Method and apparatus for managing heat energy in a metal casting plant

A method for managing heat energy in a metal casting plant includes executing a local control optimization model to control mass of solid metal charges to each modular melting furnace. The local control optimization model is configured to achieve a commanded total mass of molten material and coincidentally minimize waste heat for each of the modular melting furnaces. The method for managing heat energy in the metal casting plant further includes executing a system control optimization model to manage operation of a heat energy recovery system. The system control optimization model is configured to manage the operation of the heat energy recovery system including transferring the waste heat from the modular melting furnaces to a plurality of heat demand centers while minimizing total loss of the waste heat in the metal casting plant.

TECHNICAL FIELD

This disclosure is related to heat energy management within metal casting facilities.

BACKGROUND

Metal casting plants use heat to melt metal ingots, chips, and other solid forms, to provide molten metal that is transferred to casting locations. The molten metal is transported to casting locations for molding into a final part. The melting process generates waste heat. Casting plants (i.e., foundries) can be complex industrial facilities that include equipment and processes that demand heating and cooling. This demand for heat energy can be satisfied in part by utilizing the waste heat from the melting process, thus increasing overall energy efficiency of the casting plant.

SUMMARY

A metal casting plant including a plurality of modular melting furnaces is described. A method for managing heat energy in the metal casting plant includes executing a local control optimization model to control mass of solid metal charges to each modular melting furnace. The local control optimization model is configured to achieve a commanded total mass of molten material and coincidentally minimize waste heat for each of the modular melting furnaces. The method for managing heat energy in the metal casting plant further includes executing a system control optimization model to manage operation of a heat energy recovery system. The system control optimization model is configured to manage the operation of the heat energy recovery system including transferring the waste heat from the modular melting furnaces to a plurality of heat demand centers while minimizing total loss of the waste heat in the metal casting plant.

DETAILED DESCRIPTION

Referring now to the drawings, wherein the showings are for the purpose of illustrating certain exemplary embodiments only and not for the purpose of limiting the same,FIG. 1schematically illustrates an exemplary modular metal melting furnace10configured to convert metal in solid form to molten form by heating the solid metal. In one embodiment, the modular metal melting furnace10is a small furnace device that is dedicated to a specific casting line and can be readily relocated. The metal melting furnace10includes a melt stack12fluidly coupled to a furnace14. A preferred molten metal charge Yt25is output from the metal melting furnace10, measurable in units of mass (Kg) or another suitable metric, and may be dictated by a production schedule. Raw material in the form of solid metal is input to the metal melting furnace10in one of a plurality of solid forms including, by way of example, metal charges including ingots21, metal chips22, gates/sprues23removed and recycled from previously cast parts, and other forms24. Each of the aforementioned metal charges21,22,23, and24is measurable in units of mass (Kg) or another suitable metric. A turbine generator preferably includes a gas turbine30coupled via a driveshaft to an electric generator40, and consumes air31and natural gas33or another combustible gas to generate exhaust heat35and torque37. The exhaust heat35is input to the melt stack12to preheat the incoming solid metal in the melt stack12. The torque37drives the electric generator40, which in turn generates electric power45that is transferred to the furnace14and converted to heat to melt the metal. In one embodiment, the configuration for melting the metal is a cogeneration process. Heat loss in the form of generated waste heat15is determined, and includes any heat generated by the gas turbine30and the electric generator40beyond that which is necessary to melt the metal charges21,22,23, and24, and may be accounted for as described herein.

The modular metal melting furnace10is subject to an optimization process that manages the metal charges21,22,23, and24to achieve the preferred molten metal charge25that meets a molten metal demand from casting production in the metal melting furnace10. The optimization process is subject to a limitation of minimizing the generated waste heat15. The optimization process has the following objective function in EQ. 1:

minX⁢∑i⁢∑t⁢Wi⁢Xit[1]
whereini indicates one of the solid metal charges including metal charges21,22,23, and24;time t indicates a time index, e.g., a period of a production shift, a day, or another suitable time period;Xitrepresents a mass of solid metal for the indicated ithone of the metal charges21,22,23, and24at time t; andWiindicates waste heat (J) generated per unit of the ithone of the metal charges21,22,23, and24.

