Mathematical Modeling Problem for 500-gal Aquarium Impurities

In summary: Therefore, in order to reduce the impurity levels to 1% of their original values in a period of 3 hours, the filtering constant $a$ must be approximately 0.147190706298502.
  • #1
MarkFL
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Here is the question:

Mathematical modeling problem?

A 500-gal aquarium is cleansed by the recirculating filter system. Water containing impurities is pumped out at a rate of 15 gal/min, filtered, and returned to the aquarium at the same rate. Assume that passing through the filter reduces the concentration of impurities by a fractional amount a. In other words, if the impurity concentration upon entering the filter is c(t), the exit concentration is ac(t), where 0 < a < 1.
a. Apply the basic conservation principle (rate of change = rate in - rate out) to obtain a differential equation for the amount of impurities present in the aquarium at time t. Assume that filtering occurs instantaneously. If the outflow concentration at any time is c(t), assume that the inflow concentration at that same instant is ac(t).
b. What value of filtering constant a will reduce impurity levels to 1% of their original values in a period of 3 hr?

I have posted a link there to this thread so the OP can view my work.
 
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  • #2
Hello Kristine,

Let's let $A(t)$ represent the total amount of impurities in the aquarium at time $t$, measured in minutes. Let's also work this problem with parameters rather than numbers, so that we will derive a general formula into which we can plug the given data. Let the parameters be as follows:

$V$ is the volume in gallons of the aquarium.

$R$ is the rate, in gallons per minute, that water is filtered.

Now, if $c(t)$ is the concentration of impurities in the tank at time $t$, which is a measure of mass per gallons, then the rate at which impurities are leaving the tank and going into the filtration system is the product of the concentration and the rate of flow, which will have units of mass per minute:

\(\displaystyle \text{rate out}=Rc(t)\)

And likewise, the rate in of impurities coming in is given by:

\(\displaystyle \text{rate in}=Rac(t)\)

Now, we want to find an expression relating $A(t)$ and $c(t)$, and using the same principle that concentration is amount per volume, we may state:

\(\displaystyle c(t)=\frac{A(t)}{V}\)

And so, using the given basic conservation principle, we may model the amount of impurities in the tank at time $t$ with the following initial value problem (IVP):

\(\displaystyle \frac{dA}{dt}=\frac{R}{V}(a-1)A\) where \(\displaystyle A(0)=A_0\)

The ordinary differential equation (ODE) associated with the IVP is separable, and we may write it as:

\(\displaystyle \frac{1}{A}\,dA=\frac{R}{V}(a-1)\,dt\)

Let's exchange the dummy variables of integration so that we may use the given boundaries as our limits of integration:

\(\displaystyle \int_{A_0}^{A(t)}\frac{1}{u}\,du=\frac{R}{V}(a-1)\int_{0}^{t}\,dv\)

Applying the rules of integration and the anti-derivative form of the fundamental theorem of calculus (FTOC), we obtain:

\(\displaystyle \left[\ln(u) \right]_{A_0}^{A(t)}=\frac{R}{V}(a-1)\left[v \right]_{0}^{t}\)

Doing the evaluations indicated, there results:

\(\displaystyle \ln\left(\frac{A(t)}{A_0} \right)=\frac{R}{V}(a-1)t\)

Converting from logarithmic to exponential form, we obtain:

\(\displaystyle \frac{A(t)}{A_0}=e^{\frac{R}{V}(a-1)t}\)

Multiplying through by $A_0$, we obtain the solution satisfying the IVP:

\(\displaystyle A(t)=A_0e^{\frac{R}{V}(a-1)t}\)

In order to determine the value of $a$ that will result in the amount of impurities being 1% of the initial amount, we will let \(\displaystyle A(t)=\frac{A_0}{100}\), and $t=180$ (since 180 minutes is 3 hours), and the solve the resulting equation for $t$. This will be easier if we use the logarithmic form of the solution:

\(\displaystyle \ln\left(\frac{A(t)}{A_0} \right)=\frac{R}{V}(a-1)t\)

Solving for $a$, we find:

\(\displaystyle a=\frac{V}{Rt}\ln\left(\frac{A(t)}{A_0} \right)+1\)

We will also plug in the given data for the parameters:

\(\displaystyle R=15,\,V=500\)

And we obtain:

\(\displaystyle a=\frac{500}{15\cdot180}\ln\left(\frac{\dfrac{A_0}{100}}{A_0} \right)+1\)

Simplification yields:

\(\displaystyle a=1-\frac{10}{27}\ln\left(10 \right)\approx0.147190706298502\)
 

FAQ: Mathematical Modeling Problem for 500-gal Aquarium Impurities

What is mathematical modeling in relation to an aquarium?

Mathematical modeling is the process of using mathematical equations and principles to represent real-world situations and make predictions. In the context of an aquarium, it involves using mathematical equations to understand and predict the behavior of impurities in a 500-gallon tank.

What are the impurities that need to be modeled in a 500-gallon aquarium?

The impurities that need to be modeled in a 500-gallon aquarium can vary, but common examples include ammonia, nitrite, and nitrate. These impurities can come from sources such as fish waste, decaying plants, and uneaten food.

What factors should be considered when creating a mathematical model for impurities in an aquarium?

When creating a mathematical model for impurities in an aquarium, factors such as the type and number of fish in the tank, feeding frequency and amount, water flow and filtration, and water temperature should be taken into account. These factors can greatly impact the levels of impurities in the tank and need to be considered in the model.

How can mathematical modeling help improve the quality of the aquarium's water?

Mathematical modeling can help improve the quality of an aquarium's water by allowing for the prediction of potential issues and the implementation of preventative measures. By understanding the behavior of impurities in the tank, adjustments can be made to the tank's environment and maintenance schedule to maintain optimal water quality for the fish and other organisms living in the tank.

What are some limitations of using mathematical modeling for an aquarium?

Some limitations of using mathematical modeling for an aquarium include the complexity of the system and the accuracy of the input data. It can be challenging to accurately model all the factors that can affect impurity levels in a tank, and the model's predictions may not always align with real-world observations. Additionally, the model is only as good as the data used to create it, so it's essential to collect accurate and consistent data to get reliable results.

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