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An Enhanced Logic-Based Bender's Decomposition Algorithm with Proximity Principle for Simulator-Based Distillation Process Optimization

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PROCESSES 2025年第4期13卷 977-977页

作者： Tian, Chenshan Zhang, Xiaodong Lan, Yang Sun, Jinsheng Tianjin Univ Technol Sch Chem Engn & Technol Tianjin 300072 Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China

The optimization of distillation processes is particularly challenging due to the presence of nonlinear equations and integer variables, resulting in complex mixed-integer nonlinear programming (MINLP) problems. This work introduces an enhanced optimization algorithm, the logic-based proximity principle Bender's decomposition (LB-PBD), to address non-convex MINLP issues in simulator-based distillation optimization. The key innovation, the proximity principle, improves lower bound predictions by prioritizing information from the closest known integer solutions. Additionally, the integration of a multi-start points strategy and a delayed convergence strategy ensures the algorithm achieves global optimality while avoiding premature convergence. The effectiveness if the proposed LB-PBD is validated through three case studies. Numerical experiments demonstrate so-called proximity principle superior ability of original algorithm to navigate local optima, making LB-PBD more versatile than traditional deterministic algorithm (logic-based outer approximation algorithm) and stochastic algorithm (adaptive superstructure-differential evolution algorithm). In a single-column distillation case, LB-PBD achieves high accuracy. In an extractive distillation case, the algorithm successfully optimizes the separation of a near-azeotropic mixture, reducing energy consumption and improving product recovery compared to previous solutions. These results highlight LB-PBD as a robust and effective tool for solving non-convex MINLLP problems, particularly in simulator-based distillation process optimization.

关键词： distillation optimization mixed-integer nonlinear programming proximity principle Bender's decomposition simulator-based optimization

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Cooperative Ground-Satellite Scheduling and Power Allocation for Urban Air Mobility Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第1期43卷 218-233页

作者： Moon, Hyung-Joo Chae, Chan-Byoung Yonsei Univ Sch Integrated Technol Seoul 03722 South Korea BK21 Grad Program Intelligent Semicond Technol Seoul 03722 South Korea

In this paper, we investigate a multi-user downlink scheduling and power allocation strategy for urban air mobility (UAM) within a 6G non-terrestrial network (NTN) framework that integrates satellite and ground networks. We consider a system model involving multiple ground stations (GSs) and a single satellite, addressing the sum rate maximization problem with link-association, power, elevation angle, and minimum quality-of-service constraints. The proposed method initially segregates satellite-serviced users to reduce interference among the remaining GS-serviced users, taking into account the locations and movements of those UAMs. Subsequently, using a graph-theoretical approach, we convert the GS link association problem into a minimum-cost maximum-flow problem. In this process, we employ an analytical method involving polynomial approximations or a numerical method using integral approximation through the sum of time-sampled parameters. We then address the non-convex power allocation problem for scheduled links through iterative algorithms. The proposed scheduling and power allocation algorithms effectively manage interference in multi-UAM and multi-GS environments, and their performance is validated through extensive simulation results. Our study provides a comprehensive framework and strategy for efficient downlink transmission in future UAM operations, paving the way for novel applications in 6G NTN.

关键词： Satellites Resource management Interference Downlink Quality of service Antenna arrays Autonomous aerial vehicles Urban air mobility non-terrestrial network resource allocation mixed-integer nonlinear programming

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Optimal scheduling of power-to-ammonia systems considering multi-load operations

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Yang, Shuting Yang, Liuyi Zhang, Wei Bi, Kexin Ji, Xu Zhou, Li Dai, Yiyang Chai, Shiyang Dang, Yagu Sichuan Univ Sch Chem Engn Chengdu 610065 Sichuan Peoples R China

Power-to-ammonia (PtA) systems offer several benefits in terms of energy storage and may aid in the transition to net-zero carbon emissions, but the fluctuations and intermittency of renewable energy often induce a temporal and spatial mismatch between the electricity supply and chemical production. This study proposes an optimal scheduling framework by integrating the response surface methodology with mixed-integer nonlinear programming. Explicit formulas describing the Habor-Bosch process are provided in response surface models, which are simple, exhibit high fidelity, and help ensure optimal performance. In the optimization process, capacity is allocated based on the supply and utilization of renewable power, electricity, and hydrogen. This framework departs from traditional single-load production models, effectively minimizing curtailment and addressing the intermittency of renewable energy sources. The results show that multi-load operations achieve a favorable balance between economic benefits (profit of 33.8 million CNY.y(-1)) and renewable energy utilization efficiency (0.4 % curtailment). The industrial case studies considered herein validate the feasibility of PtA systems and support the development of other power-to-X systems.

