forward control in the material and energy balance of the decision support module the neural network models are extensive there are approximately 42 inputs to each of the iron and slag models and 6 to the ilmenite model inputs include the data derived from the other modules frozen lining thickness inventory control heat loss the models auto train as the plant conditions change the philosophy used in the control of the freeze lining is that the frozen lining is viewed as an additional layer of u201c bricks u201d as long as the frozen lining is maintained the magnesite bricks will remain in tact and should not have to be replaced for many years the maintenance of an even and uniform frozen lining means that the bath size is kept constant which makes for better operational control tight control of the frozen lining thickness is achieved by making regular changes to the c reductant addition rates in both the positive frozen lining getting thinner because of an increased rate of heat production and negative frozen lining getting thicker because of an decreased rate of heat production directions to this end the amount of titanium dioxide in the furnace is used as a measure for the control over the frozen lining control the control objectives are to maintain the tio 2 in the slag of 86 with minimal deviation and to maintain the freeze lining this is achieved through manipulation of the c reductant addition rate air carbonaceous reductant to ilmenite ratio and the energy input ipr ilmenite to power ratio the system is interactive in that both of the manipulated variables influence both of the controller variables as is shown in fig3 there are two portions to the control strategy namely a feed forward portion ff and a feed back.