Artificial intelligence in process automation

The Optimization of Unit Operations
Industrial unit operations (chemical reactors, distillation, drying, compressors, pumping, boilers, etc.) are well-understood processes. They can be described mathematically on the basis of their heat and material balances. In the past these processes were controlled by single PID loops, which maintained individual flows, pressures and temperatures, while the production rate, efficiency or energy consumption of the process remained uncontrolled. Later, it was realized that when a controller changes a valve opening, this affects not only that loop, but—because it changed the heat and/or material balance of the process—it upset the other loops also.

Plants do not sell levels and temperatures, but products, and what determines the profitability of the plant is the unit cost and quantity of the production. Artificial- intelligence-based control recognizes that the purpose of industrial plants is to make a profit. As a consequence, in order to optimize a unit operation, one can treat the individual process variables as constraints, and so long as all the process variables are within their allowable safe limits, one can modulate the process to increase production.

	FIGURE 4: CONSTRAINT CONTROL
	Multivariable envelope-based constraint control can lower overall excess oxygen by monitoring carbon dioxide and water and performing constraint-limit checks on excess oxygen, hydrocarbons, stack temperature and opacity.

Figure 4 illustrates such a constraint envelope for a combustion process where the main goal is to increase boiler efficiency by minimizing excess oxygen. Here the envelope optimizer keeps lowering excess O2 until one of the envelope limits is reached. When a constraint is reached, excess O2 control is temporarily transferred to the limiting constraint (CO, HC, opacity, etc.). Thereby, the boiler operation stays within a safe envelope, which is defined by these constraints. The lower part of Figure 4 illustrates the performance of a gas-burning boiler under conventional and under optimized envelope control. The energy savings are shown by the dark area. This saving results because envelope control makes the controls more sensitive.

Constraint envelopes are also used to stabilize the control of fast processes. For example, to control the guidance of rockets, missiles and other projectiles, engineers keep them inside a “tunnel,” which is defined by a safe envelope, and no correction is made so long as they are “inside the tunnel.”

Another example of a fast process is fluidized-bed coal gasification in which the residence time of the coal particles within the gasifier is only a few seconds. In controlling that process, I also found that envelope-based control increases stability.

Figure 5 below illustrates operation of the fuel cell, another fast process, which can also be controlled by constraint envelopes.

Herding-based optimization is another method of intelligent control. A herding envelope can, for example, be used to “herd the heat” from the interior of self-heating buildings. The interior offices of large buildings generate heat even in the winter, while the offices that have windows require heating. Therefore, the building can heat itself by transferring the heat from where it is in excess to where it is needed.

One can herd thousands of dampers by doing what a sheep dog does in directing a large herd. The Hungarian Puli, for example, goes after only one sheep at a time, the one that is furthest from the desired direction of the herd. I successfully used this algorithm, when I made the IBM headquarters in New York City self-heating.

FIGURE 5: OPERATION OF THE FUEL CELL
Gectricity is generated as hydrogen is oxidized into water. This is done by an anode, which, with the help of a catalyst removes an electron from the hydrogen to produce electricity, while the proton is combined with oxygen from the air, resulting in the exhausting of water.

Fuzzy Logic
The fuzzy controllerviii is a nonlinear controller with a linguistic nature that provides an extra set of tools to the process control engineer. It is used to control nonlinear processes that are not fully understood and cannot be controlled by conventional methods. FL can be described as controlling with sentences instead of equations by casting verbal knowledge into mathematical representation. For example, the term “hot” is not used as a temperature that exceeds some minimum value, but as a 0 to 1 membership function, where 0 means “absolutely no or total absence,” and 1 means “complete membership.” The values of 0 and 1 can correspond to two values of temperature and “membership” describes the actual state of the process.

FIGURE 6: CONTROLL RESPONSE
The flat surface at the top of the figure describes the error-output relationship of a linear PI controller. The middle surface can correspond to a nonlinear control surface of a fuzzy PI controller, while the bottom figure describes a fuzzy configuration that consists of 16 multivariable local models (*9).