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<title>Wastewater treatment simulations example (Vivek's solution)</title>
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<h1 class="title">Wastewater treatment simulations example (Vivek's solution)</h1>
<h5>BEE 4750/5750</h5>
<h5>Fall 2022</h5>
</div>
<pre class='hljl'>
<span class='hljl-cs'># activate and instantiate the environment</span><span class='hljl-t'>
</span><span class='hljl-k'>import</span><span class='hljl-t'> </span><span class='hljl-n'>Pkg</span><span class='hljl-t'>
</span><span class='hljl-n'>Pkg</span><span class='hljl-oB'>.</span><span class='hljl-nf'>activate</span><span class='hljl-p'>(</span><span class='hljl-nf'>dirname</span><span class='hljl-p'>(</span><span class='hljl-nd'>@__FILE__</span><span class='hljl-p'>))</span><span class='hljl-t'>
</span><span class='hljl-n'>Pkg</span><span class='hljl-oB'>.</span><span class='hljl-nf'>instantiate</span><span class='hljl-p'>()</span><span class='hljl-t'>
</span><span class='hljl-k'>using</span><span class='hljl-t'> </span><span class='hljl-n'>Plots</span><span class='hljl-t'> </span><span class='hljl-cs'># plotting library</span><span class='hljl-t'>
</span><span class='hljl-k'>using</span><span class='hljl-t'> </span><span class='hljl-n'>Random</span><span class='hljl-t'> </span><span class='hljl-cs'># tools for random number generation</span><span class='hljl-t'>
</span><span class='hljl-k'>using</span><span class='hljl-t'> </span><span class='hljl-n'>Distributions</span><span class='hljl-t'> </span><span class='hljl-cs'># probability distribution library</span><span class='hljl-t'>
</span><span class='hljl-k'>using</span><span class='hljl-t'> </span><span class='hljl-n'>StatsPlots</span><span class='hljl-t'> </span><span class='hljl-cs'># additional statistical plotting functionality</span>
</pre>
<p>CRUD is released and carried downriver according to this diagram:</p>
<p><img src="figures/river_diagram.png" alt="CRUD Simulation Diagram" /></p>
<p>The statuatory regulation is that CRUD concentrations cannot exceed <span class="math">$1$</span> mg/L. Environmental authorities have recently found that the concentrations in the river are in excess of this value. We would like to devise a treatment plan to bring the system back into compliance.</p>
<p>Recall that the first-order decay rate of CRUD in the river is <span class="math">$k=0.45 \ \text{d}^{-1}$</span>. For treatment efficiencies <span class="math">$E_i$</span> at each waste source <span class="math">$i$</span>, the mass of CRUD at every distance <span class="math">$x$</span> downriver from the initial inflow (<span class="math">$x=0$</span>) is:</p>
<p class="math">\[
M(x) = \begin{cases} (1100 - 1000 E_1) \exp(-0.45x/25) \ \text{kg/d} & x < 10 \\
\left[(1100 - 1000 E_1) \exp(-0.18) + 1200 (1 - E_2)\right]\exp(-0.45(x-10)/25) \ \text{kg/d} & x > 10
\end{cases}
\]</p>
<p>The cost of treating CRUD is quadratic in <span class="math">$E_1$</span> and <span class="math">$E_2$</span>, namely:</p>
<p class="math">\[
C(E_1, E_2) = 5000E_1^2 + 3000E_2^2.