关键词： Power-to-ammonia Optimal scheduling Response surface methodology mixed-integer nonlinear programming Multi-load working conditions

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Generalized optimization framework for synthesis of thermally coupled distillation columns

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AICHE JOURNAL 2025年第0期71卷

作者： Liu, Chao Ma, Yingjie Li, Jie Univ Manchester Ctr Proc Integrat Sch Engn Dept Chem Engn Manchester M13 9PL England MIT Dept Chem Engn Cambridge MA USA

In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation-oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero-flow issues. The synthesis problem is formulated as a mixed integer nonlinear programming problem, which is optimized using our previously developed Feasible Path-Based Branch and Bound method, coupled with an improved Sequential Quadratic programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%.

关键词： branch and bound complex distillation system mixed-integer nonlinear programming process synthesis thermally coupled distillation

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Global optimization of mixed-integer nonlinear programs with SCIP 8

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第2期91卷 287-310页

作者： Bestuzheva, Ksenia Chmiela, Antonia Mueller, Benjamin Serrano, Felipe Vigerske, Stefan Wegscheider, Fabian Zuse Inst Berlin Dept AIS2T Berlin Germany GAMS Software GmbH Zuse Inst Berlin Berlin Germany

For over 10 years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version 8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided.

关键词： Global optimization mixed-integer nonlinear programming SCIP Branch-and-cut Optimization software Benchmark

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Multi-objective surrogate optimization of mixed-variable IPM motor design problem with ADMM-based approach

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OPTIMIZATION AND ENGINEERING 2025年 1-26页

作者： Asanuma, Tatsuya Kanno, Yoshihiro Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Hongo 7-3-1 Tokyo 1138656 Japan JMAG Business Co JSOL Corp Kudan Kaikan TerraceKudan Minami 1-6-5 Tokyo 1020074 Japan

Designing electric motors boils down to solving multi-objective optimization problems where we look for design variables satisfying multiple requirements on, e.g., torque and electromagnetic losses. To design high-performance motors without restriction by existing design ideas, it is necessary to take the design space as wide as possible at the initial design stage. When dealing with the extensive design space, it is required to treat the basic structure parameters of motors and the detailed dimensions as design variables. For an interior permanent magnet (IPM) motor, which is one of the typical motor types, we need to handle discrete integer design variables such as the number of slots and the coil pitch in addition to the continuous ones. However, it is difficult to obtain the discrete design variable values without rounding off to the nearest allowed values in the framework of the existing continuous surrogate optimization such as sequential approximation optimization (SAO) with the response surface created from the electromagnetic finite element analysis results. We solve this mixed-variable optimization problem by the heuristic approach based on the alternating direction method of multipliers (ADMM) and show with example design problems that the better results are obtained more efficiently in terms of the number of expensive objective function evaluations compared to taking rounding-off measures.

关键词： Electric motor Multi-objective optimization mixed-integer nonlinear programming Sequential approximation optimization Surrogate-based optimization Radial basis function network

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On the use of overlapping convex hull relaxations to solve nonconvex MINLPs

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第2期91卷 415-436页

作者： Wu, Ouyang Muts, Pavlo Nowak, Ivo Hendrix, Eligius M. T. HAW Hamburg D-20099 Hamburg Germany Univ Malaga Malaga 29016 Spain

We present a novel relaxation for general nonconvex sparse MINLP problems, called overlapping convex hull relaxation (CHR). It is defined by replacing all nonlinear constraint sets by their convex hulls. If the convex hulls are disjunctive, e.g. if the MINLP is block-separable, the CHR is equivalent to the convex hull relaxation obtained by (standard) column generation (CG). The CHR can be used for computing an initial lower bound in the root node of a branch-and-bound algorithm, or for computing a start vector for a local-search-based MINLP heuristic. We describe a dynamic block and column generation (DBCG) MINLP algorithm to generate the CHR by dynamically adding aggregated blocks. The idea of adding aggregated blocks in the CHR is similar to the well-known cutting plane approach. Numerical experiments on nonconvex MINLP instances show that the duality gap can be significantly reduced with the results of CHRs. DBCG is implemented as part of the CG-MINLP framework Decogo, see https://***/en/latest/***.

