Operational characteristics of four metering systems for agricultural fertilizers and amendments

The use of fertilizers and solid amendments in agriculture generates special interest for their effect on crop productivity, as well as for their environmental impact. The efficient use of these products demands knowing their physical and mechanical properties, the storing conditions effect and the operational characteristics of the metering systems used in the fertilizing equipment. In this context, the present study was developed with the purpose of evaluating the operational characteristics of different fertilizing metering systems and to determine the adequate metering system-product operational parameters, using powder lime, powder gypsum, granular 10-30-10 (N-P-K), and granular urea. Operational differences were established among four types of commercial fertilizer metering systems, including wire auger, star-shaped feed wheel, feed screw and ridged traction wheel. The study found that the unloading rate depends directly on the fertilizer metering system's rotating speed and is affected by particle size, repose angle, bulk density and moisture content of the applied product. The wire auger and star-shaped feed wheel metering systems were adequate for the distribution of powder products and the feed screw for granulated fertilizers. Furthermore, theoretical and experimental characteristic equations were established, defining curves for calibration and handling of the products plus the rotating speed range in which a better distributing behavior was achieved.

Operational performance of the mechanized and semi-mechanized potato harvest

Potato is an important crop plant throughout the world. Harvesting is a fundamental step in its production system. Maybe, it is the most complex and expensive operation. Thus, the objective of this work was to compare the cost of the mechanized and semi-mechanized harvest, the operational capacity and the production losses during the potato harvest process. The work was accomplished in a commercial farming, cultivated under pivot system, in the municipal district of Perdizes - MG, Brazil. A completely randomized design with two treatments was used: mechanized and semi-mechanized harvest. The mechanized harvest used a self-propelled harvester. In the semi-automated harvest, a digger mounted on tractor was used and the potato was manually harvested. It was concluded that the cost of mechanized harvest was 49.03% lower than the cost of semi-mechanized harvest. On average, the harvester had a work for 23 workers in manual harvest. Mechanized harvest showed losses of 2.35% of potato yield, while the semi-mechanized harvest showed losses of 6.32%.

Operational parameters of soybean seeding in santa fe system

The use of no tillage system associated with the crop-livestock integration is an alternate managing that promotes the accumulation of dry matter in the soil, an essential fact to make the system sustainable and profitable. The aim of this study was to evaluate the operational performance of a planter-tractor set on maize straws intercropped with Urochloa, in different seeding modes. The soybean crop was seed on the intercropping of two forage species (Urochloa brizantha and Urochloa ruziziensis) in five cropping systems: MBL (Maize with Urochloa in the maize seeding row, mixed with base fertilizer and deposited at 0.10 m), MBE (Maize with Urochloa seeded between rows at the same day of seeding maize), MBC (Urochloa between rows of maize seeded with the covering fertilizer at the V4 stage), MBLA (Maize with Urochloa by broadcast seeding at the V4 stage ) and MS (Single Maize: control). The following variables were evaluated: dry mass of maize straw, dry mass of forages and total dry mass of straw; and for the operational parameters the speed of seeding, wheel slippage, traction force and average power at the drawbar. The results showed that the amount of straw produced by maize intercropping with Urochloa, interferes in the operational performance of the tractor-planter at the operation of soybean seeding, i.e., areas with higher amount of straw promote greater energy demand, as well as higher wheel slippage.

Energy demand in citrus production under varied operational efficiency values

Citrus orchards are very important in Brazil, especially in São Paulo State, where occupy an area of 600,000 ha approximately. To identify sustainability degree of citrus production system, an energy analysis allows evaluating efficiency of direct and indirect applied inputs. Thus, this study aimed to evaluate citrus production system under energetic point of view, in which invested energy is paid back with citrus production; being compared within three scenarios for operational field efficiency. As result, by sensitivity analysis was determined that fuel was the main energy demander, followed by pesticides and fertilizers. In operational work capacity analysis, all combinations between efficiency (minimum, typical and maximum) and yield levels became positive in the seventh year, except for the combination minimum efficiency and 10 % less yield, positive in the eighth year. The best combination (maximum efficiency and 10 % more yield) has promoted investment payoff around the sixth and seventh year. By this study, it is possible to determine the total energy demand to produce citrus and indentify the applied inputs that need more attention by the decision-makers. Labor and seedlings can be ommited for further studies with citrus, since they were irrelevant. Management of agricultural machinery may pose an important role on decreasing environmental impact of citrus production.

Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

The effect of dynamic capabilities on operational-level innovation – Empirical study in magazine publishing industry

This master’s thesis aims to examine the relationship between dynamic capabilities and operational-level innovations. In addition, measures for the concept of dynamic capabilities are developed. The study was executed in the magazine publishing industry which is considered favourable for examining dynamic capabilities, since the sector is characterized by rapid change. As a basis for the study and the measure development, a literary review was conducted. Data for the empirical section was gathered by a survey targeted to chief-editors of Finnish consumer magazines. The relationship between dynamic capabilities and innovation was examined by multiple linear regression. The results indicate that dynamic capabilities have effect on the emergence of radical innovations. Environmental dynamism’s effect on radical innovations was not detected. Also, dynamic capabilities’ effect on innovation was not greater in turbulent operating environment.

Structural and operational optimisation : applications in energy systems

In this work mathematical programming models for structural and operational optimisation of energy systems are developed and applied to a selection of energy technology problems. The studied cases are taken from industrial processes and from large regional energy distribution systems. The models are based on Mixed Integer Linear Programming (MILP), Mixed Integer Non-Linear Programming (MINLP) and on a hybrid approach of a combination of Non-Linear Programming (NLP) and Genetic Algorithms (GA). The optimisation of the structure and operation of energy systems in urban regions is treated in the work. Firstly, distributed energy systems (DES) with different energy conversion units and annual variations of consumer heating and electricity demands are considered. Secondly, district cooling systems (DCS) with cooling demands for a large number of consumers are studied, with respect to a long term planning perspective regarding to given predictions of the consumer cooling demand development in a region. The work comprises also the development of applications for heat recovery systems (HRS), where paper machine dryer section HRS is taken as an illustrative example. The heat sources in these systems are moist air streams. Models are developed for different types of equipment price functions. The approach is based on partitioning of the overall temperature range of the system into a number of temperature intervals in order to take into account the strong nonlinearities due to condensation in the heat recovery exchangers. The influence of parameter variations on the solutions of heat recovery systems is analysed firstly by varying cost factors and secondly by varying process parameters. Point-optimal solutions by a fixed parameter approach are compared to robust solutions with given parameter variation ranges. In the work enhanced utilisation of excess heat in heat recovery systems with impingement drying, electricity generation with low grade excess heat and the use of absorption heat transformers to elevate a stream temperature above the excess heat temperature are also studied.

Opportunities, constraints and constrained opportunities – A study on mothers’ working time patterns in 22 European countries