\]</p>
<h1>Plot untreated CRUD concentrations</h1>
<p><em>Plot the CRUD concentrations from <span class="math">$x = 0 \text{ to } 15$</span>. I recommend writing a function to calculate these concentrations, as you may want to reuse this later for different values of <span class="math">$E_1$</span> and <span class="math">$E_2$</span> (and rerunning functions is typically preferable to copying and pasting sections of code with slightly different values).</em></p>
<p><em>If you want to compute exponentials, use the <a href="https://docs.julialang.org/en/v1/base/math/#Base.exp-Tuple{Float64}"><code>exp</code> function</a>.</em></p>
<p><strong>Response</strong>:</p>
<p>First, we'll write a function to calculate the CRUD concentrations and costs. We could do this with two different functions, but this will make it easier to evaluate over multiple concentrations later, at the expense of making us do a little more work to separate the outputs. Either approach works perfectly well.</p>
<p>One feature of the below function is that I set an optional value for <code>max_dist</code>. This is because the length of the river segment I'm considering is the same across all of the simulations. Using optional values means that I don't have to explicitly set this in future calls, which makes it a little easier to use broadcasting to evaluate over different strategies.</p>
<p>In the code below, I also take advantage of our ability to name variables with subscripts in Julia (see <a href="https://viveks.me/environmental-systems-analysis/tutorials/julia-basics/#variables">the Julia Basics tutorial on the website</a> for more details, including the use of Greek letters). This keeps things a little cleaner and more in sync with the mathematical notation, but is otherwise not essential.</p>
<p>We'll also set a random seed for reproducibility in random number generation.</p>
<pre class='hljl'>
<span class='hljl-n'>Random</span><span class='hljl-oB'>.</span><span class='hljl-nf'>seed</span><span class='hljl-p'>(</span><span class='hljl-ni'>1</span><span class='hljl-p'>)</span>
</pre>
<pre class="julia-error">
ERROR: UndefVarError: seed not defined
</pre>
<pre class='hljl'>
<span class='hljl-cs'># Function to evaluate CRUD concentrations and cost of treatment</span><span class='hljl-t'>
</span><span class='hljl-cs'># E₁ and E₂ are treatment efficiencies at waste sources 1 and 2</span><span class='hljl-t'>
</span><span class='hljl-cs'># max_dist is the length that we model downriver from waste source 1 (x=0)</span><span class='hljl-t'>
</span><span class='hljl-cs'># we set max_dist to be 15 by default so we don't have to keep repeating it (this will come in handy later)</span><span class='hljl-t'>
</span><span class='hljl-k'>function</span><span class='hljl-t'> </span><span class='hljl-nf'>crud_treatment</span><span class='hljl-p'>(</span><span class='hljl-n'>E₁</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>max_dist</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>15</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-cs'># initialize concentration storage</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>zeros</span><span class='hljl-p'>(</span><span class='hljl-n'>max_dist</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-cs'># we're using a spatial step of 1 km with this loop</span><span class='hljl-t'>
</span><span class='hljl-cs'># you could do something else, but make sure your indices are integers</span><span class='hljl-t'>
</span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>0</span><span class='hljl-oB'>:</span><span class='hljl-p'>(</span><span class='hljl-n'>max_dist</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-k'>if</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'><</span><span class='hljl-t'> </span><span class='hljl-ni'>10</span><span class='hljl-t'>
</span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>1100</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>1000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.45</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>25</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-p'>[</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>]</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>600000</span><span class='hljl-t'> </span><span class='hljl-cs'># convert mass to concentration</span><span class='hljl-t'>
</span><span class='hljl-k'>else</span><span class='hljl-t'>
</span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>((</span><span class='hljl-ni'>1100</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>1000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.18</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1200</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>1</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-p'>))</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.45</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>10</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>25</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-p'>[</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>]</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>660000</span><span class='hljl-t'> </span><span class='hljl-cs'># convert mass to concentration</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>.*</span><span class='hljl-t'> </span><span class='hljl-nfB'>1e3</span><span class='hljl-t'> </span><span class='hljl-cs'># convert kg/m³ to mg/L</span><span class='hljl-t'>
</span><span class='hljl-n'>cost</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>5000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-oB'>^</span><span class='hljl-ni'>2</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>3000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-oB'>^</span><span class='hljl-ni'>2</span><span class='hljl-t'> </span><span class='hljl-cs'># compute cost of this treatment</span><span class='hljl-t'>
