The quality of coke produced in a coke oven depends on the coal blend characteristics and carbonisation conditions. Scarcity of good quality coking coal made it necessary to look for techniques capable of producing superior coke from inferior coals. Precarbonisation techniques improve the bulk density of the coal charge and produce good quality coke from inferior coals. The stamp charging technique, the most effective among them requires finer crushing of coal and higher moisture as binder, both requiring additional energy. JSW Steel has adopted vibrocompaction along with non-recovery ovens for its 1 . 2 Mtpa coke production. This is a highly ecofriendly coke making process producing excellent quality coke from inferior coals. It increases the bulk density of cake, similar to stamp charging, using compaction in place of stamping. A cake density of 1 . 10 t m 23 has been achieved using the vibrocompacting technique with optimum moisture and crushing fineness. Coal blend containing up to 35% soft coal and coking coal having 32% volatile matter have been successfully used to produce a coke with coke strength after reaction .65%, coke reactivity index ,25% and M10 ,6%. The paper discusses the experience of operating vibrocompaction non-recovery coke ovens.
Sulphur in steel, which is deleterious to its mechanical properties, is reduced in hot metal by external desulphurisation using calcium carbide (CaC 2 ) based reagent at JSW Steel. With an aim of optimising the consumption of this reagent for desulphurisation, sulphur mapping was carried out at different locations, from hot metal production to steel casting, to establish optimum S levels at different stages of hot metal and steel treatment. The performance analysis of plant data for desulphurisation of hot metal shows that the degree of desulphurisation (DS) and hot metal temperature had significant impact on the consumption of reagent. Optimum consumption of desulphurisation reagent is achieved by maintaining hot metal temperature above 1350uC and the DS less than 80%. A computer based model using multiple linear regression techniques has been developed to guide the operator to ensure optimum consumption of desulphurisation reagent.
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