed in the last chapter. / Uppdragsgivaren är ett företag grundat och operativa i Sverige, Stockholm. Företaget inriktar sig mot tillverknings av lösningar för grön energi med sin egenutvecklade teknik. Tekniken appliceras på ett substrat som tillverkas i fabriken, dagens hantering av substratet måste effektiviseras och automatiseras. Syftet med arbetet är att analysera och optimera hanteringen av substrat i fabriken. Detta omfattar förflyttningen från substrattillverkning till lagring, till och med nästa process grupp i kedjan.  Tillvägagångssättet för arbetet började med en bakgrundsstudie, upprättning av tidsschema, utveckling av koncept och konceptevaluering. För att strukturera arbetet togs beslutet att följa metoderna framtagna i boken Six Sigmas.  I bakgrundsstudien samlades information och kunskap kring ämnet för att ge en djupare förståelse. Marknadsstudier genomfördes även för att finna lämpliga lösningar samt komponenter och produkter.  Ett flertal koncept utvecklades med hjälp av brainstorming. Koncepten utvärderades med hjälp av Pughs matris. Det mest lämpliga konceptet vidareutvecklades och dimensionerades utifrån förutsättningarna. Datormodeller tillverkades för utveckling och illustrering av koncept.  Projektet resulterade i att sättet förtaget förflyttar rullar idag är det optimalaste med dom förutsättningarna som existerar. Fokuset förflyttades därför till utveckling av ett paternosterverk för lagring av rullar. Lagrings systemet som utvecklades håller upp till 10 rullar samtidigt med en vikt på 450 kg styck. Konstruktionen har en maximal höjd på 3 meter, bredd på 3 meter och ett djup på 1,6 meter. Konceptet kommer öka effektiviteten för arbetarna genom att enklare sortera rullar och minska arbetsbelastningen.  Flera förbättringsmöjligheter finns för optimering av substrathanteringen i fabriken och dessa tas upp i sista kapitlet.

Automatic parallelization is the conversion of sequential code into multi-threaded code with little or no supervision. An ideal implementation of automatic parallelization would allow programmers to fully utilize available hardware resources to deliver optimal performance when writing code. Automatic parallelization has been studied for a long time, with one result being that modern compilers support vectorization without any input. In the study, contemporary parallelizing compilers are studied in order to determine whether or not they can easily be used in modern software development, and how code generated by them compares to manually parallelized code. Five compilers, ICC, Cetus, autoPar, PLUTO, and TC Optimizing Compiler are included in the study. Benchmarks are used to measure speedup of parallelized code, these benchmarks are executed on three different sets of hardware. The NAS Parallel Benchmarks (NPB) suite is used for ICC, Cetus, and autoPar, and PolyBench for the previously mentioned compilers in addition to PLUTO and TC Optimizing Compiler. Results show that parallelizing compilers outperform serial code in most cases, with certain coding styles hindering the capability of them to parallelize code. In the NPB suite, manually parallelized code is outperformed by Cetus and ICC for one benchmark. In the PolyBench suite, PLUTO outperforms the other compilers to a great extent, producing code not only optimized for parallel execution, but also for vectorization. Limitations in code generated by Cetus and autoPar prevent them from being used in legacy projects, while PLUTO and TC do not offer fully automated parallelization. ICC was found to offer the most complete automatic parallelization solution, although offered speedups were not as great as ones offered by other tools. / Automatisk parallellisering innebär konvertering av sekventiell kod till multitrådad kod med liten eller ingen tillsyn. En idealisk implementering av automatisk parallellisering skulle låta programmerare utnyttja tillgänglig hårdvara till fullo för att uppnå optimal prestanda när de skriver kod. Automatisk parallellisering har varit ett forskningsområde under en längre tid, och har resulterat i att moderna kompilatorer stöder vektorisering utan någon insats från programmerarens sida. I denna studie studeras samtida parallelliserande kompilatorer för att avgöra huruvida de lätt kan integreras i modern mjukvaruutveckling, samt hur kod som dessa kompilatorer genererar skiljer sig från manuellt parallelliserad kod. Fem kompilatorer, ICC, Cetus, autoPar, PLUTO, och TC Optimizing Compiler inkluderas i studien. Benchmarks används för att mäta speedup av paralleliserad kod. Dessa benchmarks exekveras på tre skiljda hårdvaruuppsättningar. NAS Parallel Benchmarks (NPB) används som benchmark för ICC, Cetus, och autoPar, och PolyBench för samtliga kompilatorer i studien. Resultat visar att parallelliserande kompilatorer genererar kod som presterar bättre än sekventiell kod i de flesta fallen, samt att vissa kodstilar begränsar deras möjlighet att parallellisera kod. I NPB så presterar kod parallelliserad av Cetus och ICC bättre än manuellt parallelliserad kod för en benchmark. I PolyBench så presterar PLUTO mycket bättre än de andra kompilatorerna och producerar kod som inte endast är optimerad för parallell exekvering, utan också för vektorisering. Begränsningar i kod genererad av Cetus och autoPar förhindrar användningen av dessa redskap i etablerade projekt, medan PLUTO och TC inte är kapabla till fullt automatisk parallellisering. Det framkom att ICC erbjuder den mest kompletta lösningen för automatisk parallellisering, men möjliga speedups var ej på samma nivå som för de andra kompilatorerna.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

