Process performance models in the optimization of multiproduct protein production plants

Abstract

In this work we propose a model that simultaneously optimizes the process variables and the structure of a multiproduct batch plant for the production of recombinant proteins. The complete model includes process performance models for the unit stages and a posynomial representation for the multiproduct batch plant. Although the constant time and size factor models are the most commonly used to model multiproduct batch processes, process performance models describe these time and size factors as functions of the process variables selected for optimization. These process performance models are expressed as algebraic equations obtained from the analytical integration of simplified mass balances and kinetic expressions that describe each unit operation. They are kept as simple as possible while retaining the influence of the process variables selected to optimize the plant. The resulting mixed-integer nonlinear program simultaneously calculates the plant structure (parallel units in or out of phase, and allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes, and operating times of semicontinuous items), and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases in the liquid-liquid extraction). A noteworthy feature of the proposed approach is that the mathematical model for the plant is the same as that used in the constant factor model. The process performance models are handled as extra constraints. A plant consisting of eight stages operating in the single product campaign mode (one fermentation, two microfiltrations, two ultrafiltrations, one homogenization, one liquid-liquid extraction, and one chromatography) for producing four different recombinant proteins by the genetically engineered yeast Saccharomyces cerevisiae was modeled and optimized. Using this example, it is shown that the presence of additional degrees of freedom introduced by the process performance models, with respect to a fixed size and time factor model, represents an important development in improving plant design.

Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

“…Let us recall that (Pinto et al, 2001) only used a monocriterion optimization approach based on investment cost for batch plant design with equipment sizes and operating conditions as variables, under several intermediate storage policies.…”

“…Two preliminary simulation runs were performed in order to validate our approach on the example treated by (Pinto et al, 2001). …”

“…A brief description of each process stage is presented in what follows and the different assumptions used to compute the processing time and the mass balance through the plant are given (for a more detailed description see (Pinto et al, 2001). Let us note that some differences in the hypothesis were carried out because this work aims at describing the involved unit operations with simple models, in order to obtain complete information about the treatment stage (i.e.…”

Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

“…The case study is a multiproduct batch plant for the production of four recombinant proteins (Dietz et al, 2004a, 2004b, Dietz, Azzaro-Pantel, Pibouleau, & Domenech, 2004cAsenjo, Montagna, Vecchietti, Iribarren, & Pinto, 2000;Montagna, Vecchietti, Iribarren, Pinto, & Asenjom, 2000;Pinto, Montagna, Vecchietti, Iribarren, & Asenjo, 2001). This is a multiproduct batch plant, with four products to be manufactured by fermentation and eight treatment stages.…”

The Integrated Simulation and Assessment of the Impacts of Process Change in Biotherapeutic Antibody Production

“…The application of process modeling within the bioprocess sector is in its infancy (18). Technical models of single stages (19), such as fermentation (20), filtration (21), centrifugation (22,23), homogenization (24,25), and ion exchange, size exclusion, affinity and expanded bed chromatography (26)(27)(28)(29) do exist, but development of multistage simulations has been limited largely to 3-4 unit operations, for example (30). While such models constitute a useful first step toward the production of robust and predictive modeling packages, it is the simultaneous consideration of all operations and replication of the interactions between them that is needed for the assessment of whole process feasibility.…”