Design Of Multipurpose Batch Plants Research Articles

Simultaneous optimization of scheduling, equipment dimensions and operating conditions of sequential multi-purpose batch plants

Abstract The design of multi-purpose batch plants is a challenging task, because the number of degrees of freedom for optimization is high. Important optimization variables are the scheduling, operating conditions and the sizes of the equipment items. Since all factors interact, a simultaneous consideration would be beneficial in order to reduce capital and operating costs. In this article, this complex task is tackled for a new case study of a sequential plant for protein production. The case study contains comprehensive models of the unit operations to evaluate equipment dimensions, mass balances and operating times. Variable changeover times and semicontinuous unit operations are considered. For optimization, a MINLP model is used that consists of smaller NLP and MILP submodels in order to simplify modeling. Simulation runs for different product demands are performed and it is shown that good solutions can be found in an adequate time.

Design and Scheduling of Periodic Multipurpose Batch Plants under Uncertainty

This work deals with the design of multipurpose batch plants under uncertainty. The model proposed by Pinto et al. [Comput. Chem. Eng. 2005, 29 (6), 1293−1303] for the detailed design of batch plants is extended to address the problem of uncertainty associated with production demand. Equipment choices, as well as plant topology and associated schedule, are defined simultaneously under an uncertain demand environment. Uncertainty is treated through a two-stage stochastic model leading to a mixed-integer linear programming (MILP) formulation, where profit is maximized and the expected value of perfect information (EVPI) is determined; the latter is estimated as the difference between the values of profit obtained for the wait-and-see and the here-and-now models. A scenario is set up where demand is represented by a discrete probability function and a cyclic operation considered, together with mixed storage policies and sharing of resources. Some illustrative examples are solved to show the model applicability and the EVPI are determined and analyzed.

Design of Multipurpose Batch Plants: A Comparative Analysis between the STN, m-STN, and RTN Representations and Formulations

This paper presents and discusses different unified frameworks that can be used for design of multipurpose batch plants. The first framework is based on the state−task network (STN) process representation proposed by Kondili, Pantelides, and Sargent (1993, 1998) for the scheduling of batch plants. The second is the maximal state−task network (m-STN) where design and operational characteristics are represented simultaneously (Barbosa-Póvoa and Machietto, 1994). Finally, the third framework uses the resource−task network (RTN), proposed by Pantelides (1994) for the scheduling problem. The application of these three representations to the design problem is explored and generalized formulations are developed, which are described and characterized. A set of examples is solved and results are compared and discussed, with an emphasis on the adequacy and effectiveness of these representations to the modeling and solution of the design of multipurpose batch plants.

A New Algorithm for Cyclic Scheduling and Design of Multipurpose Batch Plants

Many researchers have studied the scheduling, planning, and design of multipurpose batch processes. However, not so many studies have treated design and scheduling or design and planning simultaneously. The complexity of these systems stems from the fact that plant configuration must be determined for the purpose of process scheduling, yet scheduling must be done to devise the plant configuration. In the present study, a new algorithm for determining the best multipurpose scheduling and plant configuration is suggested. Since the objective function of the problem is nonlinear, it is linearized using a separable programming method. The proposed method consists of a number of procedures. First, a feasible configuration is obtained. Next, both the optimum equipment size and cyclic scheduling are determined for the plant configuration obtained in the first procedure. Last, the evolutionary design method proposed by Fuchino et al.(J. Chem. Eng. Jpn. 1994, 27, 57−64) is used to find the solution that minimizes the total cost. The efficacy of the proposed approach is demonstrated in three examples. Usually, equipment sizes are considered by a continuous variable in mixed integer linear programming and task processing time is assumed to be constant. However, most types of equipment are manufactured only in discrete volume classes. In addition, processing times are dependent on batch sizes. Hence, to apply the optimization methods developed here to real industries, the method was modified such that the volumes of equipment are considered as discrete variables and the processing time is a function of batch size.

Retrofit design of multipurpose batch plants with multiple production routes

This work analyzes possible alterations of configuration of an existing multipurpose batch plant for which new production targets and selling profits have been specified. New equipment can be added to the batch plant in the form of a retrofit in order to become a more economical and optimal design. The retrofit problem is posed as a mixed-integer formulation (MILP). The application of the proposed model is illustrated using three examples where can be verified that the method is efficient and of easy application, when compared of the others strategies to solve the retrofit problem.

Environmental impact considerations in the optimal design and scheduling of batch processes

A systematic methodology for incorporating ecological considerations in the optimal design and scheduling of batch/semi-continuous processes is presented in this paper. The methodology embeds principles from Life Cycle Analysis (LCA) within a general multi-objective formulation for the design of multipurpose batch plants, with process economics and environmental impact as distinct design objectives. An expanded boundary is defined around the process of interest, for the consistent evaluation of environmental impact, which is quantified by a set of metrics (for air, water pollution, global warming etc.). Examples from the dairy industry are presented to demonstrate the potential of the methodology to assist in arriving at environmentally friendly and economically favourable batch designs and schedules. Issues regarding the use of alternative cleaning and legislation policies on batch operation and design are also discussed.