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. / O aumento da concorrência motiva a indústria a implementar ferramentas que melhorem a eficiência de seus processos. A automação é uma dessas ferramentas, e o Real Time Optimization (RTO) ou Otimização em Tempo Real, é uma metodologia de automação que considera aspectos econômicos e restrições de processos e equipamentos para atualizar o controle do processo, de acordo com preços de mercado e distúrbios. Basicamente, o RTO usa um modelo fenomenológico em estado estacionário para predizer o comportamento do processo, em seguida, otimiza uma função objetivo econômica sujeita a esse modelo. Embora amplamente utilizado na indústria, não há ainda um consenso geral sobre os benefícios da implementação do RTO, devido a algumas limitações discutidas no presente trabalho: incompatibilidade estrutural entre planta e modelo, problemas de identificabilidade e baixa frequência de atualização dos set points. Algumas metodologias de RTO foram propostas na literatura para lidar com o problema da incompatibilidade entre planta e modelo. No entanto, não há uma comparação que avalie a abrangência e as limitações destas diversas abordagens de RTO, sob diferentes aspectos. Por esta razão, o método clássico de RTO é comparado com metodologias mais recentes, baseadas em derivadas (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality), utilizando-se o método de Monte Carlo. Os resultados desta comparação mostram que o método clássico de RTO é coerente, desde que seja proporcionado um modelo suficientemente flexível para se representar a topologia do processo, um método de estimação de parâmetros apropriado para lidar com características de ruído de medição e um método para melhorar a qualidade da informação da amostra. Já os problemas de identificabilidade podem ser observados a cada iteração de RTO, quando o método atualiza alguns parâmetros-chave do modelo, o que é causado principalmente pela ausência de medidas e ruídos. Por esse motivo, quatro abordagens de estimação de parâmetros (Discriminação Rotacional, Seleção Automática e Estimação de Parâmetros, Reparametrização via Geometria Diferencial e o clássico Mínimos Quadrados não-lineares) são avaliados em relação à sua capacidade de predição, robustez e velocidade. Os resultados revelam que o método de Discriminação Rotacional é o mais adequado para ser implementado em um ciclo de RTO, já que requer menos informação a priori, é simples de ser implementado e evita o sobreajuste observado no método de Mínimos Quadrados. A terceira desvantagem associada ao RTO é a baixa frequência de atualização dos set points, o que aumenta o período em que o processo opera em condições subotimas. Uma alternativa para lidar com este problema é proposta no presente trabalho, integrando-se o RTO e o Self-Optimizing Control (SOC) através de um novo algoritmo de Model Predictive Control (MPC). Os resultados obtidos com a nova abordagem demonstram que é possível reduzir o problema da baixa frequência de atualização dos set points, melhorando o desempenho econômico do processo. Por fim, os aspectos práticos da implementação do RTO são discutidos em um estudo de caso industrial, que trata de um processo de destilação com bomba de calor, localizado na Refinaria de Paulínia (REPLAN - Petrobras). Os resultados deste estudo sugerem que os parâmetros do modelo são estimados com sucesso pelo método de Discriminação Rotacional; que o RTO é capaz de aumentar o lucro do processo em cerca de 3%, o equivalente a 2 milhões de dólares por ano; e que a integração entre SOC e RTO pode ser uma alternativa interessante para o controle deste processo de destilação.

The discipline of marketing is evolving from a product centric paradigm where all value is invested in the product by the supplier and it is exchanged for a market determined price by means of an arm’s length transaction, to a service centric paradigm where value is co-created by customer and supplier through complex relationships in which the rewards are determined through negotiation. This study recognizes that in practice a supplier will and ought to continue to have some customer relationships that are transactional and others that involve higher levels of value co-creation. A five point continuum of relationships from transactional to strategic alliance is defined. Dyadic data in which customer and supplier are asked to evaluate the same relationship from their respective points of view are analyzed resulting in a portfolio of a supplier’s relationships that include each of the five levels. Three structured equation models are validated: first, the customer’s assessment of the level of relationship as a function of new, behaviorally anchored measures; second, the supplier’s assessment as a function of new, behaviorally anchored measures of investment; third, the differences between customer and supplier assessments as a function of differences in ratings of new, behaviorally anchored measures. Additionally, segmentation of the customer base is identified based on the level of assessment of the current and desired future level of relationship. Servicing processes are defined to enable the supplier to match the right offerings to each level of customer thereby optimizing their investment in their customer portfolio.

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Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.