</span><span class='hljl-k'>return</span><span class='hljl-t'> </span><span class='hljl-n'>conc</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>cost</span><span class='hljl-t'> </span><span class='hljl-cs'># return both, this will be returned as a tuple</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span>
</pre>
<pre class="output">
crud_treatment (generic function with 2 methods)
</pre>
<p>Next, we create our plot. Make sure to add labels, and I've also turned off the legend and grid as they don't add much to this particular plot.</p>
<pre class='hljl'>
<span class='hljl-cs'># plot untreated CRUD concentration</span><span class='hljl-t'>
</span><span class='hljl-cs'># note that we don't have to specify max_dist = 15</span><span class='hljl-t'>
</span><span class='hljl-n'>crud_untreated</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>crud_treatment</span><span class='hljl-p'>(</span><span class='hljl-ni'>0</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>0</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-nf'>plot</span><span class='hljl-p'>(</span><span class='hljl-n'>crud_untreated</span><span class='hljl-p'>[</span><span class='hljl-ni'>1</span><span class='hljl-p'>],</span><span class='hljl-t'> </span><span class='hljl-n'>xlabel</span><span class='hljl-oB'>=</span><span class='hljl-s'>"Distance Downstream (km)"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>ylabel</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-s'>"CRUD Concentration (mg/L)"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>grid</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>legend</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>)</span>
</pre>
<img 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" />
<p><em>In the absence of any treatment, what is the maximum concentration? How far downstream does it occur? Does this make sense physically?</em></p>
<p><em>You can use the <a href="https://docs.julialang.org/en/v1/base/collections/#Base.maximum"><code>maximum</code> function</a> to find the maximum over a data structure, or use the <a href="https://docs.julialang.org/en/v1/base/collections/#Base.findmax"><code>findmax</code> function</a> to find the maximum value along with the index at which it occurs.</em></p>
<p><strong>Response</strong>:</p>
<p>We will use <code>findmax()</code> to find the function and the distance downstream.</p>
<pre class='hljl'>
<span class='hljl-n'>crud_max</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>findmax</span><span class='hljl-p'>(</span><span class='hljl-n'>crud_untreated</span><span class='hljl-p'>[</span><span class='hljl-ni'>1</span><span class='hljl-p'>])</span>
</pre>
<pre class="output">
(3.2102988372006047, 11)
</pre>
<p>The maximum concentration is 3.21 mg/L, and it occurs 10 km downriver. This makes sense, as this is where the second waste stream is located, and there isn't enough distance between the two waste streams for most of the CRUD from waste stream 1 to decay, so the inflow at waste stream 2 is much higher than at waste stream 1.</p>
<h1>Experiment with different treatment plans</h1>
<p><em>Play with different values of <span class="math">$E_1$</span> and <span class="math">$E_2$</span>. Can you find combinations that bring the CRUD concentrations into compliance? What is the treatment cost?</em></p>
<p><strong>Response</strong>:</p>
<p>We could manually test different strategies by evaluating our function several times. Instead, I'm going to do something overkill and randomly sample treatment strategies, then filter out the ones that result in compliance with the effluent standard. Plotting those will give us some insight into what types of strategies will work.</p>
<p>Notice that I can directly sample each treatment in one command by sampling directly into an array by specifying a tuple with dimensions instead of a single value in the <code>rand()</code> call. This is also allowed because <span class="math">$E_1$</span> and <span class="math">$E_2$</span> are independent, not correlated.</p>
<pre class='hljl'>
<span class='hljl-cs'># evaluating different treatment plans</span><span class='hljl-t'>
</span><span class='hljl-cs'># generate some potential plans...this could also be done manually</span><span class='hljl-t'>
</span><span class='hljl-cs'># we'll sample 1000 strategies, each of which consists of two values, E₁ and E₂</span><span class='hljl-t'>
</span><span class='hljl-n'>n_treatment_samples</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>1000</span><span class='hljl-t'>
</span><span class='hljl-n'>treatment_samples</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>rand</span><span class='hljl-p'>(</span><span class='hljl-nf'>Uniform</span><span class='hljl-p'>(</span><span class='hljl-ni'>0</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>),</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-n'>n_treatment_samples</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>2</span><span class='hljl-p'>))</span><span class='hljl-t'>
</span><span class='hljl-cs'># now broadcast over the treatment samples and evaluate the cost and the maximum</span><span class='hljl-t'>
</span><span class='hljl-n'>treatment_output</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>crud_treatment</span><span class='hljl-oB'>.</span><span class='hljl-p'>(</span><span class='hljl-n'>treatment_samples</span><span class='hljl-p'>[</span><span class='hljl-oB'>:</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>],</span><span class='hljl-t'> </span><span class='hljl-n'>treatment_samples</span><span class='hljl-p'>[</span><span class='hljl-oB'>:</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>2</span><span class='hljl-p'>])</span>
</pre>
<pre class="output">
1000-element Vector{Tuple{Vector{Float64}, Float64}}:
([0.331305464341627, 0.3253953168837186, 0.3195906003550694, 0.31388943397
67441, 0.3082899705209617, 0.30279039571257776, 0.2973889276412438, 0.29208
3816184052, 0.28687334243848067, 0.2817558181654545, 1.9544270923091334, 1.