Design of multipurpose plants using the resource-task network unified framework

A new formulation is presented for the optimal design of multipurpose plants. The proposed model permits a detailed consideration of the design problem taking account of trade-offs between capital costs, revenues and operational flexibility. The optimal solution involves the selection of the required processing and storage equipment items, and the required levels of provision of other production resources such as utilities, manpower, cleaning and transportation equipment. The recently proposed unified Resource-Task Network (RTN) framework ( Pantelides, 1994) is used for the process representation. A simple Mixed Integer Linear Programming (MILP) formulation is developed. Binary variables are used to represent the operational and structural choices that have to be made. An example illustrating the flexibility and the applicability of the new approach to the design of multipurpose batch plants is presented.

Perspectives on model based integration of process operations

The chemical process industry has increasingly been pursuing the use of computing technology to gather, organize, disseminate and exploit enterprise information and to closely coordinate the decisions made at the various levels of the process operational hierarchy so as to optimize overall corporate objectives. The thesis advanced in this paper is that mathematical programming models and solution methods offer the most effective tools for integration of the tactical and strategic levels of the operational hierarchy. To that end, existing strategies for implementing model-based integrated applications are reviewed. Four classes of examples of integration are presented: scheduling of multiproduct plants, large-scale model predictive control, integration of planning and scheduling across single and multiple plant sites and design of multipurpose batch plants under uncertainty. The methodology used to address these model-based integration instances successfully accomodates the key features of process operations: diverse time scale, multiple reference frameworks, spatial and organizational aggregation/disaggregation and uncertainty in the enterprise information. The applications further demonstrate that for the foreseeable future no single model or reference framework will be sufficient and efficient for treating all aspects of the process operations hierarchy.

A branch and bound procedure for the design of multipurpose batch plants with uncertain demands

A systematic procedure for the design of multipurpose batch plants subject to uncertain demands is proposed. The approach relies on the sequential refinement of upper and lower bounds on a stochastic flexibility index (SF). An optimization procedure is presented for the case of uncertain demands and discrete sizes of processing units. The procedure consists of the solution of a sequence of MILP problems. It guarantees the selection of the plant design with the best flexibility index value (within a given tolerance). The method is demonstrated on a case study.

Design of Multipurpose Batch Plants with Multiple Production Plans Based on Cyclic Production.

The design challange in multipurpose batch plants is optimizing plant configuration and equipment size under the constraints of multiple production plans, and these decision variables are interactive with each other through scheduling. To optimize these decision variables, the evolutionary design method on the basis of scheduling according to cyclic production (cyclic scheduling) is appropriate, and batch composition of a cycle (number of batches and their relative batch sizes per one-cycle) for respective production plans should be specified beforehand. In this study, a method to specify the batch composition that enables optimal design is developed. From the viewpoint of optimizing plant configuration, the common batch sizes for a multiple number of production plans (core batch sizes) are introduced for each product, and specifying the batch composition on the basis of these core batch sizes is converted into assignment problems, from the viewpoint of optimizing equipment sizes. These assignment problems are formulated into IP (Integer Programming) models, and the optimal design satisfying multiple production plans becomes possible. The effectiveness of this method is demonstrated through an example problem.

Detailed design of multipurpose batch plants

A formulation is presented for the optimal selection of both the equipment units and the network of connections for multipurpose batch plants so as to satisfy given production requirements for a variety of products. The model includes very general constraints and objective functions and permits to optimize simultaneously the structural aspects of the plant and the associated production schedule, accounting for both capital costs of equipment units and pipework, operating costs and revenues. A special novelty of the formulation is that it provides the possibility of generating directly the optimal connectivity between the process units, optimal sizing and costs of the transfer lines, and the optimal storage policies (and associated vessel sizes, if any) for stable intermediates without the need for a priori assumptions (e.g. unlimited storage, etc.). The resulting MILP problem is solved using a branch and bound method. Several examples are detailed, demonstrating the generality of the approach and the importance of including structural and operational aspects at the design stage. The flexibility of the formulation permits utilizing various design strategies, from a simultaneous optimization of all problem decisions (equipment, connections and schedule) to a sequential optimization of main equipment, followed by the design of connections etc. These strategies are also illustrated by an example.

Scheduling method in design of multipurpose batch plants with constrained resources.

