 There are very many new products that appear in the biotechnology area that need to be commercialized or tested, but most of these products need to be produced in small quantities. So people who develop these products tend to go and hire pilot plans or small production plans in order to produce them. This is how the concept of a multi-product batch plant appears. A few years ago one of my students had the opportunity to go to Berkeley and work in a company that had such a pilot plant or such a plant. So to generate this paper we had real data of an actual plant that say in a campaign of one year had to produce four totally different products. Now with my student, Gabriela Sandoval, who is the main author of this paper, we worked at developing modern tools in order to study the optimization using computer techniques and modern mathematical techniques in order to optimize the equipment in such a plan. Now these computational techniques that were developed in a previous paper were now applied to a real plant with real data, which is the value of this paper. So now, Gabriela is going to explain with much more detail the main novelty of this paper, the methodology, the novel methodology she developed and how this has an impact or will have an impact, we hope, in the biotechnology industry. Hello, I'm Gabriela Sandoval and I'm going to talk to you about a little of my paper published in biotechnology and bioengineering. In this paper, we used an improvement of a previous work where we want to design a multi-product batch plant that produces four products. In our model, we maintain the an important future of our model that permits the selection of equipments in a continuous range of sizes. That is a key future that is very important because we know that some equipments can be built according to customer needs. But we also know that some items such as centrifuges or homogenizers come in discrete sizes. So in this article we included some equations that permit us to select discrete sizes of equipments and obviously select some discrete costs. So another important future of our work is that with real known data, we are able to compute parameters of our model that are size and time factors. So with this, we can now apply our model to real data and we can design a multi-product batch plant that is able to produce four products with 44 stages of downstream processing and the more important is that our plant is not oversized. As a matter of fact, the result is that the cost, the equipment cost of an optimized plant in order to produce these four products went down more than 50% with the optimization tools that were developed in the paper. The results of my paper are quite impressive, so you're invited to download it and if you have any comments or doubts you can write me right here to my email address. Thank you!