9195621306513706, 1.8853191239164493, 1.851686977070627, 1.8186547930041082
], 4073.0279481507246)
([1.545665523431573, 1.5180924461741871, 1.4910112441497776, 1.46441314281
198, 1.438289524143278, 1.412631923862691, 1.387432028683273, 1.36268167361
8535, 1.3383728393369172, 1.3144976495634566, 1.8775708366591402, 1.8440769
112589797, 1.811180482910226, 1.7788708928823134, 1.747137672585425], 1275.
7479467933786)
([1.205404163557306, 1.1839009976884378, 1.1627814260996985, 1.14203860586
51366, 1.1216658161295374, 1.1016564559308024, 1.0820040420611836, 1.062702
2069666663, 1.0437446966838255, 1.0251253688134876, 0.9362113630321212, 0.9
195103188212, 0.9031072039975401, 0.8869967038084424, 0.8711735983109097],
3641.1447033237737)
([1.7963718338957977, 1.7643264148784705, 1.732852653054058, 1.70194035064
83939, 1.671579491805075, 1.6417602393402362, 1.612472931555217, 1.58370807
91060876, 1.5554563619290163, 1.5277086262204893, 2.5021939855666497, 2.457
557428024872, 2.413717140588718, 2.3706589186215417, 2.328368810882746], 42
2.13475777227484)
([1.7386360235418539, 1.707620551777198, 1.677158364009744, 1.647239590224
1685, 1.6178545364760337, 1.5889936817508614, 1.560647674879243, 1.53280733
15069792, 1.5054636311192666, 1.478607714117975, 2.4682096998921366, 2.4241
79386922833, 2.3809345292818422, 2.3384611152569788, 2.2967453830905376], 4
23.73975605645296)
([0.36887816291386893, 0.362297757301919, 0.35583473933274984, 0.349487014
9320004, 0.3432525273814343, 0.33712925665254545, 0.3311152187520506, 0.325
20846507905854, 0.31940708179370647, 0.31370918919705904, 0.773251122684043
3, 0.759457120927575, 0.7459091899220899, 0.7326029400194237, 0.71953405987
82033], 5453.642040476786)
([0.8022112636767346, 0.7879006429021986, 0.7738453088285923, 0.7600407074
047182, 0.7464823658189511, 0.7331658910500078, 0.720086968443569, 0.707241
360314295, 0.6946249045727768, 0.6822335133769846, 1.0432885689816112, 1.02
46773779585938, 1.0063981913700089, 0.9884450865994948, 0.9708122466840496]
, 3652.1636875299264)
([1.8246784152391258, 1.792128036033168, 1.7601583219885903, 1.72875891463
83682, 1.697919640299835, 1.6676305067783206, 1.6378817001295916, 1.6086635
814800483, 1.5799666839036457, 1.5517817093545256, 2.3986908597303014, 2.35
5900691101133, 2.313873854905523, 2.272596734080889, 2.2320559544789886], 5
88.2700944378869)
([1.7844284669551345, 1.752596105274195, 1.7213316000632404, 1.69062482134
90778, 1.6604658198667779, 1.6308448238360265, 1.6017522357949832, 1.573178
629490617, 1.5451147468245208, 1.5175514948532058, 3.158077673666513, 3.101
740828236003, 3.0464089739681635, 2.9920641828581624, 2.9386888467182684],
4.511567837555354)
([1.4698333162334198, 1.4436130072664406, 1.4178604415426783, 1.3925672750
055529, 1.3677253124478395, 1.3433265048563494, 1.31936294680398, 1.2958268
73888287, 1.2727106602157492, 1.2500068159309148, 1.9157317479568738, 1.881
5570712948952, 1.8479920355840553, 1.815025765459153, 1.782647579560277], 1
179.3153384434593)
⋮
([0.777915837917787, 0.7640386224572061, 0.7504089621941835, 0.73702244099
95604, 0.723874721523363, 0.7109615437894636, 0.6982787238153109, 0.6858221
522562824, 0.6735877930742221, 0.6615716822297274, 1.3856308684566623, 1.36
09126442309245, 1.3366353676073095, 1.312791172535812, 1.2893723332886369],
2955.221911363183)
([0.5644933872469005, 0.5544234079778498, 0.5445330667431324, 0.5348191589
856663, 0.5252785373143602, 0.5159081104843302, 0.50670484239531, 0.4976657
511079278, 0.48878790787753135, 0.48006843620525025, 0.5181534311702286, 0.
