Optimal protein separation

Jennings, Luke

doi:10.1016/0098-1354(94)00099-a

Cited by 9 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time transformation method used by Jennings et al is based on the substitution t = st f , where t ∈ [0, t f ] is the original time variable, s ∈ [0, 1] is the new time variable, and t f is the final time of the process. The same transformation has been successfully applied to solve time-optimal control problems. , When applied to the chromatography optimal control problem, this transformation does not map the retention times to fixed points; it maps only the final time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Max–Min Control Problem Arising in Gradient Elution Chromatography

Chai

Loxton

Teo

et al. 2012

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Gradient elution chromatography is an industrial process used to separate and purify multi-component chemical mixtures. In this article, we consider an optimal control problem in which manipulative variables in the chromatographic process need to be determined to maximize separation efficiency. This problem has two nonstandard characteristics: (i) the objective function is nonsmooth, and (ii) each state variable is defined over a different time horizon. The final time for each state variable, the so-called retention time, is not fixed and actually depends on the control variables. To solve this optimal control problem, we first introduce a set of auxiliary decision variables to govern the ordering of the retention times. Then, we approximate the control by a piecewise-constant function and apply a novel time-scaling transformation to map the retention times and control switching times to fixed points in a new time horizon. The retention times and control switching times become decision variables in the new time horizon. On this basis, the optimal control problem is reduced to an approximate nonlinear optimization problem that can be solved using a recently developed exact penalty method. Numerical results show that our approach is both accurate and efficient.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In this article, we consider the same chromatography optimal control problem as formulated by Jennings et al We propose a new method for reformulating this problem that facilitates accurate determination of the retention times. First, a set of auxiliary decision variables are introduced to govern the ordering of the retention times.…”

Section: Introductionmentioning

confidence: 99%

A Max–Min Control Problem Arising in Gradient Elution Chromatography

Chai

Loxton

Teo

et al. 2012

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Using the control parametrization technique, the control variables are approximated by piecewise linear functions, and consequently a sequence of nonstandard optimal parameter selection problems is obtained. Each of these approximate problems was then shown to be equivalent to a standard min‐max optimization problem and solvable by the existing optimization software (7).…”

Section: Introductionmentioning

confidence: 99%

Optimal Synthesis of Protein Purification Processes

2001

View full text Add to dashboard Cite

There has been an increasing interest in the development of systematic methods for the synthesis of purification steps for biotechnological products, which are often the most difficult and costly stages in a biochemical process. Chromatographic processes are extensively used in the purification of multicomponent biotechnological systems. One of the main challenges in the synthesis of purification processes is the appropriate selection and sequencing of chromatographic steps that are capable of producing the desired product at an acceptable cost and quality. This paper describes mathematical models and solution strategies based on mixed integer linear programming (MILP) for the synthesis of multistep purification processes. First, an optimization model is proposed that uses physicochemical data on a protein mixture, which contains the desired product, to select a sequence of operations with the minimum number of steps from a set of candidate chromatographic techniques that must achieve a specified purity level. Since several sequences that have the minimum number of steps may satisfy the purity level, it is possible to obtain the one that maximizes final purity. Then, a second model that may use the total number of steps obtained in the first model generates a solution with the maximum purity of the product. Whenever the sequence does not affect the final purity or more generally does not impact the objective function, alternative models that are of smaller size are developed for the optimal selection of steps. The models are tested in several examples, containing up to 13 contaminants and a set of 22 candidate high-resolution steps, generating sequences of six operations, and are compared to the current synthesis approaches.

show abstract

“…Rational process design uses computer models to identify optimal separation coefficients, which appear to be of value in a chromatography experiment. In the conventional setting an explicit set of experiments must first be performed and the optimum conditions are selected (Jennings et al, 1995;Leser and Asenjo, 1992). The rational computer-assisted * To whom all correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Fundamental questions in optimizing ion-exchange chromatography of proteins using computer-aided process design

Jungbauer

Kaltenbrunner

2000

Biotechnol. Bioeng.

View full text Add to dashboard Cite

The major objectives for preparative protein chromatography are maximal loading and increased flow rate while maintaining defined resolution. Conventionally a series of chromatographic experiments are performed and the optimal conditions are selected according to the separation criteria. Computer‐aided process design uses the same strategy, except a group of related experiments are generated by computer simulation. The access to concrete separation parameters for valid simulation necessitates chromatographic experiments. Optimal conditions are determined in the same manner as conducted in the conventional strategy. Beside other parameters, the distribution coefficient (K) determines the performance of a chromatographic purification under overloading conditions. In ion‐exchange chromatography the distribution coefficient is strongly influenced by the protein concentration (C) and the salt concentration (I). A strategy to derive the distribution coefficient from chromatographic experiments, such as isocratic runs (pulse response), linear gradients, and frontal analysis, is described and compared to previously published strategies. In ion‐exchange chromatography, the number of plates and transfer units change with the salt concentration. The distribution coefficient for salt also changes under various conditions including salt and protein concentration. The number of plates and transfer units also vary with the flow rate. Furthermore criteria such as the multicomponent situation require a more complex mathematical treatment. Several solutions have been validated to circumvent those obstacles. © 1996 John Wiley & Sons, Inc.

show abstract

Optimal protein separation

Cited by 9 publications

References 0 publications

A Max–Min Control Problem Arising in Gradient Elution Chromatography

A Max–Min Control Problem Arising in Gradient Elution Chromatography

Optimal Synthesis of Protein Purification Processes

Fundamental questions in optimizing ion-exchange chromatography of proteins using computer-aided process design

Contact Info

Product

Resources

About