Building a Pipeline Optimization Problem

In summary, the conversation revolved around finding the minimum cost for laying a pipeline from an oil rig to a shore plant. The discussion involved using a formula and optimizing it to find the minimum cost. The final answer was approximately 21.82 km.
  • #1
ardentmed
158
0
Hey guys, I'm having trouble with this problem set I'm working on at the moment. I'd appreciate some help with this question:

(I'm only asking about number two. Ignore number one please.)

08b1167bae0c33982682_24.jpg

So if the length for the hypotenuse of the leftmost triangle is represented by:
c^2 = x^2 + y^2

Then,

c= √(2500 + x^2)

Therefore, the total cost comes to:

C(x) = 400,000-20,000x + 50,000√(2500 + x^2)

Am I on the right track?

Moreover, we need to optimize and deduce the minimum cost, x's smallest possible value:

c'(x) = dy/dx (400,000-20,000x + 50,000√(2500 + x^2))

Then isolate and solve for "x."

x=21.821789 km

x ~ 21.km.

Thanks in advance.
 
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  • #2
I like to work problems like this in general terms, so that I have a formula to use for future problems. Let's let the distance of the oil rig from the shore be $O$, the distance down shore from the rig to the plant be $P$, the cost to per unit length to lay the pipe over land be $C$ and the cost per unit length to lay the pipe underwater be $kC$ where $0\le k,\,k\ne 1$. We will then let $x$ be the point on the shoreline we select to minimize the cost of the pipeline.

Hence, the total cost function $C_T$ is:

\(\displaystyle C_T(x)=C(P-x)+kC\sqrt{x^2+O^2}=C\left(P-x+k\sqrt{x^2+O^2}\right)\)

We need only optimize the factor containing $x$. THus differentiating, and equating the result to zero, we find:

\(\displaystyle \frac{d}{dx}\left(\frac{C_T}{C}\right)=\frac{kx}{\sqrt{x^2+O^2}}-1=0\)

This implies:

\(\displaystyle x=\frac{O}{\sqrt{k^2-1}}\)

Now, observing that:

\(\displaystyle \frac{d^2}{dx^2}\left(\frac{C_T}{C}\right)=\frac{kO^2}{\left(x^2+O^2\right)^{\frac{3}{2}}}>0\)

for all real $x$, we know our critical point is at a minimum. So, we can now plug in the given data (distances in km):

\(\displaystyle O=50,\,k=\frac{5}{2}\)

we obtain:

\(\displaystyle x=\frac{50}{\sqrt{\left(\frac{5}{2}\right)^2-1}}=\frac{100}{\sqrt{21}}\approx21.82\)

So, our answers agree. (Yes)
 

FAQ: Building a Pipeline Optimization Problem

What is a pipeline optimization problem?

A pipeline optimization problem is a mathematical problem that involves finding the most efficient way to transport materials or resources through a network of interconnected pipelines. It takes into account factors such as the flow rate, pressure, and temperature of the materials, as well as the layout and design of the pipeline network.

Why is building a pipeline optimization problem important?

Building a pipeline optimization problem allows for the efficient and cost-effective transportation of materials through a pipeline network. It can also help prevent issues such as leaks, blockages, and inefficiencies in the system, which can save time and resources in the long run.

What are the key components of a pipeline optimization problem?

The key components of a pipeline optimization problem include the physical parameters of the pipeline network (such as diameter, length, and material), the properties of the materials being transported (such as density and viscosity), and the objectives of the optimization (such as minimizing cost or maximizing flow rate).

How do you approach building a pipeline optimization problem?

The first step in building a pipeline optimization problem is to gather data and analyze the current system to identify any inefficiencies or areas for improvement. Then, mathematical models and algorithms are used to simulate and optimize the pipeline network. The results are then evaluated and the system can be adjusted accordingly.

What are some challenges in building a pipeline optimization problem?

Some challenges in building a pipeline optimization problem include the complexity of the pipeline network, the variability of the materials being transported, and the need to balance multiple objectives. Additionally, accurate data collection and modeling can be time-consuming and costly.

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