Container logistic innovations in forest-energy sector: Markets, future service concepts and technical improvements

The report presents the results of the commercialization project called the Container logistic services for forest bioenergy. The project promotes new business that is emerging around overall container logistic services in the bioenergy sector. The results assess the European markets of the container logistics for biomass, enablers for new business creation and required service bundles for the concept. We also demonstrate the customer value of the container logistic services for different market segments. The concept analysis is based on concept mapping, quality function deployment process (QFD) and business network analysis. The business network analysis assesses key shareholders and their mutual connections. The performance of the roadside chipping chain is analysed by the logistic cost simulation, RFID system demonstration and freezing tests. The EU has set the renewable energy target to 20 % in 2020 of which Biomass could account for two-thirds. In the Europe, the production of wood fuels was 132.9 million solid-m3 in 2012 and production of wood chips and particles was 69.0 million solidm3. The wood-based chips and particle flows are suitable for container transportation providing market of 180.6 million loose- m3 which mean 4.5 million container loads per year. The intermodal logistics of trucks and trains are promising for the composite containers because the biomass does not freeze onto the inner surfaces in the unloading situations. The overall service concept includes several packages: container rental, container maintenance, terminal services, RFID-tracking service, and simulation and ERP-integration service. The container rental and maintenance would provide transportation entrepreneurs a way to increase the capacity without high investment costs. The RFID-concept would lead to better work planning improving profitability throughout the logistic chain and simulation supports fuel supply optimization.

Influence of surface functionalization on the behavior of silica nanoparticles in biological systems

Personalized nanomedicine has been shown to provide advantages over traditional clinical imaging, diagnosis, and conventional medical treatment. Using nanoparticles can enhance and clarify the clinical targeting and imaging, and lead them exactly to the place in the body that is the goal of treatment. At the same time, one can reduce the side effects that usually occur in the parts of the body that are not targets for treatment. Nanoparticles are of a size that can penetrate into cells. Their surface functionalization offers a way to increase their sensitivity when detecting target molecules. In addition, it increases the potential for flexibility in particle design, their therapeutic function, and variation possibilities in diagnostics. Mesoporous nanoparticles of amorphous silica have attractive physical and chemical characteristics such as particle morphology, controllable pore size, and high surface area and pore volume. Additionally, the surface functionalization of silica nanoparticles is relatively straightforward, which enables optimization of the interaction between the particles and the biological system. The main goal of this study was to prepare traceable and targetable silica nanoparticles for medical applications with a special focus on particle dispersion stability, biocompatibility, and targeting capabilities. Nanoparticle properties are highly particle-size dependent and a good dispersion stability is a prerequisite for active therapeutic and diagnostic agents. In the study it was shown that traceable streptavidin-conjugated silica nanoparticles which exhibit a good dispersibility could be obtained by the suitable choice of a proper surface functionalization route. Theranostic nanoparticles should exhibit sufficient hydrolytic stability to effectively carry the medicine to the target cells after which they should disintegrate and dissolve. Furthermore, the surface groups should stay at the particle surface until the particle has been internalized by the cell in order to optimize cell specificity. Model particles with fluorescently-labeled regions were tested in vitro using light microscopy and image processing technology, which allowed a detailed study of the disintegration and dissolution process. The study showed that nanoparticles degrade more slowly outside, as compared to inside the cell. The main advantage of theranostic agents is their successful targeting in vitro and in vivo. Non-porous nanoparticles using monoclonal antibodies as guiding ligands were tested in vitro in order to follow their targeting ability and internalization. In addition to the targeting that was found successful, a specific internalization route for the particles could be detected. In the last part of the study, the objective was to clarify the feasibility of traceable mesoporous silica nanoparticles, loaded with a hydrophobic cancer drug, being applied for targeted drug delivery in vitro and in vivo. Particles were provided with a small molecular targeting ligand. In the study a significantly higher therapeutic effect could be achieved with nanoparticles compared to free drug. The nanoparticles were biocompatible and stayed in the tumor for a longer time than a free medicine did, before being eliminated by renal excretion. Overall, the results showed that mesoporous silica nanoparticles are biocompatible, biodegradable drug carriers and that cell specificity can be achieved both in vitro and in vivo.

