Effective management of district heating networks depends upon the correct forecasting of heat consumption during a certain period. In this work short-term forecasting for the amount of heat consumption is performed first to validate the three forecasting methods: partial least squares (PLS) method, artificial neural network (ANN), and support vector regression (SVR) method. Based on the results of short-term forecasting, one-week ahead forecasting was performed for the Suseo district heating network. Data of heat consumption and ambient temperature during January and February in 2007 and 2008 were employed as training elements. The heat consumption estimated was compared with actual one in the Suseo area to validate the forecasting models.
In polyolefin processes the melt flow index (MFI) is the most important control variable indicating product quality. Because of the difficulty in the on-line measurement of MFI, a large number of MFI estimation and correlation methods have been proposed. In this work, mechanical predicting methods such as partial least squares (PLS) method and support vector regression (SVR) method are employed in contrast to conventional dynamic prediction schemes. Results of predictions are compared with other prediction results obtained from various dynamic prediction schemes to evaluate predicting performance. Hourly MFIs are predicted and compared with operation data for the high density polyethylene process involving frequent grade changes. We can see that PLS and SVR exhibit excellent predicting performance even for severe operating situations accompanying frequent grade changes.
This paper presents an optimal management model for structural and operational optimization of an integrated district heating system (DHS) with multiple regional branches. A DHS consists of energy suppliers and consumers, district heating pipelines and heat storage facilities in a region. The integrated DHS considered in this paper consists of 11 regional DHS branches. In the optimal management system, production and consumption of heat, transport and storage of heat at each regional DHS are taken into account. The optimal management system is formulated as a mixed integer linear programming (MILP), where the objective is to minimize the overall cost or to maximize the profits of the integrated DHS by generating electricity while satisfying the operation constraints of heat units and networks, as well as fulfilling heating demands from consumers. Evaluation of the operation cost is based on daily operations for two months (during August and December) at each DHS located in Seoul and Gyeonggi-do in Korea. Results of numerical simulations show the increase of energy efficiency due to the introduction of the present optimal operation system.
The Charpy impact test is used to identify the transition between ductility and brittleness. The percentages of ductile and brittle fractures in steel can be evaluated based on each fracture area, which is presently determined by an analyzer with the naked eye. This method may lead to subjective judgement, and difficulty accurately quantifying the percentage. To resolve this problem, a new analysis method based on image processing is proposed in this study. A program that can automatically calculate the percentage of the ductile and brittle fractures has been developed. The analysis is performed after converting an RGB fracture image into a binary image using image processing techniques. The final binary image consists of 0 and 1 pixels. The parts with the pixel values of 1 correspond to the brittle fracture areas, and the pixel values of 0 represent the ductile fracture areas. As a result, by counting the number of 0 pixels in the entire area, it is possible to automatically calculate the percentage of ductile fracture. Using the proposed automatic fracture analysis program, it is possible to selectively distinguish only the brittle fracture from the entire fracture area, and to accurately and quantitatively calculate the percentages of ductile and brittle fractures.
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