Autonomous cooperating agents

Industry expert Ernst Dummermuth discusses the working of autonomous cooperating systems to achieve overall process performance characteristics and provides a real-world example to the theories.

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Autonomous Cooperating AgentsBy Ernst Dummermuth

THE DESIGN and implementation of control schemes known as “autonomous cooperating systems” begins with the ability to precisely assess the performance of each involved device at the design stage and during process execution. It’s important to know what additional resources are available, such as power, operating range, task modification, or a flexible schedule, in order to adapt to varying process requests or perform a function that normally is done by another device.

This article will examine a process in which each device has excess capability over and above its normal duty, so it can help another device that encounters difficulties meeting requirements, or one that already has reached its boundaries. Negotiations between these agents strive for a global goal such as minimum operating cost. 

Rod Mill Devices Cooperate
There’s been considerable interest recently in applying autonomous cooperative systems to control applications in factories. For example, a rod mill can use “agents,” such as Furnace, Rolling Presses, Water Cooling, and Laying Head. Each unit autonomously controls its own behavior, while cooperating with other devices to meet a global goal. The Water Cooling agent might itself be composed of cooperating cooling functions that work together to achieve a certain cooling characteristic.

Key problems a rod mill cooling system can have include operating valve failures at water cooling boxes, and aging or worn out water spray nozzles. The degradation or loss of cooling unit(s) is compensated for by mutual assistance from the remaining units. The model achieves cooling unit cooperation to compensate for decaying units, and provides information for monitoring, scheduling, and restoration of deteriorated units.

Inside Steel Making
The principal function of a rod mill is to reduce 12x127x127 mm steel billets to rods with diameters between 5.5 and 12 mm (See Figure 1 below). The process first heats the billet to about 1,150 ºC, and then a multi-pass rolling process gradually reduces the bar to the required diameter. The reduced bar then is water cooled, followed by controlled air cooling, which gives the bar its required metallurgical properties. The cooling system must be adaptive to produce rods of different shapes, sizes, and grades of metallurgical and dimensional quality.

FIGURE 1: KEY ROD MILL FUNCTIONS
Key Rod Mill Functions
This rod mill uses “agents” such as Furnace, Rolling Presses, Water Cooling, and Laying Head. Each unit autonomously controls its own behavior, while cooperating with other devices to meet a global goal.

A system to control this process could be applied by using agents. Each agent stores a cost function related to its particular capabilities. During what we might call negotiations, an agent proposes to move its operating point by a small amount towards a lower cost. The other agents recalculate their cost function as a result of this change. If the overall total cost is reduced, then all agents might adjust to the new setting. If the total cost increases, then the first agent might try an adjustment in the opposite direction. Again, if this new proposal is successful, then all the other agents readjust. Each agent gets a turn to propose adjustments, leading to a convergence to the minimum cost. By range checking, the process verifies that the system didn’t converge to a local minimum, but actually found the true minimum.

In Figure 2 below, each horizontal axis—per agent—represents a collection of normalized variables. For each agent, there are generally many different variables that affect the cost. With Agent 1, for example, the use of heating gas contributes to the cost, so a higher furnace temperature translates into higher cost. However, a certain minimum temperature is needed, or the resulting off-spec steel might be high-cost scrap.

FIGURE 2: LET'S GET MINIMAL
Goal-Oriented Messages
Individual agents must negotiate with each other to minimize overall cost. Changes in a unit’s x-axis operating variables create a corresponding change to the unit’s y-axis cost of operation within required parameters.

Agent 2 requires more pressure and higher torque if the billets aren’t “maximum” hot, and that would mean a higher energy cost to produce more pressure and torque. So in this case, higher temperature results in lower energy cost. Also, the rolling drums suffer faster deterioration from excessive pressure as well as excessive heat. The cost to redress or replace the drums, as well as other machine parts such as bearings, also is part of the equation.

Agent 3 is tasked to cool the rod rapidly. To obtain uniform rod properties, slow cooling is desired. Yet, the very technique of making steel requires a rapid cooling to freeze some desired crystalline properties. The cost not only is a function of the amount of cooling water needed, but is much more complex.

Depending on the rod processed and the steel quality desired, it might be desirable to operate only units 1, 3, and 5. As the rod is cooled, the outside temperature drops rapidly. Proceeding through unit 2 where no cooling is applied, the rod is “back-blooming,” that is, its surface temperature rises again. For best results, the surface and center temperature should remain close as the rod cools. However, since cooling only can be applied from the outside, there always will be a heat gradient. A goal of the cooperating cooling units is to minimize this gradient, and ensure that the surface temperature doesn’t drop prematurely below a certain value during the total cooling process.

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