Production planning and optimization of a pulp mill: introduction of the optimisation based scheduling software for pulp industry

The objective of this project was to introduce a new software product to pulp industry, a new market for case company. An optimization based scheduling tool has been developed to allow pulp operations to better control their production processes and improve both production efficiency and stability. Both the work here and earlier research indicates that there is a potential for savings around 1-5%. All the supporting data is available today coming from distributed control systems, data historians and other existing sources. The pulp mill model together with the scheduler, allows what-if analyses of the impacts and timely feasibility of various external actions such as planned maintenance of any particular mill operation. The visibility gained from the model proves also to be a real benefit. The aim is to satisfy demand and gain extra profit, while achieving the required customer service level. Research effort has been put both in understanding the minimum features needed to satisfy the scheduling requirements in the industry and the overall existence of the market. A qualitative study was constructed to both identify competitive situation and the requirements vs. gaps on the market. It becomes clear that there is no such system on the marketplace today and also that there is room to improve target market overall process efficiency through such planning tool. This thesis also provides better overall understanding of the different processes in this particular industry for the case company.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

CFD modeling of dispersion water feed in wastewater cleaning application

Fluid particle breakup and coalescence are important phenomena in a number of industrial flow systems. This study deals with a gas-liquid bubbly flow in one wastewater cleaning application. Three-dimensional geometric model of a dispersion water system was created in ANSYS CFD meshing software. Then, numerical study of the system was carried out by means of unsteady simulations performed in ANSYS FLUENT CFD software. Single-phase water flow case was setup to calculate the entire flow field using the RNG k-epsilon turbulence model based on the Reynolds-averaged Navier-Stokes (RANS) equations. Bubbly flow case was based on a computational fluid dynamics - population balance model (CFD-PBM) coupled approach. Bubble breakup and coalescence were considered to determine the evolution of the bubble size distribution. Obtained results are considered as steps toward optimization of the cleaning process and will be analyzed in order to make the process more efficient.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

Influence of Pulsed-Electric Field Irradiation in Crystallization

The growing pharmaceutical interest, among others, in the polymorphic composition of the emerging solid end-products from production processes has been traced to the need for attainment of high product purity. This is more so as the presence of different polymorphs may constitute physical impurity of the product. Hence, the need for optimization of the yield of desired product component(s) through controlled crystallization kinetics for instance. This study was carried out to investigate the impact of pulsed electric field (PEF) irradiation on the crystal morphology of glycine obtained by cooling crystallization (without seeding) from commercial glycine sample in distilled deionized water solution. In doing so, three different pulse frequencies (294, 950 and 145 Hz) and a case without PEF were studied at three cooling rates (5, 10 and 20 ºC/h). The crystal products obtained were analyzed for polymorphic composition by powder x-ray diffraction (PXRD) and Fourier transform infrared (FTIR) spectroscopy while the particles characterization was done on Morphologi G3. The results obtained from this study showed that pulsed electric field irradiation had significant impact on metastability of the aqueous solution as well as on the polymorphic composition of the end product. With increasing PEF frequency applied, nucleation started earlier and the γ-glycine polymorph content of the product crystals increased. These were found to have been aided by cooling rate, as the most significant effect was observed at 5 ºC/h. It was also discovered that PEF application had no measurable impact on the pH of the aqueous solution as well as the size distribution of the particles. Cooling on the contrary was believed to be responsible for the broadening of the particle size distribution with a downward shift of the lower limit of the raw material from about 100 μm to between 10 and 50 μm.

Stability Analysis in Multicriteria Discrete Portfolio Optimization.

Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research. While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing. Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters). This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions. In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Optimization of a dynamic district heating power plant

The purpose of this Thesis is to find the most optimal heat recovery solution for Wärtsilä’s dynamic district heating power plant considering Germany energy markets as in Germany government pays subsidies for CHP plants in order to increase its share of domestic power production to 25 % by 2020. Different heat recovery connections have been simulated dozens to be able to determine the most efficient heat recovery connections. The purpose is also to study feasibility of different heat recovery connections in the dynamic district heating power plant in the Germany markets thus taking into consideration the day ahead electricity prices, district heating network temperatures and CHP subsidies accordingly. The auxiliary cooling, dynamical operation and cost efficiency of the power plant is also investigated.

