Pattern Identification for Feed Control Strategy Using Fuzzy Neural Algorithm

摘要

Smelters have a difficult task in the reduction of the green house gas emission (GHG) by decreasing anode effect. When alumina buck concentration reaches critical levels an anode effects occurs and express itself as a suddenly increase in voltage. Vertical Stub Soderberg (VSS) Side Break pots had no improvements on alumina control in the past decade due the complexity of the problem. The pot is fed every two hours with a fix amount of alumina and the actual feed adjustment is done in a manual daily basis. Based on Prebaked feed control strategy; a model was developed based on a pattern identification algorithm using neuro-fuzzy networks. This algorithm will determine the patterns of the alumina concentration using the pseudo resistance shape curve of the pot. This information provides the amount of alumina that will be fed in the next cycle without mucking the pot and avoiding anode effect.