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Enabling the direct solution of challenging computer-aided molecular and process design problems: Chemical absorption of carbon dioxide

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COMPUTERS & CHEMICAL ENGINEERING 2023年 174卷

作者： Lee, Ye Seol Galindo, Amparo Jackson, George Adjiman, Claire S. Imperial Coll London Inst Mol Sci & Engn Sargent Ctr Proc Syst Engn Dept Chem Engn London SW7 2AZ England

The search for improved CO2 capture solvents can be accelerated by deploying computer-aided molecular and process design (CAMPD) techniques to explore large molecular and process domains systematically. However, the direct solution of the integrated molecular-process design problem is very challenging as nonlinear interactions between physical properties and process performance render a large proportion of the search space infeasible. We develop a methodology that enables the direct and reliable solution of CAMPD for absorption- desorption processes, using the state-of-the-art SAFT-gamma Mie group contribution approach to predict phase and chemical equilibria. We develop new feasibility tests and show them to be highly efficient at reducing the search space, integrating them in an outer-approximation algorithm. The framework is applied to design an aqueous solvent and CO2 chemical absorption-desorption process, with 150 CAMPD instances across three case studies solved successfully. The optimal solvents are more promising than those obtained with sequential molecular design approaches.

关键词： Computer-aided molecular and process design mixed-integer nonlinear programming Amine solvent design CO2 chemical absorption process SAFT-gamma Mie

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Delivery drone route planning over a battery swapping network

Delivery drone route planning over a battery swapping networ...

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12th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Emerging Data and Industry 4.0 (EDI40)

作者： Cokyasar, Taner Argonne Natl Lab Energy Syst Div 9700 S Cass Ave Lemont IL 60439 USA

Many enterprises invest on drone delivery research and development to drop off packages at consumers' doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Drone delivery drone routing drone battery swapping mixed-integer nonlinear programming optimization

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mixed-integer Optimization for Bio-Inspired Robust Power Network Design 52

Mixed-Integer Optimization for Bio-Inspired Robust Power Net...

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52nd North American Power Symposium (NAPS)

作者： Huang, Hao Panyam, Varuneswara Narimani, Mohammad Rasoul Layton, Astrid Davis, Katherine R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

ISBN: (纸本)9781728181929

Power systems are susceptible to natural threats including hurricanes and floods. Modern power grids are also increasingly threatened by cyber attacks. Existing approaches that help improve power system security and resilience may not be sufficient;this is evidenced by the continued challenge to supply energy to all customers during severe events. This paper presents an approach to address this challenge through bio-inspired power system network design to improve system reliability and resilience against disturbances. Inspired by naturally robust ecosystems, this paper considers the optimal ecological robustness that recognizes a unique balance between pathway efficiency and redundancy to ensure the survivability against disruptive events for given networks. This paper presents an approach that maximizes ecological robustness in transmission network design by formulating a mixed-integer nonlinear programming optimization problem with power system constraints. The results show the increase of the optimized power system's robustness and the improved reliability with less violations under N - x contingencies.

关键词： Power Networks Design mixed-integer nonlinear programming Power System Resilience

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Adjustable Robust Optimization for the Multi-period Planning Operations of an Integrated Refinery-Petrochemical Site under Uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2022年 160卷 107703-107703页

作者： Zhang, Lifeng Yuan, Zhihong Chen, Bingzhen Tsinghua Univ Dept Chem Engn State Key Lab Chem Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China

This paper concentrates on the formulation of a large-scale nonconvex mixed-integer nonlinear programming model and the application of robust optimization for the multi-period operational planning of realworld integrated refinery-petrochemical site in China under uncertain product demands and crude oil price. To avoid excessive conservativeness resulting from classical static robust optimization, an adjustable robust counterpart incorporating resource decisions via an affinely adjustable linear decision rule is first derived. On the basis of a proposed polyhedral dynamic uncertainty set that mimics the dynamic behavior of the product demand over time, an adjustable robust counterpart with a dynamic uncertainty set is further formulated. Classical static robust optimization, adjustable robust optimization, and adjustable robust optimization with dynamic uncertainty sets are systematically compared for case studies. The results clearly illustrate the advantages of the affinely adjustable robust optimization with a dynamic uncertainty set over the classic robust optimization in decision making.

