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2.3: Modeling with First Order Differential Equations

  • Page ID
    382
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    Whenever there is a process to be investigated, a mathematical model becomes a possibility. Since most processes involve something changing, derivatives come into play resulting in a differential equation. We will investigate examples of how differential equations can model such processes.

    Example \(\PageIndex{1}\): Pollution

    A pond initially contains 500,000 gallons of unpolluted water has an outlet that releases 10,000 gallons of water per day. A stream flows into the pond at 12,000 gallons per day containing water with a concentration of 2 grams per gallon of a pollutant. Find a differential equation that models this process and determine what the concentration of pollutant will be after 10 days.

    Solution

    We let \(x(t)\) be amount of pollutant in grams in the pond after \(t\) days.

    We use a fundament property of rates:

    \[Total Rate = Rate In - Rate Out.\]

    To find the rate in we use

    \[\begin{align} \dfrac{\text{grams}}{\text{day}} &= \dfrac{\text{gallons}}{\text{day}} \dfrac{\text{grams}}{\text{gallon}} \\ &= \dfrac{12,000}{1} \dfrac{2}{1} \\ &= 24,000 \text{ grams per day} \end{align}\]

    To find the rate out we first notice that since there was initially 500,000 gallons of water in the lake and the water level is increasing at a rate of 2,000 gallons per day, the total number of gallons of water in the lake after \(t\) days is

    \[gallons = 500,000 + 2,000 t.\]

    The units for the rate out is grams per day. We write

    \[\begin{align} \dfrac{grams}{day} = \dfrac{gallons}{day} \dfrac{grams}{gallon} &= \dfrac{10,000}{1} \dfrac{x}{500,000 + 2,000 \, t} \\ &= \dfrac{10x}{500 + 2t} \text{grams per day}. \end{align} \]

    Putting this all together, we get

    \[ \dfrac{dx}{dt} = 24,000 - \dfrac{10x}{500 +2t}.\]

    This is a first order linear differential equation with

    \[ p(t) = \dfrac{10}{500 + 2t} \;\;\; \text{and} \;\;\; g(t) = 24,000. \]

    We have

    \[\begin{align} \large \mu &= e^{\int \frac{10}{500 + 2t}dt} \\ &= e^{5\, \ln (500 + 2t)} \\ & = (500 + 2t)^5. \end{align} \]

    Multiplying by the integrating factor and using the reverse product rule gives

    \[ ((500 + 2t)^5x)' = 24,000(500 + 2t)^5. \]

    Now integrate both sides to get

    \[ (500 + 2t)^5x = 2,000(500 + 2t)^6 + C \]

    \[ \implies x = 2000(500+2t) + \dfrac{C}{(500+2t)^5}. \]

    Now use the initial condition to get

    \[ x = 2000(500)+\dfrac{C}{(500)^5} \]

    \[\implies C = -3.125 \times 10^{19}. \]

    Now plug in 10 for \(t\) and calculate \(x\)

    \[ \begin{align} x &= 2000(500+2(10)) + \dfrac{-3.125\,\text{x}\,10^{19}}{(500+2(10))^5} \\ &=\,218,072\,\text{grams}. \end{align}\]

    A graph is given below

    Modeli2.gif

    Example \(\PageIndex{2}\): Getting Lucky

    You just won the lottery. You put your $5,000,000 in winnings into a fund that has a rate of return of 4%. Each year you use $300,000. How much money will you have twenty years from now?

    Solution

    This is also a

    \[Total Rate = Rate In - Rate Out \]

    problem. Let

    \[x = \text{the balance after $t$ years.}\]

    The rate out is

    \[300,000\]

    and the rate in is

    \[0.04x.\]

    We have the differential equation

    \[ \dfrac{dx}{dt} = 0.04x - 300,000.\]

    This is both linear first order and separable. We separate and integrate to obtain

    \[\begin{align} \int \dfrac{dx}{0.04x - 300,000} &= \int dt \\ \implies 25\, \ln\, (0.04\, x - 300,000) &= t + C_1 \\ \implies 0.04x - 300,000 &= C_2 e^{\frac{t}{25}} \\ \implies x &= Ce^{\frac{t}{25}} + 7,500,000. \end{align}\]

    Now use the initial condition that when \(t = 0\), \(x = 5,000,000\)

    \[ 5,000,000 = C + 7,500,000. \]

    So that

    \[ x = -2,500,000\, e^{t/25} + 7,500,000. \]

    Plugging in 20 for \(t\) gives

    \[ x = 1,936,148. \]

    You will have about $2 million left.

    Contributors and Attributions


    This page titled 2.3: Modeling with First Order Differential Equations is shared under a not declared license and was authored, remixed, and/or curated by Larry Green.

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