Kalman Filtering in Online Paper Quality Assessment

Controlling the quality variables (such as basis weight, moisture etc.) is a vital part of making top quality paper or board. In this thesis, an advanced data assimilation tool is applied to the quality control system (QCS) of a paper or board machine. The functionality of the QCS is based on quality observations that are measured with a traversing scanner making a zigzag path. The basic idea is the following: The measured quality variable has to be separated into its machine direction (MD) and cross direction (CD) variations due to the fact that the QCS works separately in MD and CD. Traditionally this is done simply by assuming one scan of the zigzag path to be the CD profile and its mean value to be one point of the MD trend. In this thesis, a more advanced method is introduced. The fundamental idea is to use the signals’ frequency components to represent the variation in both CD and MD. To be able to get to the frequency domain, the Fourier transform is utilized. The frequency domain, that is, the Fourier components are then used as a state vector in a Kalman filter. The Kalman filter is a widely used data assimilation tool to combine noisy observations with a model. The observations here refer to the quality measurements and the model to the Fourier frequency components. By implementing the two dimensional Fourier transform into the Kalman filter, we get an advanced tool for the separation of CD and MD components in total variation or, to be more general, for data assimilation. A piece of a paper roll is analyzed and this tool is applied to model the dataset. As a result, it is clear that the Kalman filter algorithm is able to reconstruct the main features of the dataset from a zigzag path. Although the results are made with a very short sample of paper roll, it seems that this method has great potential to be used later on as a part of the quality control system.

Automated formulation of optimisation models for steel beam structures

Over 70% of the total costs of an end product are consequences of decisions that are made during the design process. A search for optimal cross-sections will often have only a marginal effect on the amount of material used if the geometry of a structure is fixed and if the cross-sectional characteristics of its elements are property designed by conventional methods. In recent years, optimalgeometry has become a central area of research in the automated design of structures. It is generally accepted that no single optimisation algorithm is suitable for all engineering design problems. An appropriate algorithm, therefore, mustbe selected individually for each optimisation situation. Modelling is the mosttime consuming phase in the optimisation of steel and metal structures. In thisresearch, the goal was to develop a method and computer program, which reduces the modelling and optimisation time for structural design. The program needed anoptimisation algorithm that is suitable for various engineering design problems. Because Finite Element modelling is commonly used in the design of steel and metal structures, the interaction between a finite element tool and optimisation tool needed a practical solution. The developed method and computer programs were tested with standard optimisation tests and practical design optimisation cases. Three generations of computer programs are developed. The programs combine anoptimisation problem modelling tool and FE-modelling program using three alternate methdos. The modelling and optimisation was demonstrated in the design of a new boom construction and steel structures of flat and ridge roofs. This thesis demonstrates that the most time consuming modelling time is significantly reduced. Modelling errors are reduced and the results are more reliable. A new selection rule for the evolution algorithm, which eliminates the need for constraint weight factors is tested with optimisation cases of the steel structures that include hundreds of constraints. It is seen that the tested algorithm can be used nearly as a black box without parameter settings and penalty factors of the constraints.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Capacity of an I-beam made of S960 QC based on Eurocode 3

The capacity of beams is a very important factor in the study of durability of structures and structural members. The capacity of a high-strength steel I-beam made of S960 QC was investigated in this study. The investigation included assessment of the service limits and ultimate limits of the steel beam. The thesis was done according to European standards for steel structures, Eurocode 3. An analytical method was used to determine the throat thickness, deformation, elastic and plastic moment capacities as well as the fatigue life of the beam. The results of the analytical method were compared with those obtained by Finite Element Analysis (FEA). Elastic moment capacity obtained by the analytical method was 172 kNm. FEA and the analytical method predicted the maximum lateral-torsional buckling (LTB) capacity in the range of 90-93 kNm and the probability of failure as a result of LTB is estimated to be 50%. The lateral buckling capacity meant that the I-beam can carry a safe load of 300 kN instead of the initial load of 600 kN. The beam is liable to fail shortly after exceeding the elastic moment capacity. Based on results in of the different approaches, it was noted that FEA predicted higher deformation values on the load-deformation curve than the analytical results. However, both FEA and the analytical methods predicted identical results for nominal stress range and moment capacities. Fatigue life was estimated to be in the range of 53000-64000 cycles for bending stress range using crack propagation equation and strength-life approach. As Eurocode 3 is limited to steel grades up to S690, results for S960 must be verified with experimental data and appropriate design rules.

Fundraising and investor relations in social enterprise: Marketing your way out of the pioneer gap in African off-grid renewable energy

Social enterprises apply the best of business for the pursuit of social or environmental mission while also generating revenues. Globally, nearly 1,3 billion people lack access to electricity, as well as another billion having access to only low quality and infrequent electricity. Off-grid renewable energy, like solar, will increasingly have a key role in the solution of the energy access issue. The pioneer gap in off-grid renewable energy consists of financing (or funding) gaps and capacity gaps, to do with both the early stage of the enterprises in question, as well as the early stage of the whole industry. The gaps are emphasised by specific characteristics of off-grid renewable energy business models and the requirements of operating in bottom-of-the-pyramid markets. The marketing perspective to fundraising is chosen to uncover the possible role enterprises themselves have in bridging the pioneer gap. The purpose of this thesis is to study how social enterprises operating in off-grid renewable energy in Africa utilise marketing activities in their investor relations in bridging the pioneer gap. This main research question is divided into the following sub-questions:  How does the pioneer gap affect fundraising for these enterprises?  How are the funding needs for these enterprises characterised?  How do these enterprises build trust in their investor relations? The theoretic framework is built on relationship marketing and investor relations, with an emphasis on creation of trust. The research is conducted as a thematical case study. Primary data is gathered via semi-structured interviews with six solar energy companies and two accelerators. According to the findings, the main components affecting trust-creation are diminished information asymmetry and perceived risk, mission alignment as well as a personal fit or relationship with the investor. Therefore, an enterprise can utilise e.g. the following marketing activities in their investor relations to bridge the pioneer gap: ensuring investor material, the enterprise story and presenting of them is clear, concise and complete to “package” the enterprise as an investment; taking investor needs and motivations into account as well as utilising existing investors as ambassadors.

Northern agriculture : constraints and responses to global climate change

Selostus: Maatalous pohjoisilla äärialueilla: ilmastolliset rajoitukset ja ilmaston muutosten vaikutukset viljelyyn