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Design optimization of shell and tube heat exchangers sizing with heat transfer enhancement

COMPUTERS & CHEMICAL ENGINEERING 2020年 137卷 106821-106821页

作者： Yang, Zekun Ma, Yingjie Zhang, Nan Smith, Robin Univ Manchester Ctr Proc Integrat Sch Chem Engn & Analyt Sci Manchester M13 9PL Lancs England

Heat transfer enhancement (HTE) is an efficient technology to improve the performance of shell and tube heat exchangers (STHEs). While there exist many commercial software packages, heat exchanger design still highly depends on users' experiences, resulting in long computing time and inferior solutions. Therefore, a systematic optimization methodology is required in order to achieve an efficient and accurate design solution. This paper presents a generalized disjunctive programming (GDP) model for the optimization of STHE design. It is formulated as a mixed integer non-linear programming (MINLP) problem, involving selection for 12 technology combinations (four tube-side techniques, three shell-side techniques), and all discrete decisions for each selection are modeled by disjunctions. The model is then applied to minimize the total capital cost. Both global optimization solver BARON, and general MINLP solver DICOPT are tested. The results show that the developed method provides a better design solution compared with conventional STHE design procedures. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Mathematical optimization Heat transfer enhancement Shell and tube heat exchanger design generalized disjunctive programming

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Optimization of the supply chain for the production of biomass-based fuels and high-added value products in Mexico

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COMPUTERS & CHEMICAL ENGINEERING 2021年 145卷 107181-107181页

作者： Mercedes Espinoza-Vazquez, Yulissa Israel Gomez-Castro, Fernando Maria Ponce-Ortega, Jose Univ Guanajuato Dept Ingn Quim Div Ciencias Nat & Exactas Campus GuanajuatoNoria Alta S-N Col Noria Alta Guanajuato 36050 Mexico Univ Michoacana de San Nicolas Hidalgo Fac Ingn Quim Div Estudios Posgrad Francisco J Mugica S-N Morelia 58060 Michoacan Mexico

In Mexico, high quantities of lignocellulosic residues are generated. Such residues can be used to produce either biofuels of other products. In this work, a mathematical model representing the supply chain for the conversion of the lignocellulosic biomass in Mexico into biofuels and high-added value products is presented. The model aims to maximize the profit while satisfying as much as possible of the gasoline demand in the country. Additionally, the effect of the variations on the availability of raw materials and the demand of products over the optimal supply chain is analyzed. According to the results, the highest profit can be obtained if the biomass is used to produce levulinic acid and furfural, satisfying all the demand. If it is desired to satisfy the demand of ethanol or butanol, the profit is reduced. Under the studied conditions, only a fraction of the alcohol demand can be covered. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Supply chain Lignocellulosic biomass generalized disjunctive programming Uncertainty analysis

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Pyosyn: A new framework for conceptual design modeling and optimization

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COMPUTERS & CHEMICAL ENGINEERING 2021年 153卷 107414-107414页

作者： Chen, Qi Liu, Yunshan Seastream, Grant Siirola, John D. Grossmann, Ignacio E. Carnegie Mellon Univ Ctr Adv Proc Decis Making Pittsburgh PA 15213 USA Sandia Natl Labs Albuquerque NM 87123 USA

We present Pyosyn, an open-source framework for systematic superstructure-based process synthesis, including a new representation, superstructure generation approaches, modeling, and solution strategies. The new Pyosyn Graph (PSG) representation consists of units, ports, and streams, and includes support for nested units, including new "single-choice" units and modular superstructure construction. We introduce superstructure generation strategies based on both library-assisted and direct-hierarchical means-ends analysis. For the library-assisted approach, we describe generalized port annotations that describe conditions for compatibility between connected unit ports. We extend literature methods to present seven screening rules based on new material port annotations that categorize process chemical species as primary , secondary , or residual . We then describe high-level mathematical modeling of PSG representation elements using *** and ***, including the automated handling of special cases. We also introduce the use of tailored logic-based decomposition algorithms to address "zero-flow" singularities characteristic of synthesis problems. Finally, we demonstrate the flexible use of Pyosyn tools on a set of diverse case studies. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Conceptual design Mathematical programming generalized disjunctive programming Process synthesis Open source