5089101088781474, 0.49983167791333644, 0.49091519678474416, 0.4821577764744
827], 5609.797274040928)
([1.4303046916419448, 1.4047895325299735, 1.3797295375157734, 1.3551165869
417328, 1.330942705996549, 1.3072000621313211, 1.2838809625217331, 1.260977
8515755143, 1.238483308484359, 1.216390044819522, 2.1192930021170056, 2.081
487002828961, 2.0443554235388777, 2.0078862332902347, 1.9720676157463568],
851.5612720267936)
([1.1848804398162787, 1.1637433959911134, 1.1429834152067868, 1.1225937710
47914, 1.1025678570914172, 1.0828991847659855, 1.0635813812497226, 1.044608
1874052948, 1.0259734557519176, 1.0076711484735168, 2.2509107505928716, 2.2
10756826548692, 2.171319207056224, 2.132585113981748, 2.0945419971402583],
954.7948457150449)
([1.6419860485168238, 1.6126947125292106, 1.5839259037364626, 1.5556703007
928583, 1.5279187486358619, 1.500662255519801, 1.4738919901024639, 1.447599
2785836662, 1.421775601894865, 1.396412592938905, 2.772380264102526, 2.7229
238622807155, 2.6743497116106787, 2.6266420736426683, 2.579785490684631], 1
43.61166831456256)
([0.8681343830900857, 0.8526477619214952, 0.8374374060868103, 0.8224983872
976791, 0.8078258651813259, 0.7934150857122285, 0.779261379671772, 0.765360
1611353814, 0.7517069259866416, 0.7382972504579245, 1.9032591287114626, 1.8
6930695070061, 1.835960444494658, 1.8032088055338782, 1.771041422000732], 1
976.029959099784)
([0.489997695078215, 0.48125664205120744, 0.47267152038630317, 0.464239548
4289791, 0.45595799414655747, 0.4478241742430028, 0.43983545328951124, 0.43
198924287060747, 0.42428300074547654, 0.41671423002425495, 0.59062993469820
83, 0.5800937064049082, 0.5697454335471974, 0.5595817631941431, 0.549599402
2276378], 5570.302406701656)
([0.32776129433015094, 0.32191437120639393, 0.3161717511550451, 0.31053157
351697086, 0.30499201082527566, 0.2995512682131867, 0.2942075828325017, 0.2
889592232824102, 0.2838044890485047, 0.2787417099517994, 0.6877978653200808
, 0.6755282614566059, 0.6634775346594282, 0.6516417803876443, 0.64001716375
33298], 5806.5476454525)
([0.5946714330419733, 0.5840631085904947, 0.5736440256956385, 0.5634108084
833999, 0.5533601413018809, 0.5434887676469905, 0.5337934891073087, 0.52427
11643277737, 0.5149187079918546, 0.5057330898218824, 1.1594479115540535, 1.
