The mass flow of a gas, for example, can be calculated on the basis of the measurements of absolute pressure, differential pressure and temperature. Similarly, the flow through an adjustable-speed centrifugal pump can be calculated from the shaft power equation based on the measurements of the pump’s power consumption and speed. Another application is viscosity control in rubber blending, where the final hardness of the batch rubber product can be predicted on the basis of batch temperature, pressure, agitator speed and torque. Smart sensors can also detect their own failure on the basis of signal observation. For example, if a signal is changing faster than the sensor is capable of, that can be interpreted as failure.
Manufacturers and users have both developed neural networks. For example Emerson Process Management offers an Intelligent Sensor Toolkit for creating virtual sensors for process analysis. Process Perfector from Pavilion Technologies combines neural networks with model-predictive control technology to develop its nonlinear model that is applicable to a wide range of processes (*11).
Neural networks can be installed in existing multivariable control applications and can calculate inferential properties while providing tighter control of nonlinear processes. NeuCOP II controller from NeuralWare incorporates nonlinear models into a model-predictive control strategy with an embedded optimizer that can compute optimal control actions without violating the operating constraints. This software package combines ANN, statistics and multivariable modeling techniques to create dynamic, nonlinear models from process data. The results can be implemented either manually or automatically.
User reports on successful ANN applications include municipal wastewater applications (*12), feed-forward control of refinery cookers (*13), soft sensors in acid plants (*14) and in steady-state modeling to improve chemical utilization (*15).
The age of the single-loop PID controller is nearing its end. Process control engineers of the future will treat the variables of flows, pressures or temperatures only as constraints, and will control and maximize the efficiency of unit operations by multivariable unit operations’ controllers. One of the tools that will be used to do that will be artificial intelligence. The fusion of fuzzy logic and neural networks seems to be the most promising advanced tool for future control applications.
What we all must remember is that no matter how advanced or intelligent the control algorithm is, it is still only a tool and the key to control remains to be the same what it was in the age of manual control, namely: One must fully understand a process, before one can control it!
There are no lack of references from which to enlarge your knowledge about the technologies of AI. Here are a few more recent sources.
- AI Magazine (ISSN 0738-4602), published quarterly in March, June, September and December by the American Association for Artificial Intelligence (AAAI).
- Artificial Intelligence and Soft Computing (Behavioral and Cognitive Modeling of the Human Brain by Amit Komar, ISBN 0-8493-1385-6, 750 pp., CRC Press (1999).
- Artificial Intelligence Applications in Manufacturing, A. Fazel Famili, Dana S. Nau, and Steven H. Kim, eds., ISBN 0-262-56066-6, 469 pp. Soft cover price is $49.95.
- Computational Intelligence, The Experts Speak, David B. Fogel and Charles J. Robinson, eds, ISBN 0-471-27454-2, 282 pp., IEEE Press/Wiley-Interscience (2003).
- Constraint Processing by Rina Dechter, ISBN 1-55860-890-7, 480 pp, Morgan Kaufmann Publishing, (June 2003).
- Fuzzy Logic Control, Advances in Applications, Hen Verbruggen and Robert Babuška, eds. ISBN 981-02-3825-8, 340 pp., World Scientific Publishing Co. (1999).
- The International Dictionary of Artificial Intelligence by William Raynor, ISBN 1-88899-800-8, 380 pp., CRC Press (1999).
- Neural and Fuzzy Logic Control of Drives and Power System, by M.N. Cirstea, A. Dinu, J.G. Khor and M. McCormick, ISBN 0 7506 55585, 399 pp., Newness, an imprint of Elsevier Science (2002).
See the author's articles in the following issues of
CONTROL magazine: August 1997, “Coal Gasification,” November 1998, “Envelope Control”; October 1999 “Model-Based Control”; September 2002, “Fuel Cells”; July 2004, “ANN”; May 2005, “Smart Valves”; January, March, May 2006, “Global Warming.”
- AI’s scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine. Artificial Intelligence (AI) is an umbrella term for a variety of methods, including fuzzy logic, artificial neural networks, statistical process control and others.
- Neural networks are based on the way the biological nervous systems, such as the brain, function. The fundamental concept of neural networks is the structure, which is used in the information processing system. Their highly interconnected processing elements (neuron networks) use the human-like technique of learning by example. The neural network is configured for data classification or pattern recognition through training. Just as in biological systems, learning involves adjustments to the synaptic connections that exist between the neurons. Neural networks are being applied to an increasingly large number of real-world problems. Their primary advantage is that they can solve non-mathematical problems—problems that do not have mathematical solutions or for which an algorithmic solution is too complex to be defined. In general, neural networks are well suited to problems that people are good at solving, but for which computers generally are not. These problems include pattern recognition and forecasting, which requires the recognition of trends in data.
