Engineers often worry about laws.
Not necessarily the legal system kind, although
most engineers have to consider those as well!
What I mean is laws, like the laws of physics.
Those laws describe limits on the sorts of
things that are possible;
for example, there’s no way of getting around the
conservation of momentum that we know of!
But outside of physics, there’s one law
that keeps many an engineer up at night:
Murphy’s Law.
Murphy’s so-called ‘law’ is more of a
tongue-in-cheek proverb, named after
aerospace engineer Edward A. Murphy.
Simply put, it says:
“Anything that can go wrong will go wrong.”
That makes it sound like all attempts at engineering
are doomed.
But we’re going to find a way to use Murphy’s
law to make us better engineers.
[Theme Music]
Murphy’s “law” isn’t a literal statement.
Your calculator isn’t going to spontaneously
combust every time you use it.
But, keeping in mind all the different ways
mishaps can jeopardize an engineering process,
as Murphy’s Law tells us they can, is a
good reminder to stay vigilant.
Even if many parts of a process are in flux
or changing, you need a system to keep things
stable and reliable.
That same system should also prevent mistakes
from spiraling into a major disaster!
To do this, engineers use what’s called process
control: an automated system that takes a look
at what’s going on in your process,
and makes adjustments based on those
observations to keep everything on track.
The aspects of the system your process control
looks at are called parameters,
while the outputs that get changed as a result of
those observations are called controlled variables.
To see all this in action and understand why
process control matters, consider cheese.
A few years ago, a cheese company based in
Toronto ran into trouble when some of the vats they
used for storing milk started having problems.
The vats needed a certain amount of milk in
them for the cheese making process.
Like many industrial processes, precision
is what allows high quality products to be
manufactured with consistency.
And no one wants bad cheese!
Too little milk in the tank, and it would affect
production further down the line, perhaps leading
to a loss of the entire production run.
Too much milk in the vat, and some of it would
start to spill over.
At first, that might not seem like a big deal.
Even if it did spill over, some of it would be
directed into the drains leading to the sewer,
removing it from the factory floor.
The rest could be cleaned up and, as in
the case of too little milk, you might need
to write off this production run.
But that’s not the worst of it!
Cheese is made using bacteria.
If the milk containing that bacteria
overflows from the vat and into the drains,
it could end up in the sewer systems.
And at the end of the sewer line would be
a waste treatment plant that uses its own 
bacteria to treat wastewater!
If the bacteria in the cheese began to kill
off the plant’s bacteria, it would jeopardize
the whole system,
perhaps even threatening the water supply
of an entire urban area, like the city of Toronto.
This sounds like exactly the sort of catastrophe
Murphy’s Law tries to warn us about!
Thankfully, process control is exactly the
sort of thing that prevents both major and
minor problems like these.
What was happening with the cheese factory was
that the inputs to the system of milk vats needed to be
controlled to maintain the level of the contents inside.
In the language of process control, one of
the parameters involved in making the product,
in this case, the level of milk in the tank,
is sensitive to the controlled variables of
the process, which are the amounts of all
the ingredients pumped into the tank.
The failure was that the system that automatically adjusted the flow rate of the ingredients in response to the measurements made in the tank wasn’t working as intended.
As we’ve mentioned, process control uses
measurements of a process’s parameters to
make changes to its controlled variables.
That might sound a little abstract, but various
types of process control are currently in use around
us, making the world run smoothly.
When you set the thermostat in a building, you’re
essentially programming a process control system
to keep it at a particular temperature – say, 25°C.
That’s the setpoint – the number that
represents the target output or operating
state you want a process to achieve.
For example, after you set it, the thermostat
then turns the furnace or the air conditioner
on or off,
to heat up or cool down the environment
and maintain it at 25 degrees.
The controlled variable here would be whether the
furnace was on or off, which has a direct effect on the
parameter you’re interested in, the temperature.
Other parameters you might want process control to
be sensitive to are things like the pressure in an
oxygen tank, or the force being applied to something.
You can even use it to control a mixture’s
chemical properties, like acidity.
Because of its broad uses, process control
is everywhere.
It’s a major consideration in chemical,
electrical, industrial, and mechanical engineering,
just to name a few.
The beauty of it is that along with avoiding big mistakes,
process control lets you create products to particular
standards with consistently high quality and precision.
Plus, monitoring and controlling things so
carefully can help you find new ways to use
materials and energy more efficiently.
In modern control systems, measurements are
taken as electronic readings from sensors,
which are delivered to a computerized
control system, called well, the controller.
The controller also sends signals to the machinery
responsible for changing the controlled variables.
This normally means the operation of things
like valves or switches.
There are two main kinds of process control
to consider: feedback, and feedforward – which,
yes, is a real word.
To see this in action, let’s revisit our
old friend the heat exchanger!
Heat exchangers transfer thermal energy
from one fluid to another to raise or cool
the temperature of one of the fluids.
