Artificial intelligence in process automation

WHAT IS intelligence? It is the totality of man’s mental processes. Intelligence makes us capable of adapting to our environment. It is what a child uses to process the information received from the sensors of hearing, vision, touch and the observation of the relationships between the causes and consequences of outside events. Figure 1 below illustrates a neuron of the human brain, which allows us to relate a number of inputs (x) to an output (y). Human intelligence includes the capability of perceiving relationships and analogies, reasoning, storing and retrieving information, classifying, generalizing, predicting on the basis of the past and adjusting to new conditions.

FIGURE 1: BIOLOGICAL NEURON MODEL

A biological neuron model, which processes N inputs (xN) to arrive at the output (y). (McCulloch-Pitts neuron).

Over the years, through the efforts of control professionals, our process controllers have gradually become more and more intelligent. First we improved PID control by adding external reset, decoupling, dead time and dynamic compensation, gain scheduling, feed forward and auto-tuning. Than came the linear quadratic Gaussian (LPG) regulator, Kalman filter, model predictive control (MPC), adaptive control, repetitive control and optimization.

In the vocabulary of control engineers, artificial intelligence is a relatively new term. It was coined during a technical conference at Dartmouth University in 1956. This article will concentrate on the latest stage, the family of algorithms consisting of fuzzy logic, rule-based artificial intelligence and neural networks.

Today, our control tool, the computer, can handle 10 billion instructions per second and has memory densities exceeding 300 GB per square inch. Compared to human intelligence, machine intelligence is more reliable, because computers do not get tired, mad, drunk, jealous, angry, fall in love, become senile, etc. In addition, intelligent machines have no egos or emotions; they just follow orders of their programmers—who do have egos and emotions.

Naturally, the knowledge of machines cannot exceed that of their programmers. Therefore, the process control knowledge of the programmer is key because one must fully understand a process before one can control it.

Certain machines already approach or border on having intelligence and as such, can often replace or outperform human operators. For example, the computer “Deep Blue” beat the world chess champion, Gary Kasparov. Artificial intelligence (AI)-based computer systems are also the primary controllers of spacecraft. Similarly, industrial unit operations can be fully automated and can optimize the production rate, efficiency and safety (*1) of the processes they control. Other examples of AI include global positioning, autopilots in airplanes and the directing of driverless vehicles and military robots. AI has also been used in the field of medicine; for example a semi-automatic robot assisted the doctor when he replaced my heart valve.

Some form of intelligence is incorporated in all of the following control strategies and control tools: artificial intelligence (*2), artificial neural networks (ANN) (*3), back propagation algorithms, business rules, case-based reasoning, common sense, cybernetics, data mining, data visualization, evolutionary software, expert systems (ES) (*4), face recognition, fuzzy logic (FL), genetic algorithms (GA), herding control, intelligent agents, intelligent controls, internal model control, knowledge-based systems (*5), language processing, machine learning, model-based controls, model-free controls, model-predictive control, neural networks forecasting, object-oriented networks, optimization, pattern matching and recognition, robotics, rule-based systems, speech recognition (*6), statistical process control, supervisory controls, text mining, unit operation control, vision—the list goes on.

Limitations of Intelligence
Human intelligence tends to focus on the short range and humans tend to assume that the future will be similar to the past. Our intelligence evolved to serve our individual survival and, therefore, its time scale is closely related to our life spans. Human intelligence is less concerned with the distant future, and it assumes that nature can affect humans, but not the other way around. This is because in the past, the scale of human influence was not large enough to have global consequences.

Yet, the facts are that during the last century, global population doubled, the carbon dioxide content of the atmosphere quadrupled, the consumption of resources increased five-fold, the consumption of water six-fold, consumption of oil seven-fold, not to mention the potential future consequences of global warming or of the proliferation of nuclear weapons, the number of which today exceeds 100,000.

	FIGURE 2: ARTIFICIAL NEURAL NETWORK
	An artificial neural network (ANN) process control model—trained on historical data—can predict the scale and timing of future events. (Click image to enlarge.)