- Expert systems use human knowledge to solve problems that normally would require human intelligence. These represent expertise in the form of data and rules, which can be called upon when needed to solve problems. Computer programs use decision-making logic and the necessary boundary conditions. This program knowledge is often embedded as part of the programming code, so that as the knowledge changes, the program has to be changed and then rebuilt. Knowledge-based systems collect the small fragments of human know-how into a knowledge base, which is used to reason through a problem. The ability of these systems to explain the reasoning process through back-tracing and to handle levels of confidence and uncertainty provides an additional feature that conventional programming can not handle. Most expert systems are developed via specialized software tools called shells that are equipped with chaining mechanisms (backward, forward or both), and require knowledge to be entered according to a specified format. They come with tools for writing hypertext, for constructing user-friendly interfaces, for manipulating lists, strings and objects, and for interfacing with external programs and databases. These shells are like languages, but with a narrower range.
- Often the term “expert system” is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. More often than not, the two terms, expert systems and knowledge-based systems, are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain. “Task” refers to some goal-oriented, problem-solving activity. “Domain” refers to the area within which the task is being performed. Typical tasks are diagnosis, planning, scheduling, configuration and design. Building an expert system is known as knowledge engineering, and its practitioners are called knowledge engineers. The knowledge engineer must make sure that the computer has all the knowledge needed to solve a problem. The knowledge engineer must choose one or more forms in which to represent the required knowledge as symbol patterns in the memory of the computer; that is, he (or she) must choose a knowledge representation. He must also ensure that the computer can use the knowledge efficiently by selecting from a handful of reasoning methods. The practice of knowledge engineering is described later. We first describe the components of expert systems.
- In automatic speech recognition, a computer maps an acoustic speech signal to text. In automatic speech understanding, a computer maps an acoustic speech signal to some form of abstract meaning of the speech. Speech synthesis is the task of transforming written input to spoken output. The input can be provided in a graphemic/orthographic or a phonemic script, depending on its source.
- One might ask that if the contents of a human brain were downloaded into a machine, would that machine become self-aware? For obvious reasons, I do not presume to get involved with such questions.
- Fuzzy logic provides a means for finding crisp conclusions from vague and imprecise inputs similar to the way problems occur in everyday life. It offers a simpler method that can eliminate rigorous equations and the totally numeric logic flow of traditional computing. Fuzzy logic was introduced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s as a means to model the uncertainty of natural language. It is a superset of conventional (Boolean) logic that has been extended to handle the uncertainty in data. It is useful in process control, because it can handle relationships, which true/false type logic cannot. It lets a process control expert (or an operator) describe, in everyday language, how the process operates and how one can best control or operate a process. This is done without getting into the complex mathematical interrelationships or other theoretical aspects of the process.
- See section 2.31 in the second volume of the Instrument Engineers’ Handbook, prepared by Dr. János Abonyi.
- Supervised (ANNs trained to mimic a human operator or another controller), Adaptive (ANN controllers of minimum cost), Reinforcement type (ANN controllers trained by means of reinforcement training), Predictive (ANN controllers trained to match the output of an optimization routine on a plant simulator), Optimal (ANN controllers which include the capability to minimize non-trivial cost functions), Model Reference (ANN controllers trained to track a reference model), Inverse (ANN controllers using the inverse model of the plant as their reference), Output Matching (ANN control serving to minimize the error between the output of the actual plant and a reference signal from its model), Indirect Output Matching (Controller errors are calculated from plant output error signals by back propagating them through plant models), Direct Input Matching (Calculating the ANN controller error by using the controller as inverse model of the process).
- VanDoren, V.J., “Advanced Control Software Goes Beyond PID”, Control Engineering, January 1, 1998.
- Miderman, P.A., and McAvoy, T.J.,“Neural Net Modeling and Control of Municipal Waste Water Process,” Proceedings of American Control Conference, 1993.
- Hobson, G., “Neural Net Applications at PSP,” National Petroleum Refiners Association Meeting, 1990.
- Piosovo, M., and Owens, A., “Sensor Data Analysis Using Artificial Neural Networks,” Proceedings of Chemical Process Control IV, 1991.
- Samdani, G., “Neural Nets, They Learn From Examples”, Chemical Engineering, 1990.