Optimal Design of Protein Production Plants with Time and Size Factor Process Models

Montagna, Jorge Marcelo; Vecchietti, Aldo; Iribarren, Oscar Alberto; Pinto, José M.; Asenjo, Juan A.

doi:10.1021/bp990145d

Cited by 52 publications

(72 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Conceptually, the mathematical optimization model for the design of the multiproduct batch plant will be the same as that presented by Montagna et al (2000) if size and time factors were held constant, plus the additional constraints that describe these factors as functions of the process variables. As a result, it is expected that the introduction of these new degrees of freedom into the optimization model will provide a better design.…”

Section: Process Performance Modelsmentioning

confidence: 99%

“…Furthermore, there is a set of constraints that correspond to the upper and lower bounds for all variables involved. A more detailed description has been given by Montagna et al (2000).…”

Section: Design Model For the Multiproduct Plantmentioning

confidence: 99%

“…For the design and structural optimization of the multiproduct batch plant a modular model that considers in-and out-of-phase parallel units and allocation of intermediate storage tanks is considered. The results obtained with the process performance models are compared with a more traditional approach that considers constant size and time factors (Montagna et al, 2000). Figure 1 shows the flowsheet of a multiproduct batch plant intended for the production of recombinant proteins.…”

Section: Introductionmentioning

confidence: 99%

“…In most process flowsheets for recombinant proteins there are differences, which depend on the specific host used to synthesize the product and on the specific properties of the product and its contaminants that will determine the purification process, as well as its final use. We have recently made an attempt to "standardize" such a process for purposes of generating a generic plant (Montagna et al, 2000). In this process, even a liquid-liquid extraction step was included as an initial separation/purification stage.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Process performance models in the optimization of multiproduct protein production plants

Pinto

Montagna

Vecchietti

et al. 2001

Biotech & Bioengineering

View full text Add to dashboard Cite

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.

show abstract

Section: Process Performance Modelsmentioning

confidence: 99%

“…Furthermore, there is a set of constraints that correspond to the upper and lower bounds for all variables involved. A more detailed description has been given by Montagna et al (2000).…”

Section: Design Model For the Multiproduct Plantmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Process performance models in the optimization of multiproduct protein production plants

Pinto

Montagna

Vecchietti

et al. 2001

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…The same formulation was used again by Patel et al [4] who treated larger size examples with Simulated Annealing and by Wang et al [5][6][7] who tackled successively Genetic Algorithms, Tabu Search and an Ants Foraging Method. Then, Montagna et al [8] studied a quite similar formulation for the design of a protein production plant, with the location of the storage tanks being an optimisation variable: the solution, obtained with DICOPT++ [2], proves to be a more viable alternative thanks to storage availability.…”

Section: E-mail Addressmentioning

confidence: 99%

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Ponsich

Azzaro‐Pantel

Domenech

et al. 2008

Chemical Engineering and Processing: Process Intensification

View full text Add to dashboard Cite

Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming (MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.

show abstract

Multiproduct batch plants design using linear process performance models

Moreno

Montagna

2010

AIChE Journal

View full text Add to dashboard Cite

In this contribution, a novel linear generalized disjunctive programming (LGDP) model is developed for the design of multiproduct batch plants optimizing both process variables and the structure of the plant through the use of process performance models. These models describe unit operations using explicit expressions for the size and time factors as functions of the process variables with the highest impact. To attain a linear formulation, values of the process variables as well as unit sizes are selected from a set of meaningful discrete values provided by the designer. Regarding structural alternatives, both kinds of unit duplications in series and in parallel are considered in this approach. The inclusion of the duplication in series requires different detailed models that depend on the structure selected. Thus, in a new approach for the multiproduct batch plant design, a set of potential structural alternatives for the plant is defined. V V C 2010 American Institute of Chemical Engineers AIChE J, 57: 122-135, 2011 Keywords:LGDP, performance models, multiproduct batch plants, duplication in series IntroductionA large number of optimization models for the design of multiproduct batch plants have been developed over the last decades.1 This kind of plants produces a number of related products using the same equipment in the same operation sequence. Mainly, approaches with constant time and size factors are the most used in the modeling of such plants. [2][3][4][5][6][7][8][9][10][11] Similar approaches using constant factors have also been presented in the field of multipurpose batch plant design. [12][13][14][15][16] However, these models fix the process variables to get a determined process recipe avoiding the evaluation of various economic trade-offs involved in the design decisions. To attain more detailed formulations, process performance models have been included into the design of multiproduct batch plants. The performance models describe size and time factors as a function of the process decision variables (i.e., variables with the highest economic impact on the process) selected for the optimization. Several contributions [17][18][19][20][21][22][23] have tackled performance models instead of fixed recipes to incorporate information about the production process in the plant design. Specifically, the performance models entail additional algebraic equations obtained from mass balances and simplified kinetic equations that describe every unit operation in the process. They may be constant values, an equation, or even a system of equations in accordance with the selected level of detail.In all of the aforementioned problem formulations, the final model is nonlinear and, generally, depending on the proposed representation for the unit operations, nonconvex.Correspondence concerning this article should be addressed to M. S. Moreno at smoreno@santafe-conicet.gov.ar. 122AIChE Journal January 2011 Vol. 57, No. 1 Typically, the most common approach used to solve this problem has been to formulate it as a mixe...

show abstract

Optimal Design of Protein Production Plants with Time and Size Factor Process Models

Cited by 52 publications

References 13 publications

Process performance models in the optimization of multiproduct protein production plants

Process performance models in the optimization of multiproduct protein production plants

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Multiproduct batch plants design using linear process performance models

Contact Info

Product

Resources

About