This project shows how a simple chemical system comes to 
different equilibrium states depending on the concentrations
of the initial reactants.  It is very similar to its partner
model, "Chemical Equilibrium 2," which shows how an
identical system's dynamics depend on the kinetics of the
forward and backward equilibrium reactions.  Equilibrium is
the term we use to describe a system in which there are no 
macroscopic changes.  This means that the system "looks" 
like nothing is happening.  In fact, in all chemical systems
microscopic processes continue but in a balance that yields 
no changes at the macroscopic level.  This model simulates 
two simple reactions of four molecules.   

The reactions can be written A + B yields C + D.
At the same time, of course, C + D yields A + B.

A classic real-life example that would illustrate such
reactions is the reactions of carbon monoxide with nitrous
dioxide to produce carbon dioxide and nitrous monoxide.  The
reverse is also possible.  All the reactants are gases.  We
could watch such an equilibrium system because NO2 is a
reddish colored gas which is visible. 

Upon combining NO2 with carbon monoxide, the two gases
forming the colorless products NO and CO2, and the system 
loses reddish color.  However, not all the color is lost. 
Ultimately the system comes to equilibrium with some of the
"reactants" and some of the "products" present.

How much "reactant" and "product" a system ends up with
depends on a number of factors.  The inherent kinesthetics
of the reaction are of vital concern... some reactions tend
to go in a particular direction because energy is released
in that direction, for instance.  A system's equilibrium is
also affected by the concentrations of the system's
reactants and by the temperature of the system.

As stated above, this model simulates a chemical system of 
four different molecules.  They are represented on the 
graphics screen as squares of four different colors.  In 
this simulation, yellow molecules react with blue molecules 
to produce green molecules and brown molecules.  

The model is setup by first adjusting the YELLOWMOLS and
BLUEMOLS sliders and pushing the SETUP button.  YELLOWMOLS
sets how many yellow molecules the simulation starts with,
while BLUEMOLS sets how many blue molecules the simulation
starts with.

The GO button sets the simulation in motion.  Molecules move
randomly and react [changing color to represent
rearrangement of atoms into different molecular structures]
with each other.  The system soon comes into equilibrium.

Four monitors show how many of each kind of molecule are
present in the system.  There is also a plot window which
plots the number of each kind of molecule present versus

Notice that the number of product molecules is limited by
the smallest amount of reactant product.  Notice that there
are always the same number of reactant products since they
are formed in a one-to-one correspondence with each other. 

How do different amounts of the two reactants affect the
final equilibrium.  Are absolute amounts important, or is it
a ratio of the two reactants that matters?  

Try setting the YELLOWMOLS slider to 400 and the BLUEMOLS
slider to 20, 40, 100, 200, and 400 in five successive
simulations.  What sort of equilibrium state do you predict
in each case?  Are certain ratios predictable?

One variation of this model already exists.  Chemical
Equilibrium 2 shows a similar system in which forward and
reverse reaction rates are the variables controlled instead
of initial concentrations.  Look at this other model and 
consider how the two fit together.  You could put the two 
models together and see if concentration and reaction rates 
act independently of each other on the final equilibrium state.  

You could also extend the program by allowing the user to
introduce new molecules into the simulation while it is
running.  How would the addition of fifty blue molecules
affect a system that was already at equilibrium?

Notice that the primitive CLEARPLOT is not abbreviated in
this program.  CP is no longer the abbreviation for
CLEARPLOT as it is now used as a primitive to clear all

Notice also that the primitive STOP which is used at the end
of a series of consequents to IF statements checking for
neighboring molecules, stops the turtled from executing not
just that single consequent, but the entire procedure.  This
is vital to this model, or a single blue molecule surrounded
by a number of yellow molecules at once could single-
handedly change all of them to brown.  Instead, the
blue molecule changes only one to brown (the first one it
"notices") and then stops the entire looking procedure.