The objective function set forth in EQ. 1 is subject to the constraint that the sum of the solid metal from the metal charges21,22,23, and24at time t must be at least equal to the preferred total molten metal charge25at time t, represented as follows in EQ. 2:

∑i⁢Xit≥Yt⁢∀t[2]whereinYtis the preferred molten metal charge25that meets the total demand for molten metal at time t to achieve melting production in the metal melting furnace10; andXitrepresents the mass of solid metal for the indicated ithone of the metal charges21,22,23, and24at time t.

The preferred decision variable is Xit. Thus the solution set indicates the mass of solid metal for each of the metal charges21,22,23, and24at time t.

In one embodiment, linear programming is employed to minimize the objective function set forth in EQ. 1 subject to the constraint set forth in EQ. 2 to determine the mass of metal for each of the metal charges21,22,23, and24at time t, thus minimizing the generated waste heat15while meeting the production schedule using the preferred molten metal charge25that meets the total demand for molten metal at time t to achieve casting production for the specific modular metal melting furnace10.

FIG. 2schematically illustrates a flow diagram representative of a portion of a heat energy recovery system200for a metal casting plant including a plurality of modular metal melting furnaces110,120,130, each of which is analogous to the modular metal melting furnace10described with reference toFIG. 1. The described system is illustrative and not restrictive. Operation of the modular metal melting furnaces110,120,130have corresponding amounts of generated waste heat115,125, and135, respectively, each which is analogous to the waste heat15shown with reference toFIG. 1. The generated waste heat115,125, and135are is conveyed to corresponding nodes140,150, and160of the heat energy recovery system200. The nodes140,150, and160represent physical points within a network of conduits wherein heat energy in the form of hot exhaust gases from the modular metal melting furnaces110,120,130are distributed.

The generated waste heats115,125, and135from the modular metal melting furnaces110,120,130are distributed to a plurality of usage distribution centers, including space heating indicated by node210, space cooling indicated by node220, process heating indicated by node230, and process cooling indicated by node240. The usage distribution centers have integrated energy conversion processes or devices to convert waste heat energy in the form of hot gases into another form of energy as dictated by process demand requirements. Heat exchangers and absorption chillers are examples of such devices. Other usage distribution centers may be employed depending upon the configuration of the heat energy recovery system200. The distribution of the generated waste heat115,125, and135to the plurality of usage distribution centers indicated by nodes210,220,230, and240has accompanying heat losses that are indicated by arcs141,142,143,151,152,153,154,161,162, and163. The usage distribution center employing the aforementioned arcs is illustrative. Other configurations of arcs may be employed.

Heat transfers from the nodes210,220,230, and240to a plurality of heat demand centers indicated by nodes250,260,270, and280. The heat demand centers have integrated energy conversion processes or devices to convert waste heat energy in the form of hot gases into another form of energy as dictated by process demand requirements. Heat exchangers and absorption chillers are examples of such devices. Each distribution from the usage distribution centers indicated by nodes210,220,230, and240to the heat demand centers indicated by nodes250,260,270, and280has accompanying heat losses that are indicated by arcs211,221,231,232,233,241,242, and243. Each of the heat demand centers indicated by nodes250,260,270, and280represents a piece of equipment or a process that has one or more demands for heat, including heat demand251associated with node250, heat demand261associated with node260, heat demands273,275, and277associated with demand centers272,274, and276, respectively, of node270, and heat demands283,285, and287associated with demand centers282,284, and286, respectively, of node280. In one embodiment, the heat demand center indicated by node250is associated with space heating, the heat demand center indicated by node260is associated with space cooling, the heat demand center indicated by node270is associated with process heating, and the heat demand center indicated by node280is associated with process cooling. Alternative configurations of usage distribution centers and heat demand centers may be employed with similar effect.

The heat energy recovery system200is subject to an optimization process that is employed to manage transfer of the generated waste heat therethrough.