The aim of this study was to analyse mothers’ working time patterns across 22 European countries. The focus was on three questions: how much mothers prefer to work, how much they actually work, and to what degree their preferred and actual working times are (in)consistent with each other. The focus was on cross-national differences in mothers’ working time patterns, comparison of mothers’ working times to that of childless women and fathers, as well as on individual- and country-level factors that explain the variation between them. In the theoretical background, the departure point was an integrative theoretical approach where the assumption is that there are various kinds of explanations for the differences in mothers’ working time patterns – namely structural, cultural and institutional – , and that these factors are laid in two levels: individual- and country-levels. Data were extracted from the European Social Survey (ESS) 2010 / 2011. The results showed that mothers’ working time patterns, both preferred and actual working times, varied across European countries. Four clusters were formed to illustrate the differences. In the full-time pattern, full-time work was the most important form of work, leaving all other working time forms marginal. The full-time pattern was perceived in terms of preferred working times in Bulgaria and Portugal. In polarised pattern countries, fulltime work was also important, but it was accompanied by a large share of mothers not working at all. In the case of preferred working times, many Eastern and Southern European countries followed it whereas in terms of actual working times it included all Eastern and Southern European countries as well as Finland. The combination pattern was characterised by the importance of long part-time hours and full-time work. It was the preferred working time pattern in the Nordic countries, France, Slovenia, and Spain, but Belgium, Denmark, France, Norway, and Sweden followed it in terms of actual working times. The fourth cluster that described mothers’ working times was called the part-time pattern, and it was illustrated by the prevalence of short and long part-time work. In the case of preferred working times, it was followed in Belgium, Germany, Ireland, the Netherlands and Switzerland. Besides Belgium, the part-time pattern was followed in the same countries in terms of actual working times. The consistency between preferred and actual working times was rather strong in a majority of countries. However, six countries fell under different working time patterns when preferred and actual working times were compared. Comparison of working mothers’, childless women’s, and fathers’ working times showed that differences between these groups were surprisingly small. It was only in part-time pattern countries that working mothers worked significantly shorter hours than working childless women and fathers. Results therefore revealed that when mothers’ working times are under study, an important question regarding the population examined is whether it consists of all mothers or only working mothers. Results moreover supported the use of the integrative theoretical approach when studying mothers’ working time patterns. Results indicate that mothers’ working time patterns in all countries are shaped by various opportunities and constraints, which are comprised of structural, cultural, institutional, and individual-level factors.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Cost-efficient virtual machine management : provisioning, admission control, and consolidation

One of the main challenges in Software Engineering is to cope with the transition from an industry based on software as a product to software as a service. The field of Software Engineering should provide the necessary methods and tools to develop and deploy new cost-efficient and scalable digital services. In this thesis, we focus on deployment platforms to ensure cost-efficient scalability of multi-tier web applications and on-demand video transcoding service for different types of load conditions. Infrastructure as a Service (IaaS) clouds provide Virtual Machines (VMs) under the pay-per-use business model. Dynamically provisioning VMs on demand allows service providers to cope with fluctuations on the number of service users. However, VM provisioning must be done carefully, because over-provisioning results in an increased operational cost, while underprovisioning leads to a subpar service. Therefore, our main focus in this thesis is on cost-efficient VM provisioning for multi-tier web applications and on-demand video transcoding. Moreover, to prevent provisioned VMs from becoming overloaded, we augment VM provisioning with an admission control mechanism. Similarly, to ensure efficient use of provisioned VMs, web applications on the under-utilized VMs are consolidated periodically. Thus, the main problem that we address is cost-efficient VM provisioning augmented with server consolidation and admission control on the provisioned VMs. We seek solutions for two types of applications: multi-tier web applications that follow the request-response paradigm and on-demand video transcoding that is based on video streams with soft realtime constraints. Our first contribution is a cost-efficient VM provisioning approach for multi-tier web applications. The proposed approach comprises two subapproaches: a reactive VM provisioning approach called ARVUE and a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling. Our second contribution is a prediction-based VM provisioning approach for on-demand video transcoding in the cloud. Moreover, to prevent virtualized servers from becoming overloaded, the proposed VM provisioning approaches are augmented with admission control approaches. Therefore, our third contribution is a session-based admission control approach for multi-tier web applications called adaptive Admission Control for Virtualized Application Servers. Similarly, the fourth contribution in this thesis is a stream-based admission control and scheduling approach for on-demand video transcoding called Stream-Based Admission Control and Scheduling. Our fifth contribution is a computation and storage trade-o strategy for cost-efficient video transcoding in cloud computing. Finally, the sixth and the last contribution is a web application consolidation approach, which uses Ant Colony System to minimize the under-utilization of the virtualized application servers.