关键词： mixed-integer nonlinear programming Adjustable robust optimization Dynamic uncertainty set Planning operations Refinery-petrochemical

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Optimal computer-aided molecular design of ionic liquid mixtures for post-combustion carbon dioxide capture

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COMPUTERS & CHEMICAL ENGINEERING 2022年 157卷 107622-107622页

作者： Silva-Beard, Andrea Flores-Tlacuahuac, Antonio Rivera-Toledo, Martin Univ Iberoamer Ciudad Mexico Dept Ingn Quim Ind & Alimentos Prolongac Paseo Reforma 880 Ciudad De Mexico 01219 Mexico Tecnol Monterrey Escuela Ingn & Ciencias Campus MonterreyAve Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico

This work addresses the post-combustion carbon dioxide capture capabilities of optimally designed mixtures of ionic liquids, as well as pure ionic liquids. We postulate that ionic liquid mixtures can display better absorption rates and processing features when compared with pure ionic liquids. The computer-aided molecular design of the ionic liquids mixture was posed as a mixed-integer nonlinear programming problem to determine both the chemical structure and the amount of each ionic liquid in the mixture. Also, multiobjective optimization was performed to determine optimal operating conditions, specifically pressure and ionic liquids mixture feed stream, for an absorption separation process with various optimal mixtures. Results show that the anion [Tf2N] with the chain -(CH)(2)(CH2)(5)(CH3)(3) was present both in the pure ionic liquid case and in the mixtures of these compounds. Results also indicate that, even though the amount of carbon dioxide absorbed by pure ionic liquid is slightly higher than the absorbed by ionic liquids mixture, there are important operational and environmental advantages of using such type of mixtures since they have milder physical properties than the pure ionic liquid. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Multiobjective optimization Ionic liquid mixture Carbon dioxide capture mixed-integer nonlinear programming Computer-aided molecular design

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Novel general active reliability redundancy allocation problems and algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第PartA期218卷 108167-108167页

作者： Yeh, Wei-Chang Zhu, Wenbo Tan, Shi-Yi Wang, Gai-Ge Yeh, Yuan-Hui Natl Tsing Hua Univ Dept Ind Engn & Engn Management Integrat & Collaborat Lab Hsinchu Taiwan Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Ocean Univ China Dept Comp Sci Qingdao Peoples R China Univ New South Wales UNSW Business Sch Sydney NSW Australia

The traditional (active) reliability redundancy allocation problem (RRAP) is used to maximize system reliability by determining the redundancy and reliability variables in each subsystem to satisfy the volume, cost, and weight constraints. The RRAP structure is very simple, that is, redundant components are parallel in each subsystem, and all subsystems are either connected in series or in a bridge network. Owing to its important and practical applications, a novel RRAP, called the general RRAP (GRRAP), is proposed to extend the series-parallel structure or bridge network to a more general network structure. To solve the proposed novel GRRAP, a new algorithm, called the BAT-SSOA3, used the simplified swarm optimization (SSO) to update solutions, the small-sampling triobjective orthogonal array (SS3OA) to tune the parameters in the proposed algorithm, the binary-addition-tree algorithm (BAT) to calculate the fitness (i.e., reliability) of each solution, and the penalty function to force infeasible back to the feasible region. To validate the proposed algorithm, the BAT-SSOA3 is compared with the boundary SSO (BSO), which is the best-known SSO-based algorithm for the RRAP, via designed experiments and computational studies.

关键词： Reliability Redundancy Allocation Problems (RRAPs) Simplified Swarm Optimization (SSO) Binary-Addition-Tree Algorithm (BAT) Small Sampling Orthogonal Array (OA) mixed-integer nonlinear programming

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Coordinated operation of electricity and gas-hydrogen systems with transient gas flow and hydrogen concentration tracking

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期211卷

作者： Mhanna, Sleiman Saedi, Isam Mancarella, Pierluigi Zhang, Zihang Univ Melbourne Dept Elect & Elect Engn Melbourne 3010 Australia Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England

This paper addresses the problem of coordinating the operation of electricity and natural gas (NG) transmission systems with green hydrogen (H2) production and injection into existing NG networks. In particular, the operation of the two systems consists of a two-stage optimization framework that solves a network-constrained unit commitment (UC) problem with transmission power losses to obtain the profiles of gas energy demands from gas-powered generators and the maximum allowable H2 injection flow rates from power-to-gas (PtG), which are then used as inputs to an optimal transient NG-H2 flow problem with H2 concentration tracking. The nonlinearities introduced by the discretization of the H2 concentration tracking equations are particularly challenging to solve using second-order nonlinear programming (NLP) methods. Moreover, the nonlinear constraints capturing transmission line losses make the electricity operational problem intractable if solved with mixed-integer NLP methods. Therefore, this work leverages the reliability and scalability of linear programming (LP) by designing two novel and distinct sequential LP (SLP) methods that exploit the particular structures of the two problems to find feasible, possibly optimal solutions, using only first-order information. The algorithmic framework is demonstrated on the IEEE 24-bus RTS connected to the 22-node Belgian gas network. This paper is the first to demonstrate H2 concentration tracking under transient gas flow in a multi-energy optimization framework on a realistic gas transmission network with multiple H2 injection locations.