1387645577776049, 1.1184501736798964, 1.0984981772227678, 1.078902103784825
2], 3880.424344435279)
</pre>
<p>In the code above, notice that I could just use the broadcast operator <code>.</code> to broadcast over the treatment samples, because I set <code>max_dist</code> to be optional. If I hadn't done this, I'd need to write a loop, since I don't have multiple distances to broadcast over.</p>
<p>Now, let's find those treatment strategies which result in compliance with the standard. This would not be necessary if we had done the previous step by trial and error, we'd just have to check the maximum values each time we proposed a new strategy. On the other hand, this allows us to more efficiently get a sense of the cost-concentration tradeoff.</p>
<p>One feature of the below code is the use of a <a href="https://viveks.me/environmental-systems-analysis/tutorials/julia-basics/#comprehensions">comprehension</a>, which may be familiar to you if you've used Python before. A comprehension lets us loop over a data structure and do something to those values without having to pre-allocate memory. In some cases, this can make things more compact and readable, but other times it can make things less readable. Use your judgement about when these are useful, and they're never necessary.</p>
<p>What does the comprehension line do? Each element in <code>treatment_output</code> is a tuple: the first part is the time series of concentrations, and the second part is the cost. We want to know the maximum concentrations, so we use <code>maximum()</code> on the first part of each element and collect those into a vector. Similarly, we want to collect the costs, and can use a concentration to do that. We could easily have pre-allocated <code>concentrations</code> and <code>costs</code> vectors and done a single loop through <code>treatment_output</code> to populate them; this would actually be faster for larger data structures!</p>
<pre class='hljl'>
<span class='hljl-cs'># find maximum concentrations</span><span class='hljl-t'>
</span><span class='hljl-n'>concentrations</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>[</span><span class='hljl-nf'>maximum</span><span class='hljl-p'>(</span><span class='hljl-n'>o</span><span class='hljl-p'>[</span><span class='hljl-ni'>1</span><span class='hljl-p'>])</span><span class='hljl-t'> </span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>o</span><span class='hljl-t'> </span><span class='hljl-kp'>in</span><span class='hljl-t'> </span><span class='hljl-n'>treatment_output</span><span class='hljl-p'>]</span><span class='hljl-t'>
</span><span class='hljl-cs'># collect costs</span><span class='hljl-t'>
</span><span class='hljl-n'>costs</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>[</span><span class='hljl-n'>o</span><span class='hljl-p'>[</span><span class='hljl-ni'>2</span><span class='hljl-p'>]</span><span class='hljl-t'> </span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>o</span><span class='hljl-t'> </span><span class='hljl-kp'>in</span><span class='hljl-t'> </span><span class='hljl-n'>treatment_output</span><span class='hljl-p'>]</span><span class='hljl-t'>
</span><span class='hljl-cs'># plot concentrations vs. costs</span><span class='hljl-t'>
</span><span class='hljl-nf'>scatter</span><span class='hljl-p'>(</span><span class='hljl-n'>concentrations</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>costs</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>xlabel</span><span class='hljl-oB'>=</span><span class='hljl-s'>"Maximum CRUD Concentration (mg/L)"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>ylabel</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-s'>"Treatment Cost (</span><span class='hljl-se'>\$\$</span><span class='hljl-s'>)"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>legend</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>grid</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-nf'>vline!</span><span class='hljl-p'>([</span><span class='hljl-ni'>1</span><span class='hljl-p'>],</span><span class='hljl-t'> </span><span class='hljl-n'>color</span><span class='hljl-oB'>=:</span><span class='hljl-n'>red</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-cs'># plot red line to mark regulatory threshold</span>
</pre>
<img 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" />
<p>This tradeoff between cost and maximum concentration makes complete sense! We can see how decreasing the maximum concentration increases the cost. However, this plot doesn't show us what treatment levels are needed to comply. Let's filter our sampled treatment plans to identify those which resulted in compliance (maximum concentration <span class="math">$\leq 1$</span> mg/L).</p>
<pre class='hljl'>
<span class='hljl-cs'># filter treatments which comply</span><span class='hljl-t'>
</span><span class='hljl-n'>treatment_comply</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>treatment_samples</span><span class='hljl-p'>[</span><span class='hljl-n'>concentrations</span><span class='hljl-t'> </span><span class='hljl-oB'>.<=</span><span class='hljl-t'> </span><span class='hljl-nfB'>1.0</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-oB'>:</span><span class='hljl-p'>]</span><span class='hljl-t'>
</span><span class='hljl-cs'># plot treatmnet levels for those</span><span class='hljl-t'>