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An integration of algal biofuel production planning, scheduling, and order-based inventory distribution control systems

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BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR 2019年第4期13卷 920-935页

作者： Nugroho, Yohanes K. Zhu, Liandong Wuhan Univ Sch Resource & Environm Sci 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Hubei Biomass Resource Chem & Environm Biotechnol 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

This paper studies an integration model of production planning, scheduling, and order- based inventory distribution control systems. It models supply-chain and production dynamics and employs process control to supply-chain operations and the production process and scheduling. It embeds generalized disjunctive programming (GDP) and branch-and-reduce algorithms to obtain an optimal solution. generalized disjunctive programming reformulates the original mixed integer nonlinear programming (MINLP) model and solves the new model with a lower number of integer variables. Afterwards, the branch and reduce algorithm, which is strengthened with outer approximation (OA) cutting planes, is able to find optimal solutions with permissible optimality gaps. The results show that supply network size does not affect forecast accuracy;there is a forecast error rate of 12.9%. Similar figures are also shown for the inventory level, which is expected to be 32% of demand level, production changeover time at 0.58 days, inventory ramp-up period to meet the expected raw materials and final products inventory at 0.02 and 0.04 days respectively, and extra delivery time to meet a certain level of demand change at 1.1 days. The results show that the model is capable of controlling demand forecast, inventory level, and delivery quantity, so that the supply chain can avoid stock out at a minimal level of inventory, regardless of the size of supply-chain networks. (c) 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

关键词： production planning scheduling process control branch and reduce generalized disjunctive programming mixed integer non-linear programming

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Continuous-time scheduling formulation for multipurpose batch plants

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AICHE JOURNAL 2020年第2期66卷 e16804-e16804页

作者： Mostafaei, Hossein Harjunkoski, Iiro Aalto Univ Sch Chem Engn Dept Chem & Met Engn Espoo Finland ABB Corp Res Germany Wallstadter Str 59 D-68526 Ladenburg Germany

Short-term scheduling of batch processes is a complex combinatorial problem with remarkable impact on the total revenue of chemical plants. It consists of the optimal allocation of limited resources to tasks over time in order to manufacture final products following given batch recipes. This article addresses the short-term scheduling of multipurpose batch plants, using a mixed integer linear programming formulation based on the state-task network representation. It employs both single-grid and multi-grid continuous-time representations, derived from generalized disjunctive programming. In comparison to other multigrid scheduling models in the literature, the proposed multi-grid model uses no big-M constraints and leads to more compact mathematical models with strong linear relaxations, which often results in shorter computational times. The single-grid counterpart of the formulation is not as favorable, as it leads to weaker linear relaxations than the multi-grid approach and is not capable of handling changeover time constraints.

关键词： batch plant generalized disjunctive programming MILP scheduling state-task network

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Rigorous design of reaction-separation processes using disjunctive programming models

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COMPUTERS & CHEMICAL ENGINEERING 2018年 111卷 16-26页

作者： Zhang, Xiang Song, Zhen Zhou, Teng Max Planck Inst Dynam Complex Tech Syst Proc Syst Engn Sandtorstr 1 D-39106 Magdeburg Germany Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

A systematic and efficient method for the rigorous design of complex chemical processes is significant in the chemical industry. In this paper, a superstructure-based optimization approach for the rigorous and simultaneous design of reaction and separation processes using generalized disjunctive programming (GDP) models is presented. In the reactor network, disjunctions for conditional reactors are introduced where the balance and reaction kinetic equations are applied only if the reactor is selected. Based on the proposed reactor disjunctions, two different reactor superstructures are developed and employed. In addition, the GDP representation of distillation columns is used to model the separation network. The reliability and efficiency of the proposed optimization method are demonstrated on two case studies, i.e., one cyclohexane oxidation process and one benzene chlorination process. The flowsheet structure and process-unit operating conditions are simultaneously optimized to minimize the total annual cost of the processes. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Reaction-separation process design Process optimization generalized disjunctive programming Cyclohexane oxidation Benzene chlorination

