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Decomposition approaches for the design and scheduling of multiproduct multistage batch plants with parallel lines

COMPUTERS & CHEMICAL ENGINEERING 2019年 127卷 111-126页

作者： Verbiest, Floor Pinto-Varela, Tania Cornelissens, Trijntje Springael, Johan Barbosa-Povoa, Ana Univ Antwerp Dept Engn Management Operat Res Grp ANT OR Prinsstr 13 B-2000 Antwerp Belgium Inst Super Tecn CEG Av Rovisco Pais P-1049001 Lisbon Portugal

Multiproduct chemical batch plants, often equipped with parallel production lines, are facilities where similar products are produced by sharing available equipment and time. Hence, when designing such plants, the operational use should be considered simultaneously. In this paper, the design and scheduling problem of a batch plant with parallel lines is addressed, using the Resource-Task Network formulation. The proposed mixed-integer linear programming model aims at minimising total costs through the optimisation of the number of lines to install, their design and the number, size and timings of the batches of every product on the installed lines. Besides an integral (exact) approach, we applied three decomposition approaches to tackle the computational complexity associated with this problem: line decomposition, bottleneck decomposition and a combination of both. Through several examples, it is shown that all three decomposition approaches reduce the computation times significantly compared to the integral approach, while obtaining (near) optimal solutions. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： design and scheduling Batch plants Parallel lines Resource-Task Network

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The design and scheduling of chemical batch processes: Computational complexity studies

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COMPUTERS & CHEMICAL ENGINEERING 2019年 121卷 367-374页

作者： de Miranda, Joao Luis Inst Politecn Portalegre P-7300110 Portalegre Portugal Univ Lisbon Inst Super Tecn CERENA Ctr Recursos Nat & Ambiente Ave Rovisco Pais P-1049001 Lisbon Portugal

The pharmaceutical industry is quite restrictive concerning quality and safety, the manufacturing disruptions often lead to drug shortages in despite of the high costs involved. Due to the minimization of equipment costs, the design and scheduling of chemical batch processes (DSCBP) is a well-known problem, and various sub-problems are selected from the literature because they were successively enlarging the design and schedule policies: single machine or multiple machines (S or M) in each stage;and single product campaigns or multiple products campaigns (S PC or M PC). In this paper, four problems are studied (by combinatorics: SS, MS, SM, and MM) and it is shown that they are all NP-hard in strong sense through polynomial reduction. This study can support innovative algorithms and methodologies for solving DSCBP problems, in a way to improve equipments sizing and configurations design, and thereby contributing to curb disruptions within pharmaceutical supply chains (PharmSC). (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Computational complexity design and scheduling Batch processes Algorithms Pharmaceutical supply chains Medicines shortages

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design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation

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COMPUTERS & CHEMICAL ENGINEERING 2001年第4-6期25卷 665-674页

作者： Lin, X Floudas, CA Princeton Univ Dept Chem Engn Princeton NJ 08544 USA

design, synthesis and scheduling issues are considered simultaneously for multipurpose batch plants. An earlier proposed continuous-time formulation for scheduling is extended to incorporate design and synthesis. Processing recipes are represented by the State-Task Network (STN). The superstructure of all possible plant designs is constructed according to the potential availability of all processing/storage units. The proposed model takes into account the trade-offs between capital costs, revenues and operational flexibility. Computational studies are presented to illustrate the effectiveness of the proposed formulation. Both linear and nonlinear models are included, resulting in MILP and mixed-integer nonlinear programming (MINLP) problems. respectively. The MILP problems are solved using a branch and bound method. Globally optimal solutions are obtained for the nonconvex MINLP problems based on a key property that arises due to the special structure of the resulting models. Comparisons with earlier approaches are also presented. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： design and scheduling multipurpose batch process continuous-time formulation MILP MINLP

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Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

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COMPUTERS & CHEMICAL ENGINEERING 2004年第11期28卷 2109-2129页

作者： Floudas, CA Lin, XX Princeton Univ Dept Chem Engn Sch Engn Appl Sci Princeton NJ 08544 USA

An overview of developments in the scheduling of multiproduct/multipurpose batch and continuous processes is presented. Existing approaches are classified based on the time representation and important characteristics of chemical processes that pose challenges to the scheduling problem are discussed. In contrast to the discrete-time approaches, various continuous-time models have been proposed in the literature and their strengths and limitations are examined. Computational studies and applications are presented. The important issues of incorporating scheduling at the design stage and scheduling under uncertainty are also reviewed. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： process scheduling multiproduct plants multipurpose plants discrete-time model continuous-time model mixed-integer linear programming (MILP) mixed-integer nonlinear programming (MINLP) design and scheduling uncertainty reactive scheduling

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Advances in the Optimization of Energy Systems and Machine Learning Hyperparameters

Advances in the Optimization of Energy Systems and Machine L...

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作者： Tso, William Weikang Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

Intensifying public concern about climate change risks has accelerated the push for more tangible action in the transition toward low-carbon or carbon-neutral energy. Concurrently, the energy industry is also undergoing a digital transformation with the explosion in available data and computational power. To address these challenges, systematic decision-making strategies are necessary to analyze the vast array of technology options and information sources while navigating this energy transition. In this work, mathematical optimization is utilized to answer some of the outstanding issues around designing cleaner processes from resources such as natural gas and renewables, operating the logistics of these energy systems, and statistical modeling from data. First, exploiting natural gas to produce lower emission liquid transportation fuels is investigated through an optimization-based process synthesis. This extends previous studies by incorporating chemical looping as an alternative syngas production method for the first time. Second, a similar process synthesis approach is implemented for the optimal design of a novel biomass-based process that coproduces ammonia and methanol, improving their production flexibility and profit margins. Next, operational difficulties with solar and wind energies due to their temporal intermittency and uneven geographical distribution are tackled with a supply chain optimization model and a clustering decomposition algorithm. The former describes power generation through energy carriers (hydrogen-rich chemicals) connecting resource-dense rural areas to resource-deficient urban centers. Results show the potential of energy carriers for long-term storage. The latter is developed to identify the appropriate number of representative time periods for approximating an optimization problem with time series data, instead of using a full time horizon. This algorithm is applied to the simultaneous design and scheduling of a renewable power system

关键词： Process systems engineering Mathematical optimization Process synthesis Capacity expansion Unit commitment design and scheduling Supply chain optimization Parametric programming Hyperparameter tuning Natural gas Niomass Solar Wind Energy storage Battery Hydrogen Ammonia Methanol Liquid transportation fuels Chemical looping

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