In a shell-and-tube exchanger, you run one
fluid through a series of pipes to exchange heat
through the pipes, with the surrounding fluid.
Let’s say you’re using steam in the pipes to
raise the temperature of oil to 200°C before
it flows into an engine.
At the input end for the steam, there’s
an inlet valve that controls how much steam
is entering the exchanger.
The goal is to make sure the oil leaves the
heat exchanger at a given temperature – 200 °C
– that’s the setpoint.
The controller then adjusts the machinery to
maintain the setpoint condition for the process –
in this case, by controlling the steam inlet valve
to maintain the oil’s temperature at 200 degrees.
One of the ways you can do this is with feedback
control.
First, you put a sensor to measure temperature
at the output end of the exchanger for the fluid.
In feedback control, the sensor will continuously
feed temperature data back to the controller.
The difference between the observed temperature
of the output and the setpoint is called the error.
The controller tries to minimize the error by controlling
the inlet valve at the start of the process to increase or
decrease the amount of steam entering the exchanger,
depending on whether the error is negative or positive.
If the error is negative, that means the temperature
is too low, so the controller will open the valve a
little to let more steam through.
If the error is positive, it will do the opposite.
On the other hand, you could also use a feedforward
system.
In that case, you’d be measuring the input
variables of the process.
In the heat exchanger, the sensors would
measure the amount of steam and fluid flowing
in, and the fluid’s starting temperature.
Then you’d model what you think the output
temperature will be, based on those inputs.
In this case, you’d need to know things like the
specific heat of the fluids and the heat conductance
between the steam, the pipes, and the oil.
The modeling is often the hardest part, but once it’s
done, all the input parameters provide a decent estimate
of what the output temperature of the fluid will be.
What’s more, from the model, you can then
work out the best flow rate for the steam,
given all the other inputs.
In this case, the difference between the measured flow rate and the flow rate needed to obtain the setpoint temperature would be an example of what’s called the disturbance.
The disturbance is the difference between what
the input parameters should be and what they really
are – like the output error in a feedback system.
All the sensor data is fed forward to the
controller to compare the modeled output
temperature to the setpoint.
Depending on the disturbance, it operates the valve
similarly as before, until the inputs are set according to
the model to make the oil’s temperature hit the setpoint.
On their own, both of these approaches have
some flaws.
In a feedback control system, you have to
wait until something has already gone wrong
in order to fix it!
If a lot of errors are creeping into the process
very quickly, a feedback system might not respond
fast enough to change the inputs.
On the other hand, a feedforward system relies
on having to model everything going on inside
the heat exchanger.
You can’t be sure that your model is perfectly
predicting the output temperature,
and even if it’s pretty close, it usually takes a lot
of work to get the model right in the first place.
In real life, often the most sensible thing
to do is just combine the two approaches.
The feedforward controller can help you get
the inputs as close to what you need as possible,
while the feedback controller can correct
for the flaws in the model by measuring the
actual output temperature.
You could even implement something called
cascade control as an additional measure.
It helps you make sure that turning the valve
influences the steam flow rate in the way
you expect.
To do this, you put in a separate controller
and sensor that measures the steam flow as the
valve is opened or closed by a certain amount.
Otherwise, if the steam is at a higher pressure
than expected, opening the valve might increase the
flow rate of the steam by more than you predicted!
The cascade control system puts checks in
place to prevent that from happening.
With the feedback, feedforward, and cascade
systems working together, in what’s called an
integrated approach,
you can keep the oil’s temperature steady at the
setpoint, despite all the little ways Murphy’s law
could have made things go off the rails.
You can apply the same thinking to the milk
tanks in the cheese factory.
To keep everything perfectly balanced, the sensor
in the tank was meant to measure the milk level, while
other sensors measured the flow rate of ingredients.
The controller would then adjust the flow
rate as needed.
But because the sensor – in this case a gauge
in the tank – was misreading the level of milk, the
entire system was being thrown off.
After fixing it to take accurate readings,
the factory’s process control started working
properly again.
And the water in Toronto stayed nice and clean.
So while Murphy’s law might paint a pessimistic picture
of engineering, process control steps in as the linchpin
that keeps everything from spiraling out of control.
Crisis averted!
In this episode, we looked at process control
systems, where automated controllers change process
variables in response to measured parameters.
We looked at how feedforward and feedback
systems minimize errors and disturbances,
and saw how integrating them both with the final
check of cascade control creates a system made to
handle uncertainty the world throws its way.
Next time, we’re looking at how systems react to forces,
when we delve into the world of statics and
dynamics and how they affect all the structures
an engineer might create.
Crash Course Engineering is produced in association
with PBS Digital Studios.
Wanna keep learning?
Check out Global Weirding, which explores
the intersection among climate, politics, and religion,
hosted by climate scientist Katharine Hayhoe.
Crash Course is a Complexly production and this
episode was filmed in the Doctor Cheryl C. Kinney
Studio with the help of these wonderful people.
And our amazing graphics team is Thought Cafe.
