This application addresses an integrated smart system to control the variables involved in the process for melting mineral concentrates. Specifically, it addresses an integrated smart system that allows the whole melting process operation to be controlled, measuring the mineralogical quality and quantity of the concentrate that is injected into the melting furnace, as well as variables such as the temperature, the level of the liquid phases and the percentage of copper within the furnace. In this manner, by reading said variables, it acts autonomously on manipulated variables, considering uncertainties, allowing a stable temperature to be maintained in the reactor, allowing products to be obtained at the required quality and controlling the liquid phases therein, among other controlled variables, to achieve efficient melting.

Disclosed is a blast furnace apparatus includes: a rotating chute; a plurality of tuyeres; a profile measurement device configured to measure surface profiles of a burden charged into the blast furnace through the rotating chute; and a blowing amount controller configured to control a blowing amount of at least one of hot blast or pulverized coal in each of the plurality of tuyeres, in which the profile measurement device includes: a radio wave distance meter installed on the blast furnace top and configured to measure the distance to the surface of the burden charged; and an arithmetic unit configured to derive the surface profiles of the burden on a basis of distance data for the entire blast furnace related to distances to the surface of the burden obtained by scanning a detection wave of the radio wave distance meter in the blast furnace in a circumferential direction.

Point values for each element that exert thermal stress on a high-temperature furnace are integrated with an operation time of the high-temperature furnace as an integration period, in which the point values are obtained by converting actual values of a thermal stress of each of elements into a reference thermal stress (reference value of the thermal stress received by the high-temperature furnace per the unit time). A point value obtained by converting a limit value of the thermal stress with which the high-temperature furnace can normally operate into the reference thermal stress is set as a lifetime thermal stress, the point value integrated with the operation time of the high-temperature furnace as the integration period is set as an accumulated thermal stress, and a remaining lifetime of the high-temperature furnace equipment is predicted from the result of subtracting the accumulated thermal stress from the lifetime thermal stress.

A system for generating a user-adjustable furnace profile, comprises a user interface configured to receive one or more materials properties from a user, a processor, and a memory with computer code instructions stored thereon. The memory is operatively coupled to the processor such that, when executed by the processor, the computer code instructions cause the system to implement communicating with a furnace to ascertain one or more thermal processes associated with the furnace, identifying one or more object characteristics associated with an object to be processed by furnace, and determining a thermal processing parameter profile of at least one thermal processing parameter corresponding to each of the thermal processes, based on (i) the one or more part characteristics and (ii) the one or more materials properties, the thermal processing parameter profile characterizing a cycle of the one or more thermal processes.

A method for adjusting furnace temperature of a reflow oven by AI, through obtaining product data of the reflow oven, obtaining initial characteristic data of a preceding work station and calculating mean values of temperatures of an upper furnace and a lower furnace, and taking the mean values as initial reflow characteristic data. Data as to first reflow characteristics of each reflow temperature zone and second reflow characteristics data of each zone are obtained, and data of the first and second reflow characteristics data are obtained. The electronic device further combines the characteristic data of the preceding work station with the combined reflow characteristics and combines results into a trained neural network model to output a temperature prediction, the oven temperature being adjusted according to the temperature prediction.

In the detection of a state where a belt-shaped body W is processed as conveyed through a furnace 10, a conveyance route R for conveying the belt-shaped body through the furnace is set and a processing condition for processing the belt-shaped body in the furnace as conveying the belt-shaped body along the conveyance route is set. A fluid analysis is performed on the assumption that a highly viscous fluid Wa having the same characteristic as that of the belt-shaped body flows along the conveyance route at the same speed as that of the belt-shaped body. The processing state of the belt-shaped body conveyed through the furnace is simulated.

A system for generating a user-adjustable furnace profile, comprises a user interface configured to receive one or more materials properties from a user, a processor, and a memory with computer code instructions stored thereon. The memory is operatively coupled to the processor such that, when executed by the processor, the computer code instructions cause the system to implement communicating with a furnace to ascertain one or more thermal processes associated with the furnace, identifying one or more object characteristics associated with an object to be processed by furnace, and determining a thermal processing parameter profile of at least one thermal processing parameter corresponding to each of the thermal processes, based on (i) the one or more part characteristics and (ii) the one or more materials properties, the thermal processing parameter profile characterizing a cycle of the one or more thermal processes.

A system for generating a user-adjustable furnace profile, comprises a user interface configured to receive one or more materials properties from a user, a processor, and a memory with computer code instructions stored thereon. The memory is operatively coupled to the processor such that, when executed by the processor, the computer code instructions cause the system to implement communicating with a furnace to ascertain one or more thermal processes associated with the furnace, identifying one or more object characteristics associated with an object to be processed by furnace, and determining a thermal processing parameter profile of at least one thermal processing parameter corresponding to each of the thermal processes, based on (i) the one or more part characteristics and (ii) the one or more materials properties, the thermal processing parameter profile characterizing a cycle of the one or more thermal processes.

Disclosed is a blast furnace apparatus includes: a rotating chute; a plurality of tuyeres; a profile measurement device configured to measure surface profiles of a burden charged into the blast furnace through the rotating chute; and a blowing amount controller configured to control a blowing amount of at least one of hot blast or pulverized coal in each of the plurality of tuyeres, in which the profile measurement device includes: a radio wave distance meter installed on the blast furnace top and configured to measure the distance to the surface of the burden charged; and an arithmetic unit configured to derive the surface profiles of the burden on a basis of distance data for the entire blast furnace related to distances to the surface of the burden obtained by scanning a detection wave of the radio wave distance meter in the blast furnace in a circumferential direction.

A method and a system for determining a mass of feedstock discharged by a conveyor during a first time interval t are disclosed. The method includes taking successive digital images of the feedstock in a specific zone of the conveyor being separated by a second time interval t of smaller duration than the first time interval t, for each of the second time intervals t: computing the advancing distance of a sub-volume of feedstock during the second time interval t in the specific zone of the conveyor by numerical treatment of the two successive images associated with the second time interval t; determining at least one transversal height profile of the sub-volume of feedstock; and determining an effective feedstock density for the sub-volume of feedstock. The method further includes computing the mass of feedstock discharged by the conveyor during the first time interval t into the metallurgical furnace on the basis of the advancing distance, the at least one transversal height profile and the effective feedstock density, computed or determined for each of the second time intervals t.