Adenovirus-mediated siRNA targeting TNF-α and overexpression of bone morphogenetic protein-2 promotes early osteoblast differentiation on a cell model of Ti particle-induced inflammatory response in vitro

Wear particles are phagocytosed by macrophages and other inflammatory cells, resulting in cellular activation and release of proinflammatory factors, which cause periprosthetic osteolysis and subsequent aseptic loosening, the most common causes of total joint arthroplasty failure. During this pathological process, tumor necrosis factor-alpha (TNF-α) plays an important role in wear-particle-induced osteolysis. In this study, recombination adenovirus (Ad) vectors carrying both target genes [TNF-α small interfering RNA (TNF-α-siRNA) and bone morphogenetic protein 2 (BMP-2)] were synthesized and transfected into RAW264.7 macrophages and pro-osteoblastic MC3T3-E1 cells, respectively. The target gene BMP-2, expressed on pro-osteoblastic MC3T3-E1 cells and silenced by the TNF-α gene on cells, was treated with titanium (Ti) particles that were assessed by real-time PCR and Western blot. We showed that recombinant adenovirus (Ad-siTNFα-BMP-2) can induce osteoblast differentiation when treated with conditioned medium (CM) containing RAW264.7 macrophages challenged with a combination of Ti particles and Ad-siTNFα-BMP-2 (Ti-ad CM) assessed by alkaline phosphatase activity. The receptor activator of nuclear factor-κB ligand was downregulated in pro-osteoblastic MC3T3-E1 cells treated with Ti-ad CM in comparison with conditioned medium of RAW264.7 macrophages challenged with Ti particles (Ti CM). We suggest that Ad-siTNFα-BMP-2 induced osteoblast differentiation and inhibited osteoclastogenesis on a cell model of a Ti particle-induced inflammatory response, which may provide a novel approach for the treatment of periprosthetic osteolysis.

Optimization of the RNeasy Mini Kit to obtain high-quality total RNA from sessile cells of Staphylococcus aureus

Biofilm formed by Staphylococcus aureus is considered an important virulence trait in the pathogenesis of infections associated with implantable medical devices. Gene expression analyses are important strategies for determining the mechanisms involved in production and regulation of biofilm. Obtaining intact RNA preparations is the first and most critical step for these studies. In this article, we describe an optimized protocol for obtaining total RNA from sessile cells of S. aureus using the RNeasy Mini Kit. This method essentially consists of a few steps, as follows: 1) addition of acetone-ethanol to sessile cells, 2) lysis with lysostaphin at 37°C/10 min, 3) vigorous mixing, 4) three cycles of freezing and thawing, and 5) purification of the lysate in the RNeasy column. This simple pre-kit procedure yields high-quality total RNA from planktonic and sessile cells of S. aureus.

Use of the Wasserman equation in optimization of the duration of the power ramp in a cardiopulmonary exercise test: a study of Brazilian men

This study aimed to analyze the agreement between measurements of unloaded oxygen uptake and peak oxygen uptake based on equations proposed by Wasserman and on real measurements directly obtained with the ergospirometry system. We performed an incremental cardiopulmonary exercise test (CPET), which was applied to two groups of sedentary male subjects: one apparently healthy group (HG, n=12) and the other had stable coronary artery disease (n=16). The mean age in the HG was 47±4 years and that in the coronary artery disease group (CG) was 57±8 years. Both groups performed CPET on a cycle ergometer with a ramp-type protocol at an intensity that was calculated according to the Wasserman equation. In the HG, there was no significant difference between measurements predicted by the formula and real measurements obtained in CPET in the unloaded condition. However, at peak effort, a significant difference was observed between oxygen uptake (V˙O2)peak(predicted)and V˙O2peak(real)(nonparametric Wilcoxon test). In the CG, there was a significant difference of 116.26 mL/min between the predicted values by the formula and the real values obtained in the unloaded condition. A significant difference in peak effort was found, where V˙O2peak(real)was 40% lower than V˙O2peak(predicted)(nonparametric Wilcoxon test). There was no agreement between the real and predicted measurements as analyzed by Lin’s coefficient or the Bland and Altman model. The Wasserman formula does not appear to be appropriate for prediction of functional capacity of volunteers. Therefore, this formula cannot precisely predict the increase in power in incremental CPET on a cycle ergometer.