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Expanding scope and computational challenges in process scheduling

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COMPUTERS & CHEMICAL ENGINEERING 2018年 114卷 14-42页

作者： Castro, Pedro M. Grossmann, Ignacio E. Zhang, Qi Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentals Invest Operat P-1749016 Lisbon Portugal Carnegie Mellon Univ Ctr Adv Proc Decis Making Dept Chem Engn Pittsburgh PA 15213 USA BASF SE D-67063 Ludwigshafen Germany

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review generalized disjunctive programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Planning Scheduling Demand side management Mixed-integer programming generalized disjunctive programming

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Global optimization algorithm for capacitated multi-facility continuous location-allocation problems

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JOURNAL OF GLOBAL OPTIMIZATION 2018年第4期71卷 871-889页

作者： Lara, Cristiana L. Trespalacios, Francisco Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA ExxonMobil Res & Engn Co Corp Strateg Res 1545 Route 22 East Annandale NJ 08801 USA

In this paper we propose a nonlinear generalized disjunctive programming model to optimize the 2-dimensional continuous location and allocation of the potential facilities based on their maximum capacity and the given coordinates of the suppliers and customers. The model belongs to the class of Capacitated Multi-facility Weber Problem. We propose a bilevel decomposition algorithm that iteratively solves a discretized MILP version of the model, and its nonconvex NLP for a fixed selection of discrete variables. Based on the bounding properties of the subproblems, -convergence is proved for this algorithm. We apply the proposed method to random instances varying from 2 suppliers and 2 customers to 40 suppliers and 40 customers, from one type of facility to 3 different types, and from 2 to 32 potential facilities. The results show that the algorithm is more effective at finding global optimal solutions than general purpose global optimization solvers tested.

关键词： Location-allocation problem Weber problem Nonconvex optimization generalized disjunctive programming Mixed-integer nonlinear programming

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Satisfiability modulo theories for process systems engineering

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COMPUTERS & CHEMICAL ENGINEERING 2018年 113卷 98-114页

作者： Mistry, Miten D'Iddio, Andrea Callia Huth, Michael Misener, Ruth Imperial Coll London Dept Comp London SW7 2AZ South Kensingto England

Process systems engineers have long recognized the importance of both logic and optimization for automated decision-making. But modern challenges in process systems engineering could strongly benefit from methodological contributions in computer science. In particular, we propose satisfiability modulo theories (SMT) for process systems engineering applications. We motivate SMT using a series of test beds and show the applicability of SMT algorithms and implementations on (i) two-dimensional bin packing, (ii) model explainers, and (iii) mixed-integer nonlinear optimization solvers. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Satisfiability modulo theories Mixed-integer optimization generalized disjunctive programming Mixed logical-linear programming

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Computer-aided design of optimal environmentally benign solvent-based adhesive products

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COMPUTERS & CHEMICAL ENGINEERING 2019年 130卷 106518-000页

作者： Jonuzaj, Suela Cui, Jingyue Adjiman, Claire S. Imperial Coll London Ctr Proc Syst Engn Dept Chem Engn London SW7 2AZ England

The manufacture of improved adhesive products that meet specified target properties has attracted increasing interest over the last decades. In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised disjunctive programming (GDP), a logic-based framework, to formulate and solve the product design problem. Key design decisions in product design (i.e., how many components should be included in the final product, which active ingredients and solvent compounds should be used and in what proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that number of components in the product formulation does not correlate with performance and that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives (product toxicity and concentration of the active ingredient) is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal ingredients in the product. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Solvent-based adhesives Chemical product design Computer-aided methods generalized disjunctive programming Minimise toxicity

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