Handling spirits: Controlling Alcohol Intake with Predictive Control.

Conjure thoughts of just how much the person can take of alcohol, at what frequency or how fast, and for how long before he/she gets overwhelmed by a deluge of spirits—literally. Alcohol is a spirit, afterall.

One is said to be ‘under the influence’ for good reason, because that one acts possessed of spirits other than his. It shows.

If any drinker (or drunkard) would know their limits per time, then, if they do choose to drink so much, they can in some way drink optimally. That is, to drink as much as they need to not overwhelm their system either short term or long.

This is an optimization and control of drinking problem, to which many have assumed the solution of moderation as they’ve individually defined it. Some prefer to hang out with friends who won’t let them get drunk or even help them stop drinking. AA, anyone?

Now, let’s say her input is drink, and her objective is to drink as much as possible (regularly and every time), with the constraints that she ought never get drunk and for her liver to not get too full of drink to the point of growing it significantly, or breaking it. Like for several scenarios we face in life, we use model predictive control (MPC) to deal with this.

We would need a controller-actuator combo that will determine and implement the plan. The controller judges what to do based on its current perception of the state of its inner and outer world, its projection of the future using  a fixed (or adaptive) model of reality, and how it chooses to determine and rate what’s best to do now, having these inputs.

MPC involves:
finding the series of input per time (the plan)
that ‘best’ satisfy an objective (the goal)
over a horizon (the near future),
subject to known constraints (internal and external environment),
and using an uncertain model (chance and reality happening) of how the body (or world) actually responds.

It tests some series of inputs on a model to see the output it yields over several steps; then it applies the first input from the series of inputs tested that gave the best results. And it repeats this as a cycle so that the final objective is achieved.

Thus, while the objective may stay the same, the plan may be changed because of uncertainties, and chance showing up.

The game of chess is an exercise in predictive control.

You periodically evaluate your position/performance relative to your understanding of your objectives, making necessary changes as decided by the brain (controller) through your actuator. Hence one would need to own/create/evolve/develop/grow an effective model, thinking, and actuation to make any progress at all. And obviously also, we need a sensing system to monitor changes in the world and the operating environment.

How does this translate to the drinking scenario? If the goal is minimisation, the solution is trivial: don’t drink.

Drink responsibly; drink water!

MPC is the norm in automated process plants, like in breweries and distilleries. They use it to try to make optimally spirited drinks, optimally, to make as much money as they can—to drain your pocket as often as possible by your happy permission.

Incomplete thoughts

A rehash of Beer’s law

Beer’s law (1852 by August Beer):
It relates the absorption of light to the properties of the material through which the light is traveling. (http://en.wikipedia.org/wiki/Lambert-Beer_law). That is, how well a student absorbs academic material, per topic, per time, or how much alcohol the liver will take at any specific time.

Beer Lambert Law in Solution

Beer-Lambert Law in Solution

Specifically, it is the physical law that states that the quantity of light absorbed by a substance dissolved in a non-absorbing solvent is directly proportional to the concentration of the substance and the path length of the light through the solution. (http://en.wikipedia.org/wiki/August_Beer).

Beer's Law

A is the measured absorbance (of the brain or liver etc).
ε L.mol-1.cm-1 is molar absorpitivity. The wavelength-dependent absorptivity coefficient, a function of the level/rate of understanding and comprehension, focus, attention and distraction.
l cm is path length of the sample (material),  a function of volume, presentation and pedagogy.
c mol.L-1 is solution/analyte concentration, a function of frequency and/or material concentration.

Then a saturation (can’t take this any more) point might come, or the above law break down, like when a stretched rubber (stomach or liver?) refuses to go back to its original length having been overstretched, thus distended (re: Hookes law of elasticity).


Reference also made to the Android app, Techcalc, by http://www.roamingsquirrel.com/calculator.html

Pictures from Wikimedia commons (File:Beer Lambert Law in Solution.jpg, File:Beer’s Law.png).