Pumps and Systems, January 2007
Last August, after a heated and protracted debate, the International Astronomical Union stripped Pluto of the planetary status it had held since its discovery in 1930. Pluto was demoted for failing to make the required grade under the new rules for a planet: "a celestial body that is in orbit around the sun, has sufficient mass for its self-gravity to overcome rigid body forces so that it assumes a nearly round shape, and has cleared the neighborhood around its orbit."
The last requirement earned Pluto its embarrassing demotion, due to the fact that it is locked in a tight orbit with its companion, Charon. With this controversial decision, Pluto is now reclassified to the new category of "dwarf planets," similar to the old term "minor planets."
Why all the fuss? This was clearly a disagreement between the Aristotelians and Fuzzilians, i.e. those inclined to fuzzy thinking. In other words, black-and-white thinking scientists could never see eye-to-eye with their fuzzy thinking colleagues.
The same differences between these two schools of thought also apply to pump concepts. Think this through with me.
Aristotelians view the world as black and white. They don't like areas that lack clarity, so they write rigid definitions for terms like tall, short, thin, overweight, hot, and cold. For example, you might agree in general with the statement, "All basketball players are tall." But when you specifically ask, "What do you mean by tall?" an Aristotelian response would be, "Anyone over 6-ft, 5-in is tall." In reality, there are people that are 6-ft, 4.5-in in size who are considered tall and who also play a mean game of basketball. So exceptions still exist to this statement. However, an Aristotelian would say this is accurate enough to be considered a fact.
This is where the fuzziness begins.
Fuzzy logic is a relatively new branch of mathematics created to allow computer or control algorithms to make decisions using real-world inputs that may or may not be clearly defined. A washing machine that can control water usage and cycle time based on the size of the load, type of load, and degree of dirtiness, for instance, is a working model of a fuzzy control system.
Let's go further. Fuzzy logic deals with those shades of gray involved in more subjective arguments. In a simple example, suppose two people who work together want to determine whether an animal is a pet or not. An Aristotelian scheme might state that puppies and cats are pets and all other animals are not. In reality, when these two people rank a variety of animals in terms of their pet worthiness, they both agree that dogs and cats rate highest as desirable pets. However, these same two people totally disagree on other animals being pets: one ranks hamsters, raccoons, tropical fish, and even snakes as desirable; the other won't even consider any of these.
One person clearly lives the Aristotelian persuasion, while the other does not - yet they peacefully coexist, proving that it is possible for these two camps to live and work together.
What does all this have to do with pumps? As pump users, we often have to deal with fuzzy concepts, such as:
- What is low flow?
- When are vibration levels too high?
- When is suction specific speed value too high?
- When is a pump's energy level high?
There is never a clear line of demarcation of when these variables are good or bad.
Let's say you want to empty a tank as fast and reliably as possible. Commonly, users install a level switch and have the pump shut down whenever the fluid level reaches a "low level" that is determined by the pump engineer. This is a two state, or binary system: either the fluid level is high and the pump is running, or the fluid level is low and the pump shuts down.
Instead of a binary system, let's install a hypothetical fuzzy logic level transmitter (see Figure 1) that senses level and transmits one of four level states (see Figure 2):
1) High-high for levels greater than 12-ft
2) High for levels greater than 8-ft but less than 12-ft
3) Low for levels greater than 4-ft but less than 8-ft and
4) Low-low for levels less than 4-ft
Figure 1. Fuzzy logic control scheme for pumping out contents of a tank. Controller inputs are in blue and outputs are in red.
Fuzzy Logic Tank Level States:
Figure 2. The hypothetical fuzzy controller described in this column will convert the transmitter input into control values between 0 and 1, as seen in the graph above. For example, the controller output corresponding to the tank level value of L4 will result in a fuzzy controller output indication of 100 percent High. However, you would get fuzzy controller outputs of 50 percent High and 50 percent High-High with a transmitter input halfway between L4 and L5. This "fuzzification" of the tank level results in a smooth transition between states.
Next, we send these four level states to a fuzzy logic controller with the following rules:
- If the fluid level is in the High-High range, set the control valve to allow maximum flow.
- If the fluid level is somewhere in the High range, the fuzzy controller will control flow somewhere between maximum and mid-range flow.
- If the fluid level is somewhere in Low range, the fuzzy controller will control flow somewhere between mid-range flow and minimum flow.
- If the fluid level is in Low-Low range, set the control valve to allow minimum flow, but when pump power falls to 50 percent of the level expected for minimum flow, shut the pump down.
By breaking down the tank level into smaller increments, you are able to draw down the tank level to the lowest safe level possible. Notice also that pump flow is broken down into three realms (maximum flow, (maximum flow + minimum flow)/2, and minimum flow) instead of only two flow states (on and off). Gradually reducing pump flow at the lower tank levels allows you to reduce the likelihood of vortexing or cavitation at these levels.
This is not a real pump control scheme, but it could be. I'm not recommending that anyone go out and install a control system like this. It's only used to illustrate what can be done with a fuzzy control scheme and how fuzzy transmitters can add gray to normally black and white control logic.
Welcome to the world of fuzzy logic!
The point of this column is not to teach you how to design fuzzy logic systems. The purpose is to explain the fundamental differences between Aristotelian and Fuzzilian thinking, and the merits of each.
As pump users, we are taught black-and-white rules of thumb for selecting, designing, and installing pumps. We're hampered by economic and organizational pressures to work faster, better, and cheaper. Our rules of thumb are often challenged, leading us to consider pump selections or pump operations that lie in the gray regions of application. This is not always a bad thing, as long as you understand the trade-offs being made.
Those who know me know I am a card-carrying Fuzzilian. I see gray everywhere - except when it comes to safety and environmental consideration. When I think about the two logic camps, I don't think one is good and one is bad. Instead, I see a spectrum of logical thinking as healthy, even essential, for industrial progress. But progress only happens when both camps peacefully coexist - which brings me back full circle to Pluto.
Even though the dwarf planet is a victim of a rigid set of definitions, the various parties were able to compromise by allowing Pluto to maintain some of its stature and respect. The International Astronomical Union exhibited fuzzy thinking by classifying Pluto somewhere between a planet and an overgrown Kuiper belt object. Hang in there Pluto, we still love you!