Optimization of random amplified polymorphic DNA protocol for molecular identification of Lophius gastrophysus

Lophius gastrophysus has important commercial value in Brazil particularly for foreign trade. In this study, we described the optimization of Random Amplified Polymorphic DNA (RAPD) protocol for identification of L. gastrophysus. Different conditions (annealing temperatures, MgCl concentrations, DNA quantity) were tested to find reproducible and adequate profiles. Amplifications performed with primers A01, ² A02 and A03 generate the best RAPD profiles when the conditions were annealing temperature of 36ºC, 25 ng of DNA quantity and 2.5 mM MgCl2. Exact identification of the species and origin of marine products is necessary and RAPD could be used as an accurate, rapid tool to expose commercial fraud.

Development and characterization of thin printed films for gas sensing applications

The monitoring and control of hydrogen sulfide (H2S) level is of great interest for a wide range of application areas including food quality control, defense and antiterrorist applications and air quality monitoring e.g. in mines. H2S is a very poisonous and flammable gas. Exposure to low concentrations of H2S can result in eye irritation, a sore throat and cough, shortness of breath, and fluid retention in the lungs. These symptoms usually disappear in a few weeks. Long-term, low-level exposure may result in fatigue, loss of appetite, headache, irritability, poor memory, and dizziness. Higher concentrations of 700 - 800 ppm tend to be fatal. H2S has a characteristic smell of rotten egg. However, because of temporary paralysis of olfactory nerves, the smelling capability at concentrations higher than 100 ppm is severely compromised. In addition, volatile H2S is one of the main products during the spoilage of poultry meat in anaerobic conditions. Currently, no commercial H2S sensor is available which can operate under anaerobic conditions and can be easily integrated in the food packaging. This thesis presents a step-wise progress in the development of printed H2S gas sensors. Efforts were made in the formulation, characterization and optimization of functional printable inks and coating pastes based on composites of a polymer and a metal salt as well as a composite of a metal salt and an organic acid. Different processing techniques including inkjet printing, flexographic printing, screen printing and spray coating were utilized in the fabrication of H2S sensors. The dispersions were characterized by measuring turbidity, surface tension, viscosity and particle size. The sensing films were characterized using X-ray photoelectron spectroscopy, X-ray diffraction, atomic force microscopy and an electrical multimeter. Thin and thick printed or coated films were developed for gas sensing applications with the aim of monitoring the H2S concentrations in real life applications. Initially, a H2S gas sensor based on a composite of polyaniline and metal salt was developed. Both aqueous and solvent-based dispersions were developed and characterized. These dispersions were then utilized in the fabrication of roll-to-roll printed H2S gas sensors. However, the humidity background, long term instability and comparatively lower detection limit made these sensors less favourable for real practical applications. To overcome these problems, copper acetate based sensors were developed for H2S gas sensing. Stable inks with excellent printability were developed by tuning the surface tension, viscosity and particle size. This enabled the formation of inkjet-printed high quality copper acetate films with excellent sensitivity towards H2S. Furthermore, these sensors showed negligible humidity effects and improved selectivity, response time, lower limit of detection and coefficient of variation. The lower limit of detection of copper acetate based sensors was further improved to sub-ppm level by incorporation of catalytic gold nano-particles and subsequent plasma treatment of the sensing film. These sensors were further integrated in an inexpensive wirelessly readable RLC-circuit (where R is resistor, L is inductor and C is capacitor). The performance of these sensors towards biogenic H2S produced during the spoilage of poultry meat in the modified atmosphere package was also demonstrated in this thesis. This serves as a proof of concept that these sensors can be utilized in real life applications.