An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

Luonnonhaltijat ‒ haltijoita luonnonympäristössä? : Nature spirits : continuity and change : Conference of Belief Narrative Network of ISFNR, Zugdidi, Georgia 1.‒4.10.2014

Konferenssiraportti

Electrical Safety of Island Operated Low Voltage DC Network

Today, renewable energy technologies and modern power electronics have made it feasible to implement low voltage direct current (LVDC) microgrids (MGs) ca-pable to island operation. Such LVDC networks are particularly useful in remote areas. However, there are still pending issues in island operated LVDC MGs like electrical safety and controlled operation, which should be addressed before wide-scale implementation. This thesis is focused on the overall protection of an island operated LVDC network concept, including protection against electrical shocks, mains equipment protection and protection of photovoltaic (PV) power sources and battery energy storage systems (BESSs). The topic is approached through ex-amination of the safety hazards and the appropriate methods to protect against them, comprising considerations for earthing system selection and realisation of the protection system.

Item-level life-cycle model for maintenance network – from cost to additional value

The importance of industrial maintenance has been emphasized during the last decades; it is no longer a mere cost item, but one of the mainstays of business. Market conditions have worsened lately, investments in production assets have decreased, and at the same time competition has changed from taking place between companies to competition between networks. Companies have focused on their core functions and outsourced support services, like maintenance, above all to decrease costs. This new phenomenon has led to increasing formation of business networks. As a result, a growing need for new kinds of tools for managing these networks effectively has arisen. Maintenance costs are usually a notable part of the life-cycle costs of an item, and it is important to be able to plan the future maintenance operations for the strategic period of the company or for the whole life-cycle period of the item. This thesis introduces an itemlevel life-cycle model (LCM) for industrial maintenance networks. The term item is used as a common definition for a part, a component, a piece of equipment etc. The constructed LCM is a working tool for a maintenance network (consisting of customer companies that buy maintenance services and various supplier companies). Each network member is able to input their own cost and profit data related to the maintenance services of one item. As a result, the model calculates the net present values of maintenance costs and profits and presents them from the points of view of all the network members. The thesis indicates that previous LCMs for calculating maintenance costs have often been very case-specific, suitable only for the item in question, and they have also been constructed for the needs of a single company, without the network perspective. The developed LCM is a proper tool for the decision making of maintenance services in the network environment; it enables analysing the past and making scenarios for the future, and offers choices between alternative maintenance operations. The LCM is also suitable for small companies in building active networks to offer outsourcing services for large companies. The research introduces also a five-step constructing process for designing a life-cycle costing model in the network environment. This five-step designing process defines model components and structure throughout the iteration and exploitation of user feedback. The same method can be followed to develop other models. The thesis contributes to the literature of value and value elements of maintenance services. It examines the value of maintenance services from the perspective of different maintenance network members and presents established value element lists for the customer and the service provider. These value element lists enable making value visible in the maintenance operations of a networked business. The LCM added with value thinking promotes the notion of maintenance from a “cost maker” towards a “value creator”.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Optimization of Transportation System by Tailored Logistics Network: A Case Study in Automotive Industry

This thesis aims to redesign the supply chain system in an automotive industry in order to obtain space reduction in the inventory by using tailored logistics network. The redesigning process by tailored supply chain will combine all possible shipment methods including direct shipment, milk-run, milk-run via distribution center and Kanban delivery. The current supply chain system in Nissan goes rather well when the production volume is in moderate level. However, when the production volume is high, there is a capacity problem in the warehouse to accommodate all delivered parts from suppliers. Hence, the optimization of supply chain system is needed in order to obtain efficient logistics process and effective inventory consumption. The study will use primary data for both qualitative and quantitative approach as the research methods. Qualitative data will be collected by conducting interviews with people related to procurement and inventory control. Quantitative data consists of list of suppliers with their condition in several parameters which will be evaluated and analyzed by using scoring method to assign the most suitable transportation network to each suppliers for improvement of inventory reduction in a cost efficient manner.

Sustainable sourcing in global supply network: assessment of geographic challenges and management methods Case: Stora Enso

The purpose of this work was to describe and compare sourcing practices and challenges in different geographies, to discuss possible options to advance sustainability of global sourcing, and to provide examples to answer why sourcing driven by sustainability principles is so challenging to implement. The focus was on comparison between Europe & Asia & South-America from the perspective of sustainability adoption. By analyzing sourcing practices of the case company it was possible to describe main differences and challenges of each continent, available sourcing options, supplier relationships and ways to foster positive chance. In this qualitative case study gathered theoretical material was compared to extensive sourcing practices of case company in a vast supplier network. Sourcing specialist were interviewed and information provided by them analyzed in order to see how different research results and theories are reflecting reality and to find answers to proposed research questions.