Dynamic simulations in the design of batch processes

Salomone, Enrique; Montagna, Jorge Marcelo; Iribarren, Oscar Alberto

doi:10.1016/0098-1354(94)85008-9

Cited by 33 publications

(30 citation statements)

References 3 publications

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“…In the case of composite stages with a semicontinuous item that processes the material held in a batch item (as in the case of the homogenizer), the modeling approach of Salomone et al (1994) is followed in this study. The stage is described with Eq.…”

Section: Process Descriptionmentioning

confidence: 99%

“…There is abundant literature that approaches single-product, single-stage cases with quite detailed models (e.g., fermentors such as in Uesbeck et al [1998], extractors as in Mistry et al [1996], etc.). Single-product multistage cases resort to simpler dynamic models (e.g., for reaction-evaporation stripping in Salomone et al [1994], reaction separation networks in Smith and Pantelides [1995], distillation trains in Sharif et al [1998], particle separation processes in Agena et al [1998], etc.). Finally, multiproduct, multistage cases (e.g., Bhatia and Biegler, 1996;Montagna et al, 1994) resort to even simpler stage models.…”

Section: Introductionmentioning

confidence: 99%

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Process performance models in the optimization of multiproduct protein production plants

Pinto

Montagna

Vecchietti

et al. 2001

Biotech & Bioengineering

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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.

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Section: Process Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Process performance models in the optimization of multiproduct protein production plants

Pinto

Montagna

Vecchietti

et al. 2001

Biotech & Bioengineering

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show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2000

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In this work we propose an optimization model for the design of a biotechnological multiproduct batch plant. A first level of detail posynomial model is constructed for each unit, as well as decisions regarding the structural optimization of the plant. A particular feature of this model is that it contains composite units in which semicontinuous items operate on the material contained by batch items. This occurs in the purification steps, in particular with the microfilters operating between retentate and permeate vessels, and with the homogenizer and ultrafilters operating on the material contained in a batch holding vessel. Also, the unit models rely on batch operating time expressions that depend on both the batch size and the size of semicontinuous items. The model takes into account all of the available options to increase the efficiency of the batch plant design: unit duplication in-phase and out-of-phase and intermediate storage tanks. The resulting mathematical model for the minimization of the plant capital cost is a mixed integer non-linear program (MINLP), which is solved to global optimality with an implementation of the outer approximation/ equality relaxation/ augmented penalty (OA/ER/AP) method. A plant that produces four recombinant proteins in eight processing stages is used to illustrate the proposed approach. An interesting feature of this example is that it represents an attempt to standardize a plant for the production of both therapeutic and nontherapeutic proteins; the model applied is generic and can thus be applied to any such modular plant. Results indicate that the best solution in terms of minimal capital cost contains no units in parallel and with intermediate storage tank allocation.

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“…Salomone and Iribarren (1992) proposed posynomial models in which the constants are obtained as a result of the optimization of the process decision variables with simplified models. Salomone et al (1994) generalized the approach by allowing the process parameters to be generated from either experimental data and/or dynamic simulation. Because of the NP-hard nature of the design problem of batch plant, unbearable long computational time will be induced by the use of Mathematical Programming (MP) when the design problem is somewhat complicated.…”

Section: Introductionmentioning

confidence: 99%

Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

Hamzaoui¹,

Hernández²,

Cruz-Chávez³

et al. 2010

Chemical Product and Process Modeling

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This work deals with the problem of the search for optimal design of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem in two different ways: the first way is by using Monte Carlo Method (MC) and the second way is by Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a fourpoint crossover operator, which take into account simultaneously, the uncertainty on the demand using Gaussian process modeling with two criteria maximization the Net Present Value (NPV) and Flexibility Index (FI). The results (number and size of equipment, investment cost, production time (Hi), NPV, FI, CPU time and Idle times in plant) obtained by GAs are better than the MC. This methodology can help the decision makers and constitutes a very promising framework for finding a set of "good solutions."

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Dynamic simulations in the design of batch processes

Cited by 33 publications

References 3 publications

Process performance models in the optimization of multiproduct protein production plants

Process performance models in the optimization of multiproduct protein production plants

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

Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

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