deploy: dd2fdbc

_sources/notebooks/05/milk-pooling.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "Pooling and blending operations involve the \"pooling\" of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.\n",
     "\n",
-    "This notebook considers a simple example of a wholesale milk distributor to show how **non-convexity** arises in the optimization of pooling and blending operations. Non-convexity is due to presence of **bilinear** terms that are the product of two decision variables where one is a scale-dependent **extensive** quantity measuring the amount or flow of a product, and the other is scale-independent **intensive** quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of `ipotp`, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems."
+    "This notebook considers a simple example of a wholesale milk distributor to show how **non-convexity** arises in the optimization of pooling and blending operations. Non-convexity is due to presence of **bilinear** terms that are the product of two decision variables where one is a scale-dependent **extensive** quantity measuring the amount or flow of a product, and the other is scale-independent **intensive** quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of `ipopt`, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems."
    ]
   },
   {

notebooks/05/milk-pooling.html

@@ -528,7 +528,7 @@ <h2> Contents </h2>
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="milk-pooling-and-blending">
 <span id="index-6"></span><h1>Milk pooling and blending<a class="headerlink" href="#milk-pooling-and-blending" title="Permalink to this heading">#</a></h1>
 <p>Pooling and blending operations involve the “pooling” of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.</p>
-<p>This notebook considers a simple example of a wholesale milk distributor to show how <strong>non-convexity</strong> arises in the optimization of pooling and blending operations. Non-convexity is due to presence of <strong>bilinear</strong> terms that are the product of two decision variables where one is a scale-dependent <strong>extensive</strong> quantity measuring the amount or flow of a product, and the other is scale-independent <strong>intensive</strong> quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of <code class="docutils literal notranslate"><span class="pre">ipotp</span></code>, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems.</p>
+<p>This notebook considers a simple example of a wholesale milk distributor to show how <strong>non-convexity</strong> arises in the optimization of pooling and blending operations. Non-convexity is due to presence of <strong>bilinear</strong> terms that are the product of two decision variables where one is a scale-dependent <strong>extensive</strong> quantity measuring the amount or flow of a product, and the other is scale-independent <strong>intensive</strong> quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of <code class="docutils literal notranslate"><span class="pre">ipopt</span></code>, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems.</p>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>This cell selects and verifies a global SOLVER for the notebook.</p>