Jari Ruuska

Modification of buffered peptone water for improved recovery of heat-injured Salmonella Typhimurium.

Rapid detection of Salmonella in foods is often limited by the high demand for the sensitivity of detection, poor physiological conditions of the target cells, and high concentration of background flora. In this study, the conditions of nonselective enrichment cultivation were modified in order to improve the quantitative detection of heat-injured Salmonella in minced meat. The effect of the modifications on the recovery was observed by means of RNA-based sandwich hybridization, which was adjusted for the quantification of Salmonella enterica 23S rRNA in crude cell extracts. The supplementation of buffered peptone water with the enzyme-controlled substrate delivery system EnBase-Flo and ferrioxamine E was shown to improve the recovery of cells in both single strain cultures and in the presence of minced meat. The presented results can be used for the development of more efficient enrichment cultivation media for faster detection of food borne Salmonella.

NOZZLE CLOGGING PREDICTION IN CONTINUOUS CASTING OF STEEL

Abstract Submerged entry nozzle connects the tundish and the mold in the continuous casting of steel. Continuous casting is usually done in series including 3–6 successive heats. Casting of a heat takes about 35–50 minutes. The nozzle is changed after each series and the new series is started with a new nozzle. About 360–720 tons of steel goes through a nozzle during its lifetime. The casting speed and the stopper rod position give the indication of nozzle clogging, but they cannot, however, answer the question, how long time the casting can continue and when the nozzle should be changed. In this paper, feedforward neural networks with backpropagation training were used in modelling the nozzle clogging behaviour at Rautaruukki Steel mill, at Raahe site.

Pilot Plant Simulation as a Tool for More Efficient Mineral Processing

Abstract This paper describes a static flow sheet simulation model developed for the pilot scale mineral beneficiation plant taken recently into use at University of Oulu, Finland. The mini-pilot plant aims to serve as a research platform for students and researchers from universities, mining companies, and other organizations in the field of mining and mineral engineering. Currently, the mini-pilot plant is set up according to the concentrator plant at the Pyhasalmi deposit but can be configured for different types of ores and beneficiation processes as well. The simulator is built using the HSC Sim® simulation software. The simulator will support research and development of the new mini-pilot line. It can also be used in process design and optimisation. This paper presents preliminary results demonstrating the promising potential of the simulation model for replicating the pilot process behaviour. The future aim is to convert the static model into dynamic operating mode and study different control scenarios of mineral beneficiation plants using the simulator.

The Possibility to Use Optical Emission Spectrometry for Identifying the Amount of Inclusions in Steels

Optical emission spectrometry, OES, has been used widely to define the chemical element analysis. The possibility to use OES-PDA-data to identify the amount of inclusions in steel has been studied to get a quick knowledge about inclusions during the process. From the histogram of intensity levels the amount of chemical element being in steel matrix and in inclusions can be observed. The inclusions are observed to be in the tail of histogram. The setting of a correct threshold to divide the histogram into steel matrix and inclusions parts isproblematic. In this research some statistical methods and a histogram threshold method are tested for OES-PDA-data from steel samples.The research discussed in this paper has been carried out in co-operation with Ruukki Metal, at their Raahe Works in Finland. Steel samples were taken from different process stages and different process routes. The objective was to examine the amounts of inclusions at different process stages and how they differ during the process. The research was focused on the chemical elements that are most common in the inclusions. From the results it is observed that calcium and aluminium are identified pretty well, but for other elements both correct and incorrect results are obtained. This method shows a potential to identify the amount of inclusions, though further research is needed.

Flotation Model Based on Floatability Component Approach PGE Minerals Case

Abstract This paper discusses how modelling and simulation can be used in predicting the recovery of platinum group elements (PGE) in flotation process. In platinum group ores, commonly a floatability component approach is used where each element is divided in model fitting to two or three components. This approach has the problem of breaking the natural association of platinum group element in minerals, floatability components do not have any physical meaning and as the model fitting problem is ill-posed, comparison of different tests by floatability component parameters is difficult. To avoid this modelling and simulation here is based on minerals. Additionally, the mass proportion of fast floating material was fixed on the basis of mineralogical study and results achieved in laboratory flotation tests. Finally as the rougher and cleaning flotation was performed in fixed chemical regime the model fitting was done for both rougher and cleaning stages simultaneously using cascade model. It was found in this way a number of model fitted parameters decreased giving possibility to compare tests results easier but still reaching good fit between observed, modelled and simulated data. The developed model was used in screening the best flotation conditions and to forecast the metallurgical performance in full scale process.

MODEL-BASED MONITORING OF BASIC OXYGEN FURNACE

Abstract This paper discusses measurements, models and a splashing indicator for basic oxygen furnace (BOF). Also some ideas about the utilization of expert knowledge together with the proposal for model-based monitoring are given. Maintainability, modularity and transportability are briefly discussed. Research was done in co-operation with Ruukkis Raahe Steel Works in Finland.

Splashing in LD-KG-Converter

Abstract In this paper, variables effecting on splashing in LD-KG-converted are presented. The heat database was collected from one converter of Ruukkis Raahe Steel Works. Also the expert knowledge of personnel of the steel plant was utilized. Two dominant variables were found in this study, the batch size and added ferrosilicone in the early stages of the blow. By investigating more closely the heats, where the batch size and furthermore also ferrosilicone-addition were similar, it was possible to find some additional variables effecting on splashing. Some practical ideas were proposed to avoid strong splashing. Also most common variable combinations causing stronger or lower splashing are listed.

Temperature Model for LD-KG Converter

Abstract In this paper a temperature model for an LD-KG-converter is presented. The aim of the project was to develop models for predicting the temperature at the end of the blow, using the dropping sensor measurement. Modelling was done using data based methods with the data from the steel plant. Also literature and the expertise of thepersonnel in the steel plant were utilised. In off-line test runs 75 - 80% of the blows were predicted within the target window, ±10°C. The developed temperature model is in the use on the LD-KG-converters in Raahe.

Outlier Detection for 2D Temperature Data

Abstract This paper reports the study of using 2D temperature data for analysing the operation of the cooling process in the steel strip mill. Scanning pyrometers are producing data profiles of the strip in longitudinal and transversal directions. Instrument malfunctions, dust and dirt particles on the strip surface and other disturbances make the use of the measurements difficult. This makes the data pre-processing, and especially the outlier detection, of utmost importance for a reliable process and fault analysis.

The Modelling of Lateral Spread in Hot Strip Finishing Mill

Abstract This paper concerns with modelling of the lateral spread in the hot strip finishing mill. Correlation analysis is perfomed to obtain knowledge about the most significant variables affecting the lateral spread in the finishing mill. The results from correlation analysis are used to select proper input variables for modelling. Also, expert knowledge of Ruukki personnel is used throughout the study. Neural networks and linear regression models are built. In this study, some new variables have been introduced into the model and the results have improved significantly. The estimation error depends slightly on time and a simple adaptation scheme is incorporated into models to compensate for this time-dependency. The results with adapted models show about 20% better accuracy than the original results.