</span><span class='hljl-nf'>scatter</span><span class='hljl-p'>(</span><span class='hljl-n'>treatment_comply</span><span class='hljl-p'>[</span><span class='hljl-oB'>:</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>],</span><span class='hljl-t'> </span><span class='hljl-n'>treatment_comply</span><span class='hljl-p'>[</span><span class='hljl-oB'>:</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>2</span><span class='hljl-p'>],</span><span class='hljl-t'> </span><span class='hljl-n'>xlabel</span><span class='hljl-oB'>=</span><span class='hljl-s'>"E₁"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>ylabel</span><span class='hljl-oB'>=</span><span class='hljl-s'>"E₂"</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>legend</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>grid</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-kc'>false</span><span class='hljl-p'>)</span>
</pre>
<img 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" />
<p>We can see that there is a general "triangular" shape to the region where plans are in compliance. Moreover, if either <span class="math">$E_1$</span> or <span class="math">$E_2$</span> is on the lower side of the allowable ranges, the other needs to be much closer to 1. </p>
<p>So we could choose a number of different strategies within that range. For a balanced one, let's use <span class="math">$E_1 = E_2 = 0.75$</span>, but you might also choose a strategy with a higher <span class="math">$E_2$</span>, since the cost of increasing <span class="math">$E_2$</span> is lower than increasing <span class="math">$E_1$</span> (more on this later in the semester...)</p>
<p><em>Based on your plan, where does the maximum value occur, and what is it? Does this plan make sense?</em></p>
<p>Now, let's evaluate how well this plan works.</p>
<pre class='hljl'>
<span class='hljl-n'>crud_plan_out</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>crud_treatment</span><span class='hljl-p'>(</span><span class='hljl-nfB'>0.75</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-nfB'>0.75</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>crud_plan_max</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>findmax</span><span class='hljl-p'>(</span><span class='hljl-n'>crud_plan_out</span><span class='hljl-p'>[</span><span class='hljl-ni'>1</span><span class='hljl-p'>])</span>
</pre>
<pre class="output">
(0.8974917787787049, 11)
</pre>
<p>The maximum concentration is 0.9 mg/L, and it occurs 10 km downstream. This is the same as before, which makes sense given the balanced treatment plan. We might have gotten a different result if we had opted to make <span class="math">$E_2$</span> much higher than <span class="math">$E_1$</span>, so waste stream 2 would not substantially increase the CRUD concentration. For example:</p>
<pre class='hljl'>
<span class='hljl-n'>crud_plan_2_out</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>crud_treatment</span><span class='hljl-p'>(</span><span class='hljl-nfB'>0.6</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-nfB'>0.95</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>crud_plan_2_max</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>findmax</span><span class='hljl-p'>(</span><span class='hljl-n'>crud_plan_2_out</span><span class='hljl-p'>[</span><span class='hljl-ni'>1</span><span class='hljl-p'>])</span>
</pre>
<pre class="output">
(0.8333333333333334, 1)
</pre>
<p>In this case, the maximum outflow is right after waste stream 1, as waste stream 2 is so heavily treated that it doesn't add much more CRUD. The cost of the first, balanced plan is 4500.0 dollars, while the cost of the second plan is 4508.0 dollars.</p>
<h1>Some other questions to consider</h1>
<p><em>How would you trade off cost versus CRUD concentrations? From your current understanding of the system, would you expect the managers of waste source 1 or waste source 2 to treat their waste stream more aggressively?</em></p>
<p><strong>Response</strong>: </p>
<p>We've actually addressed this somewhat above. Navigating the tradeoffs between costs and CRUD concentrations would reflect concerns about cost vs. the potential impacts of higher CRUD concentrations, even those within the regulatory level (which we don't actually know anything about, since CRUD is made up). The considerations about waste stream 1 and waste stream 2 would reflect questions of cost (since treatment at waste stream 2 is less expensive) and equity. One could argue that waste stream 1 outputs a lot more wastewater in the river, so they should treat at a higher level, but then again, waste stream 2 is more highly concentrated and is cheaper to treat.</p>
<h1>Uncertainty analysis</h1>
<p><em>Suppose the initial inflow is uncertain, and its fluctuations are distributed based on a normal distribution with mean 0.15 mg/L and standard deviation 0.05 mg/L. Note that the inflow cannot be less than 0 mg/L.</em></p>
<p><strong>Response</strong>:</p>
<p>This problem is actually a little more complex, since our equations above relied on the initial inflow being 0.2 mg/L. I've written a new function below which accounts for that change (review <a href="https://viveks.me/environmental-systems-analysis/assets/lecture-notes/02-intro-modeling/index.html#1">the notes from lecture 2</a> to make sure you understand how this works!). I've used the optional argument trick to fix the balanced strategy from above.</p>
