Optimal Reliable Retrofit Design of Multiproduct Batch Plants

Goel, H.D.; Weijnen, T. Margot P. C.; Grievink, Johan

doi:10.1021/ie030656b

Cited by 12 publications

(7 citation statements)

References 14 publications

(37 reference statements)

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“…From the commercial point of view, this approach is inappropriate. However, in this way, the resolution is simplified, focusing on the sizing problem. − Finally, short-term scheduling is subsequently solved with certain data in a later step.…”

Section: Introductionmentioning

confidence: 99%

Detailed Design of Multiproduct Batch Plants Considering Production Scheduling

Fumero

Corsano

Montagna

2011

Ind. Eng. Chem. Res.

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Most previous approaches for the design of multiproduct batch plants have assumed the simplest scheduling policy. In order to simplify the formulation and take into account that many times demands are uncertain, they have used single product campaigns to determine the plant configuration and select the unit sizes. From the commercial point of view, this production mode is not realistic: for example, huge inventories should be kept to support this approach. However, when a stable context can be assured, the simultaneous resolution of design and a more detailed scheduling allows assessing different trade-offs. This article presents a new mixed integer linear programming (MILP) formulation assuming mixed product campaigns. Now, the composition and the sequence of the batches in the campaign must be determined as well as the assignment of batches to units when parallel units are used. Taking into account that the plant configuration is simultaneously obtained, the scheduling problem must be solved without knowing the number of available units and their sizes. Several examples are presented in order to show the performance of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Detailed Design of Multiproduct Batch Plants Considering Production Scheduling

Fumero

Corsano

Montagna

2011

Ind. Eng. Chem. Res.

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“…The number, the required volume and size of parallel equipment units in each stage are then determined to minimize the investment. It should be emphasized that the batch plant's design has long been identified as a key problem in chemical engineering as reported in the literature (Montagna et al 2000;Cao and Yuan 2002;Chunfeng and Xin 2002;Heo et al 2003;Cavin et al 2004;Chakraborty et al 2004;Goel et al 2004;Pinto et al 2005). Formulation of a batch plant design generally involves mathematical programming methods, such as linear programming (LP), nonlinear programming (NLP), mixed-integer LP or mixed-integer NLP (MINLP).…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Optimization of Multiproduct Batch Chemical Process Using Genetic Algorithm

Mokeddem

Khellaf

2010

J Food Process Engineering

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Optimal design problems are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is first formulated as a multiobjective optimization problem, to be solved using the well‐suited nondominating sorting genetic algorithm (NSGA‐II). The NSGA‐II has the capability to achieve fine tuning of variables in determining a set of nondominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA‐II ability to identify a set of optimal solutions provides the decision maker with a complete picture of the optimal solution space to gain better and appropriate choices. The effectiveness of NSGA‐II method with multiobjective optimization problem is illustrated through a carefully referenced example. PRACTICAL APPLICATIONS The inherent dynamic nature of batch processes allows for their ability to handle variations in feedstock and product specifications, and provides the flexibility required for multiproduct or multipurpose facilities. They are thus best suited for the manufacture of low‐volume, high‐value products, such as specialty chemicals, pharmaceuticals, agricultural, food and consumer products, and most recently the constantly growing spectrum of biotechnology‐enabled products. Batch processing in the food industry has some unit operations, such as panning, whipping (emulsifying), and the pulling of taffy, which are not too often used in other industries, as well as other operations,such as blending, tabletting (including granulation) and lyophilization, which are common in other industries. Reduced time to market, lower production costs and improved flexibility are all critical success factors for batch processes.

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“…A further extension is a capacity expansion retrofit scenario of an integrated production and distribution system which is optimized by Lee et al 29 using an MINLP model. The objective functions used in the MINLP model optimization are extended by Goel et al 30 to account for batch plant inherent reliability and maintainability characteristics of existing and new equipment. The MINLP deterministic retrofitting approach is used by Papageorgaki and Reklaitis 31 to investigate the retrofit problem of a plant which accommodates changes in product demands and addition and elimination of units.…”

Section: Introductionmentioning

confidence: 99%

Systematic Retrofit Method for Chemical Batch Processes Using Indicators, Heuristics, and Process Models

Simon

Osterwalder

Fischer

et al. 2007

Ind. Eng. Chem. Res.

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This work presents a novel systematic indicator, heuristics, and process model based decision support framework for retrofitting chemical batch processes. The systematic framework considers the identification of improvement opportunities in a batch plant by considering first the product market situation. The batch plant analysis is structured on three levels: the plant, the process, and the unit operation level, respectively. The analysis of these levels is based on indicators which are of various types and specific for each analysis level. These indicators are linked to heuristics which are used to identify the retrofit actions. Finally, for each identified retrofit action the improvement potential is calculated in order to assess its importance. The developed method was successfully applied to a fine chemical batch production facility, and it was able to identify in a systematic way several retrofit actions with significant improvement potential.

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Optimal Reliable Retrofit Design of Multiproduct Batch Plants

Cited by 12 publications

References 14 publications

Detailed Design of Multiproduct Batch Plants Considering Production Scheduling

Detailed Design of Multiproduct Batch Plants Considering Production Scheduling

Multicriteria Optimization of Multiproduct Batch Chemical Process Using Genetic Algorithm

Systematic Retrofit Method for Chemical Batch Processes Using Indicators, Heuristics, and Process Models

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