A scheduling method that incorporates the design of multipurpose batch plants is developed. The scheduling consists of two interactive sub-problems: production scheduling and constrained resources scheduling, and on-site work is one of the typical constrained resources. In the design of these plants, the optimal schedule should be made within a short time, and cyclic scheduling according to the cycle production is appropriate. An IP (Integer Programming) model is suitable for such short-term scheduling, and the scheduling method is developed on the basis of the previous IP model for production scheduling. To make the optimal schedule, the allocation and sequence of tasks of on-site work should be optimized in relation with the production scheduling. In this study, the required properties of the on-site work scheduling in design is considered, and this sub-problem is converted into an assignment problem. The on-site work is defined in linear constraints with integer variables describing the task assignments. Moreover, the on-site work is newly defined in the production scheduling, and these definitions are related by the introduced integer variables. Finally, the effectiveness of the developed scheduling method is demonstrated by an example problem.

The Design and Scheduling of Multipurpose Batch Plants

In multipurpose batch plants, the production rates are strongly dependent not only on the level of the available resources, but also on the efficiency of resource utilisation. Consequently, plant scheduling must be considered simultaneously with the plant design problem. This paper considers the design of multipurpose batch plants operating in a cyclic mode in which the same pattern of operations is repeated at a constant frequency over long time periods. The design problem involves the simultaneous determination of the required levels of processing equipment, storage capacity, and utility provisions, as well as a detailed cyclic operating schedule that fully exploits the inherent flexibility of batch plants in utilising such resources. This problem is formulated rigorously as a mixed integer linear program (MILP) that is solved by a branch-and-bound method, enhanced by exploiting the problem structure.

Design of multipurpose batch plants with uncertain production requirements

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTDesign of multipurpose batch plants with uncertain production requirementsNilay Shah and Constantinos C. PantelidesCite this: Ind. Eng. Chem. Res. 1992, 31, 5, 1325–1337Publication Date (Print):May 1, 1992Publication History Published online1 May 2002Published inissue 1 May 1992https://doi.org/10.1021/ie00005a013RIGHTS & PERMISSIONSArticle Views171Altmetric-Citations57LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-Alerts Get e-Alerts

Optimal design of multipurpose batch plants. 2. A decomposition solution strategy

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTOptimal design of multipurpose batch plants. 2. A decomposition solution strategySavoula Papageorgaki and Gintaras V. ReklaitisCite this: Ind. Eng. Chem. Res. 1990, 29, 10, 2062–2073Publication Date (Print):October 1, 1990Publication History Published online1 May 2002Published inissue 1 October 1990https://doi.org/10.1021/ie00106a014RIGHTS & PERMISSIONSArticle Views57Altmetric-Citations22LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-Alerts Get e-Alerts

An embedding formulation for the optimal scheduling and design of multipurpose batch plants

Summary of the Basic StepsThe basic steps in the suggested procedure for the optimal design of multi-purposebatch plants can be summarized as follows:1. List the candidate product groups.2. Obtain the super-structure that contains the candidate groups.3. Derive the horizon constraints..4. Examine the horizon constraints. If at least one horizon constraintimplicitly contains all the time periods, complete the merged formulationand solve the MINLP given in Equations (38) to (44) with the correspondinghorizon constraints. Otherwise, go to step 5.5. Select a product(s) that breaks the loop(s) in the super-structure. Treat thechosen product(s) as two or more artificial products.6. Revise the super-structure to include the additional products. Verify thatthe loop is broken. If this condition is met, go to step 7. Otherwise,return to step 5 and modify the products chosen for splitting.7. Derive the multi-period form of the constraints for the product(s) that aresplit into its (their) values for the time periods in which it (they) appear.Formulate the merged representation of the model for the rest of theproducts, including the horizon constraints. At least one horizonconstraint will contain implicitly all the time periods.Example 2The seven product, ten stage example problem of Suhami and Mah (1982) has beensolved to illustrate the design of a large multi-purpose batch plant. From the super-structure of this problem (Figure 4), the horizon constraints shown in Table II arederived.Using these constraints, the example has been solved with the merged formulation.Data for this problem appears in Table III. In addition, the following specificationshave been used: H = 6200 hr, a = 250 SFr, fi = 0.6, 1 < N <, 3, and 250 < V£ 10,000. As seen in Table IV, the MINLP formulation involves 48 variables and 67constraints. MINOS/Augmented (Murtagh and Saunders, 1980) required 16.09 secondsof CPU-time on a DEC-20 computer to solve the relaxed nonlinear program.

New procedure generating suboptimal configurations to the optimal design of multipurpose batch plants

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTNew procedure generating suboptimal configurations to the optimal design of multipurpose batch plantsMasaru Imai and Naonori NishidaCite this: Ind. Eng. Chem. Process Des. Dev. 1984, 23, 4, 845–847Publication Date (Print):October 1, 1984Publication History Published online1 May 2002Published inissue 1 October 1984https://doi.org/10.1021/i200027a036RIGHTS & PERMISSIONSArticle Views19Altmetric-Citations8LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (347 KB) Get e-Alerts

Optimal design of multipurpose batch plants

Optimal design of multipurpose batch plants