Abstract

In this work we propose optimization approaches that make use of process information for the design of multiproduct batch plants. First, we develop an optimization model for designing multiproduct batch plants. The plant consists of several parallel stages, which can work either in phase or out-of-phase. Processing times and size factors for each unit of the plant rely on first level of detail posynomial models. A particular feature of these models is that they may contain composite units where semi continuous items operate on the material contained by batch items. Secondly, we propose an approach in which the optimization model includes process performance models for the unit stages and a posynomial model for the multiproduct batch plant. The process performance models define the size and time factors of the posynomial model, as functions of the process variables selected to optimize the plant. These are expressed as algebraic equations obtained from the analytical integration of simplified mass balances and kinetic expressions that describe each unit operation Both approaches result in Mixed Integer Non Linear Programming (MINLP) models. In the first approach, since unit as well as structural plant constraints are posynomial, a convexified MINLP is solved to global optimality The decision variables are the number of parallel units in phase and out of phase and their size at each batch stage, the installation or not of intermediate storage between the batch stages and their size. In the second approach, we simultaneously optimize the structure of the plant, the batch plant decision variables and the process decision variables. A biotechnological plant consisting of eight stages operating in Single Product Campaign mode was modeled and optimized by using both approaches. Using this example, it is shown that the additional degrees of freedom introduced by the process performance models with respect to a fixed size and time factor model has a noticeable impact on improving the plant design.