The optimization process may be configured with an objective function as follows in EQ. 3.

minX,Y⁢∑i⁢∑j⁢∑t⁢Lij⁢Xijt+∑j⁢∑k⁢∑t⁢Rjk⁢Yjkt[3]
whereinLij indicates heat loss per unit of heat energy transferred from one of the modular metal melting furnaces110,120,130to one of the usage distribution centers indicated by nodes210,220,230, and240at time t;Xijtindicates a quantity of heat (J) from one of the modular metal melting furnaces110,120,130delivered to one of the intermediate nodes j, i.e., one of the usage distribution centers indicated by nodes210,220,230, and240at time t;Rjkindicates heat loss per unit of heat energy transferred from one of the usage distribution centers indicated by nodes210,220,230, and240to one of the heat demand centers indicated by nodes250,260,270, and280indicated by the aforementioned arcs; andYjktindicates a quantity of heat delivered from one of the intermediate nodes j, i.e., one of the usage distribution centers indicated by nodes210,220,230, and240to one of the heat demand centers k at time t.

The preferred decision variables include Xijt, i.e., the quantities of heat from the modular melting furnaces115,125, and135delivered to the intermediate nodes, and Yjkt, i.e., the quantities of heat delivered from the intermediate nodes j, i.e., one of the usage distribution centers indicated by nodes210,220,230, and240to the heat demand centers k at time t.

The objective function set forth in EQ. 3 is subjected to a waste heat generation constraint, as follows in EQ. 4:

∑j⁢Xijt≤Hit⁢∀i,t[4]whereinHitindicates a total supply of generated waste heat at time t from all the modular metal melting furnaces110,120,130.

Thus at each time point including a planning horizon, the solution to EQ. 3 is subject to the limitation that a total quantity of generated waste heat115,125, and135at time t delivered from the modular metal melting furnaces110,120,130does not exceed its supply at time t dictated by the production schedule.

Operation of the system includes a constraint to ensure that the heat demands251,261,273,275,277,283,285, and287are satisfied from the heat demand centers indicated by nodes250,260,270, and280at each time t, indicated as follows in EQ. 5.

∑j⁢Yjkt=Dkt⁢∀k,t[5]
whereink indicates the heat demands251,261,273,275,277,283,285, and287;Dktindicates heat demand associated with the selected one of the heat demands251,261,273,275,277,283,285, and287of the demand nodes250,260,270, and280at time t; andYjktindicates a quantity of heat delivered from intermediate node j, i.e., one of the usage distribution centers indicated by nodes210,220,230, and240to demand node k at time t.

Thus for each time t in the planning horizon, the solution to EQ. 3 is subject to the limitation that the demand of each demand node is fulfilled for all time points in the planning horizon.

Operation of the system includes an individual node heat balance constraint, which ensures that the total heat delivered into an intermediate node is equal to the total heat delivered from that node to subsequent demand nodes indicated as follows in EQ. 6.

Thus, at each time t including a planning horizon, the solution to EQ. 3 is subject to a heat balance constraint, which ensures that the total heat delivered into an intermediate node is equal to the total heat delivered from that node to subsequent demand nodes.

In one embodiment, linear programming is employed to minimize the objective function set forth EQ. 3 subject to the constraints set forth in EQs. 4, 5, and 6 to determine the quantities of heat from the modular metal melting furnaces110,120,130delivered to the intermediate nodes j, i.e., one of the usage distribution centers indicated by nodes210,220,230, and240at time t and the quantity of heat delivered from the intermediate nodes j, i.e., the usage distribution centers indicated by nodes210,220,230, and240to the demand nodes k at time t.

Execution of the local control optimization model set forth in EQ. 1 and the system control optimization model set forth in EQ. 3 to control operation of the heat energy recovery system200minimizes operational heat energy consumption while satisfying production requirements under different operating schedules. The operating schedules may include full production, partial production, and non-production. A control system employing the local control optimization model set forth in EQ. 1 and the system control optimization model set forth in EQ. 3 is able to control the diversion of heat in the form of high temperature exhaust gases from a gas turbine to various process and facility loads, thus providing operational flexibility for multiple recovery options. This facilitates use of small, modular cogeneration applications that are physically proximal to process and facility heat loads. Furthermore, natural gas-driven turbine generators produce less CO2than other known electric generating units, thus allowing emissions reduction. When the waste heat is fully utilized, this will serve to reduce energy usage.