Study of energy consumption pattern in the kudremukh plant of KIOCL and optimisation of same

Abstract

Kudremukh Iron ore Company (K.I.O.C.L) is engaged in the business of iron ore mining, concentrating the ore, and exporting the concentrate directly and converting part of it into pellets. Due to high level of mechanization, energy cost has become one of the major factors in the cost of final product (amounting to about 47%). Therefore any saving in energy consumption will increase the profit substantially. An attempt has been made in this report to find out the relationship between different process parameters (such as Total production in a month, Quality of ore fed to plant in a particular month. Type of concentrate produced in respective month etc.) and energy consumption in different areas as well as with specific energy (total energy consumption per ton of concentrate produced). Data for this purpose has been collected for the last five years and analysed using statistical software package S.P.S.S. to find out relationship between different parameters and energy consumption. These results are used to find the optimum level for these parameters, so that specific energy can be kept as low as possible. About Company: Kudremukh Iron Ore Company Ltd. is a public sector company under the ministry of steel. It was formed in 1976 and started its commercial operation in 1980.At present company's iron ore concentrate is being exported to Iran, Japan and China and iron ore pellets are being exported to Taiwan, Australia, China, Turkey, Indonesia and a small portion is being supplied in domestic market. The process involved is of mining low-grade ore (33% iron content) and enriching it in to concentrate of 68% iron content by different processes in the concentrator plant. This concentrated iron ore is transported to Mangalore through a slurry pipeline in slurry form. There a portion of it is converted into pellets and finally pellets and remaining portion of concentrate are being exported. Problem: The process of concentration of iron ore is of highly energy consuming type. The Specific energy of concentrate produced varies significantly from time to time. This is due to various process parameters such as total production in the. month, weight recovery in the month (a measure of quality of ore (cd to plant) and type of concentrate produced in the respective month. To minim i/e the specific energy consumption, optimum levels for these process parameters are required to be maintained. We have defined the problem as to find out this optimum level by analysing last five years data using statistical tools. Analysis: Details of specific energy consumption as well as energy consumption indifferent areas along with process parameters such as production in the month, weight recovery, pellet feed for each month were collected for last five years. This data was subjected to regression analysis, first by taking one process parameter at a time for all the areas and for all five years separately and then taking all process parameters together to find out if any significant relationship exists. However during the analysis we have found that even though some sort of relationship exists between energy consumption and process parameters, there is a significant variation in these relationships from year to year and from area to area. To capture the effect of time if any, on these relationships we have introduced another variable representing time. Regression analysis was repeated along with this new variable which gave us encouraging results with various process parameters showing significant relationships with energy consumption per ton in different areas as well as with specific energy. To get optimum result for energy consumption we have introduced squares of different process parameters and regression analysis was done. However some process parameters did not show significant relationship with energy consumption. Finally we have decided to go for regression analysis with stepwise regression method so that significant variable can be identified and final equation for energy consumption in different areas as well as specific energy was obtained.