Susteinable steelmaking by process integration

Environmental issues, including global warming, have been serious challenges realized worldwide, and they have become particularly important for the iron and steel manufacturers during the last decades. Many sites has been shut down in developed countries due to environmental regulation and pollution prevention while a large number of production plants have been established in developing countries which has changed the economy of this business. Sustainable development is a concept, which today affects economic growth, environmental protection, and social progress in setting up the basis for future ecosystem. A sustainable headway may attempt to preserve natural resources, recycle and reuse materials, prevent pollution, enhance yield and increase profitability. To achieve these objectives numerous alternatives should be examined in the sustainable process design. Conventional engineering work cannot address all of these substitutes effectively and efficiently to find an optimal route of processing. A systematic framework is needed as a tool to guide designers to make decisions based on overall concepts of the system, identifying the key bottlenecks and opportunities, which lead to an optimal design and operation of the systems. Since the 1980s, researchers have made big efforts to develop tools for what today is referred to as Process Integration. Advanced mathematics has been used in simulation models to evaluate various available alternatives considering physical, economic and environmental constraints. Improvements on feed material and operation, competitive energy market, environmental restrictions and the role of Nordic steelworks as energy supplier (electricity and district heat) make a great motivation behind integration among industries toward more sustainable operation, which could increase the overall energy efficiency and decrease environmental impacts. In this study, through different steps a model is developed for primary steelmaking, with the Finnish steel sector as a reference, to evaluate future operation concepts of a steelmaking site regarding sustainability. The research started by potential study on increasing energy efficiency and carbon dioxide reduction due to integration of steelworks with chemical plants for possible utilization of available off-gases in the system as chemical products. These off-gases from blast furnace, basic oxygen furnace and coke oven furnace are mainly contained of carbon monoxide, carbon dioxide, hydrogen, nitrogen and partially methane (in coke oven gas) and have proportionally low heating value but are currently used as fuel within these industries. Nonlinear optimization technique is used to assess integration with methanol plant under novel blast furnace technologies and (partially) substitution of coal with other reducing agents and fuels such as heavy oil, natural gas and biomass in the system. Technical aspect of integration and its effect on blast furnace operation regardless of capital expenditure of new operational units are studied to evaluate feasibility of the idea behind the research. Later on the concept of polygeneration system added and a superstructure generated with alternative routes for off-gases pretreatment and further utilization on a polygeneration system producing electricity, district heat and methanol. (Vacuum) pressure swing adsorption, membrane technology and chemical absorption for gas separation; partial oxidation, carbon dioxide and steam methane reforming for methane gasification; gas and liquid phase methanol synthesis are the main alternative process units considered in the superstructure. Due to high degree of integration in process synthesis, and optimization techniques, equation oriented modeling is chosen as an alternative and effective strategy to previous sequential modelling for process analysis to investigate suggested superstructure. A mixed integer nonlinear programming is developed to study behavior of the integrated system under different economic and environmental scenarios. Net present value and specific carbon dioxide emission is taken to compare economic and environmental aspects of integrated system respectively for different fuel systems, alternative blast furnace reductants, implementation of new blast furnace technologies, and carbon dioxide emission penalties. Sensitivity analysis, carbon distribution and the effect of external seasonal energy demand is investigated with different optimization techniques. This tool can provide useful information concerning techno-environmental and economic aspects for decision-making and estimate optimal operational condition of current and future primary steelmaking under alternative scenarios. The results of the work have demonstrated that it is possible in the future to develop steelmaking towards more sustainable operation.

MHC-restricted antigen presentation and recognition: constraints on gene, recombinant and peptide vaccines in humans

The target of any immunization is to activate and expand lymphocyte clones with the desired recognition specificity and the necessary effector functions. In gene, recombinant and peptide vaccines, the immunogen is a single protein or a small assembly of epitopes from antigenic proteins. Since most immune responses against protein and peptide antigens are T-cell dependent, the molecular target of such vaccines is to generate at least 50-100 complexes between MHC molecule and the antigenic peptide per antigen-presenting cell, sensitizing a T cell population of appropriate clonal size and effector characteristics. Thus, the immunobiology of antigen recognition by T cells must be taken into account when designing new generation peptide- or gene-based vaccines. Since T cell recognition is MHC-restricted, and given the wide polymorphism of the different MHC molecules, distinct epitopes may be recognized by different individuals in the population. Therefore, the issue of whether immunization will be effective in inducing a protective immune response, covering the entire target population, becomes an important question. Many pathogens have evolved molecular mechanisms to escape recognition by the immune system by variation of antigenic protein sequences. In this short review, we will discuss the several concepts related to selection of amino acid sequences to be included in DNA and peptide vaccines.