关键词： Transient gas flow Concentration tracking Green hydrogen Sequential linear programming Unit commitment Integrated modeling mixed-integer nonlinear programming

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Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期139卷

作者： Pang, Bizhao Low, Kin Huat Lv, Chen Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Nanyang Technol Univ Air Traff Management Res Inst Singapore 637460 Singapore

The increasing unmanned aircraft system (UAS) applications in urban environments pose challenges for safe and efficient low altitude air traffic management. As an essential enabler to meet these challenges, pre-flight 4D routes optimization is required to conduct conflict detection and resolution (CD&R) and to generate conflict-free flight routes before departure. Existing studies on strategic deconfliction cover several types of strategies such as scheduling or rerouting. However, a single type of strategy used to solve different types of conflicts may lead to an unsafe and inefficient way of conflict resolution. This paper proposes an adaptive decision-making framework to optimize the resolution strategies used for different types of conflicts with explainable mechanisms. The proposed framework is formulated as a double-layer optimization problem with the considerations of scheduling, speed adjustment, and rerouting strategies for conflict resolution. The first layer of the framework is established as a probabilistic selection model to make decisions on which strategy should be selected for what type of conflict. The second layer is developed as a mixed-integer nonlinear programming (MINLP) model to optimize the decision variables of the strategies selected by the first layer. To solve the proposed double-layer optimization problem, we introduce and improve a novel meta-heuristic stochastic fractal search (SFS) algorithm with two major improvements of a penalty-guided fitness function and an exploitation-exploration balancing scheme. Simulation results demonstrate that the proposed adaptive conflict resolution framework successfully optimizes the strategies used for each type of flight conflict, which subsequently optimizes the 4D routes with significant reductions in total operational cost, number of flight conflicts, and flight delays. The improved stochastic fractal search (ISFS) algorithm is also proved effective and reliable in solving the proposed optimizati

关键词： Unmanned aircraft system Conflict resolution mixed-integer nonlinear programming Third-party risk Urban environments

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A modified and extended genetic algorithm for optimal distributed generation grid-integration solutions in direct current power grids

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Zuluaga-Ríos, Carlos D. Institute for Research in Technology Universidad Pontificia Comillas Madrid Spain

The integration of distributed generation into direct current power grids presents a critical challenge in modern energy systems, as it directly impacts grid reliability, efficiency, and the successful transition to renewable energy. This study addresses the problem of optimizing distributed generation placement and sizing in direct current grids, a key issue for reducing power losses and improving energy distribution. To tackle this, a modified and extended genetic algorithm was developed, capable of handling both continuous and discrete variables simultaneously. The algorithm was tested on two standard direct current grid systems, a 21-bus microgrid and a 69-bus network. The results demonstrated significant improvements over existing methods, reducing power losses by 84.5% in the 21-bus microgrid and by 95% in the 69-bus direct current network, with notably reduced computation times. These findings indicate that the proposed algorithm not only optimizes distributed generation integration effectively but also offers superior performance compared to traditional approaches, without the need for additional methods or software. The novelty of this work lies in its ability to handle complex, nonlinear optimization problems within direct current grids using a single, efficient approach, advancing beyond previous efforts by achieving better results with fewer computational resources. © 2024 The Author

关键词： Direct current grids Genetic algorithms Meta-heuristic optimization methods mixed-integer nonlinear programming

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The berth assignment and allocation problem considering cooperative liner carriers

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年 164卷

作者： Guo, Liming Zheng, Jianfeng Du, Haoming Du, Jian Zhu, Zhihong Dalian Maritime Univ Transportat Engn Coll Dalian Peoples R China Qingdao Univ Sci & Technol Sch Math & Phys Qingdao Peoples R China Dalian Jiaotong Univ Sch Traff & Transportat Engn Dalian Peoples R China

This paper investigates a novel extension of the berth allocation problem (BAP), which mainly determines berths and times for arriving vessels. Most previous studies on the BAP do not consider a cooperative environment among liner carriers. This paper proposes a berth assignment and allocation problem considering cooperative liner carriers (BAAP), which integrates the BAP with the berth assignment problem and the liner clustering problem (LCP). The LCP addresses how to group a number of liner carriers into various liner clusters, and the berth assignment problem aims to assign proper number of berths for serving vessels of various liner clusters. For solving our proposed problem, we develop a two-stage optimization method. In stage I, a mixed-integer nonlinear programming (MINLP) model is constructed to formulate our BAAP with given liner clusters, integrating the BAP with the berth assignment problem. According to the results ob-tained in stage I, a decision model based on cooperative game theory is presented to solve the LCP in stage II. A genetic algorithm framework embedded with a mixed-integer linear programming solver (CPLEX) is designed to solve the MINLP model constructed in stage I. The LCP in stage II can be solved by using the enumeration method. Experimental results are provided to validate the efficiency and effectiveness of our models and algorithm

关键词： Berth assignment Berth allocation Liner clusters mixed-integer nonlinear programming

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