<pre class='hljl'>
<span class='hljl-k'>function</span><span class='hljl-t'> </span><span class='hljl-nf'>crud_inflow</span><span class='hljl-p'>(</span><span class='hljl-n'>inflow</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nfB'>0.75</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nfB'>0.75</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>max_dist</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>15</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-cs'># initialize concentration storage</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>zeros</span><span class='hljl-p'>(</span><span class='hljl-n'>max_dist</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-cs'># we're using a spatial step of 1 km with this loop</span><span class='hljl-t'>
</span><span class='hljl-cs'># you could do something else, but make sure your indices are integers</span><span class='hljl-t'>
</span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>0</span><span class='hljl-oB'>:</span><span class='hljl-p'>(</span><span class='hljl-n'>max_dist</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-k'>if</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'><</span><span class='hljl-t'> </span><span class='hljl-ni'>10</span><span class='hljl-t'>
</span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>500</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>inflow</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>1</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-p'>))</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.45</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>25</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-p'>[</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>]</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>600000</span><span class='hljl-t'> </span><span class='hljl-cs'># convert mass to concentration</span><span class='hljl-t'>
</span><span class='hljl-k'>else</span><span class='hljl-t'>
</span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>((</span><span class='hljl-ni'>500</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>inflow</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>1</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-p'>))</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.18</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1200</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-ni'>1</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-p'>))</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-nf'>exp</span><span class='hljl-p'>(</span><span class='hljl-oB'>-</span><span class='hljl-nfB'>0.45</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-ni'>10</span><span class='hljl-p'>)</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>25</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-p'>[</span><span class='hljl-n'>x</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>]</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>mass</span><span class='hljl-t'> </span><span class='hljl-oB'>/</span><span class='hljl-t'> </span><span class='hljl-ni'>660000</span><span class='hljl-t'> </span><span class='hljl-cs'># convert mass to concentration</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span><span class='hljl-t'>
</span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-n'>conc</span><span class='hljl-t'> </span><span class='hljl-oB'>.*</span><span class='hljl-t'> </span><span class='hljl-nfB'>1e3</span><span class='hljl-t'> </span><span class='hljl-cs'># convert kg/m³ to mg/L</span><span class='hljl-t'>
</span><span class='hljl-n'>cost</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>5000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₁</span><span class='hljl-oB'>^</span><span class='hljl-ni'>2</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>3000</span><span class='hljl-t'> </span><span class='hljl-oB'>*</span><span class='hljl-t'> </span><span class='hljl-n'>E₂</span><span class='hljl-oB'>^</span><span class='hljl-ni'>2</span><span class='hljl-t'> </span><span class='hljl-cs'># compute cost of this treatment</span><span class='hljl-t'>
</span><span class='hljl-k'>return</span><span class='hljl-t'> </span><span class='hljl-n'>conc</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>cost</span><span class='hljl-t'> </span><span class='hljl-cs'># return both, this will be returned as a tuple</span><span class='hljl-t'>
</span><span class='hljl-k'>end</span>
</pre>
<pre class="output">
crud_inflow (generic function with 4 methods)
</pre>
<p><em>How frequently will your plan fail to keep concentrations in compliance? Is this acceptable?</em></p>
<p><strong>Response</strong>:</p>
<p>Now, let's sample a lot of random inflows for our Monte Carlo analysis. Since inflow can't be less than 0, we have two options: we could sample directly from <a href="https://juliastats.org/Distributions.jl/stable/truncate/">a truncated distribution</a>, which will ensure no samples are out of the allowable range, or we could draw samples from a normal and manually set negatives to 0. Let's go the truncated route.</p>
<pre class='hljl'>