关键词： Decomposition method Parallel computing Column generation Nonconvex optimization mixed-integer nonlinear programming

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Disaggregated and Distributed Near-Real-Time RIC for Large Scale User-Centric RAN

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 1591-1609页

作者： Amrallah, Amr Tsukamoto, Yu Murakami, Takahide Ikami, Akio Shinbo, Hiroyuki Amano, Yoshiaki KDDI Res Inc Wireless Technol Div Saitama 3568502 Japan

The evolution toward user-centric radio access networks (RANs) necessitates innovative management strategies to handle growing network demands efficiently. This study presents a novel framework for the disaggregated and distributed deployment of near-real-time RAN intelligent controllers (near-RT RICs) tailored for large-scale user-centric networks. By leveraging a mixed-integer nonlinear programming (MINLP) approach, we aim to optimize both control latency and deployment costs, which are critical considerations in next-generation network management. Our proposed divide-and-conquer (D&C) and heuristic-greedy (HG)-based algorithms significantly reduce computational complexity while maintaining near-optimal performance. Simulation results indicated that the distributed framework achieved a 78% reduction in control latency with only 7% increase in deployment cost compared with the traditional centralized approach. Additionally, it achieved 99% reduction in runtime performance with an objective function value that was just 2% lower than the optimal value. These findings confirm the framework's scalability and effectiveness, validating its suitability for operational telecom environments.

关键词： Open RAN Optimization Costs Heuristic algorithms Computer architecture programming Delays 6G mobile communication 3GPP Time factors Cell-free massive MIMO large-scale network mixed-integer nonlinear programming near-real-time RIC O-RAN RAN optimization user-centric RAN

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Design of hybrid reactive-extractive distillation for separation of azeotropic ternary mixture using a systematic optimization framework

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 369卷

作者： Liu, Chao Karagoz, Ali Alqusair, Omar Ma, Yingjie Xiao, Xin Li, Jie Univ Manchester Ctr Proc Integrat Dept Chem Engn Manchester M13 9PL England MIT Dept Chem Engn Cambridge MA 02139 USA Chinese Acad Sci Inst Proc Engn Beijing 100045 Peoples R China Kind Saud Univ Coll Engn Dept Chem Engn Riyadh 11421 Saudi Arabia

Hybrid reactive-extractive distillation (RED) has been considered as a promising approach for the separation of ternary azeotropic mixtures that are widely existing in the chemical industry, due to its high energy efficiency. The optimal design of RED is quite challenging, primarily due to the problem's inherent complexity involving both reactive and extractive distillation processes with recycle streams. Mathematical modeling of RED using rigorous models introduces highly nonlinear and nonconvex terms, which are difficult to optimize. This work proposes a systematic optimization framework to address these challenges by integrating advanced modeling, simulation, and optimization techniques. First, we develop a novel mixed-integer nonlinear programming (MINLP) optimization problem in which the pseudo-transient continuation technique is employed to construct a pseudo-transient continuation reactive distillation model in an equation-oriented environment to represent column sections, condensers, and reboilers. Our previously proposed feasible path-based branch and bound algorithm is then employed to solve this difficult MINLP problem. Given the presence of recycle streams within the process, an open-loop MINLP optimization problem is first solved to achieve an optimal design based on product specifications, followed by a closed-loop nonlinear programming optimization problem for a fixed column structure to reduce the computational effort. The design of triple-column reactive-extractive distillation (TCRED) and double-column reactive-extractive distillation (DCRED) processes in three examples demonstrates that the proposed framework enables efficient RED process design, which can reduce total annualized cost by 3.5% to 25.1% compared to existing studies.

关键词： Reactive distillation Extractive distillation Azeotropic separation Process intensification Feasible path-based algorithm mixed-integer nonlinear programming

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Optimum Operation of Microgrid Considering the Effects of Energy Storage and Load Response Program to Increase Resilience

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第5期38卷 1147-1163页

作者： Valizadeh, M. Mansoor, S. Siyah Sarvenoee, A. Khosravi Kohzadipour, M. Ilam Univ Dept Elect Engn Ilam Iran

Resilience is one of the important issues in power networks. This article analyzes resilience during severe incidents and in normal and critical situations. A mixed-integer nonlinear programming (MINLP) model is presented to investigate the issue. The optimization is done in two levels. In the first level, the optimal size of the energy storage system and its optimal discharge depth are determined for the optimal planning of the microgrid under normal operating conditions. In the second level, after a fault occurs and the microgrid is disconnected from the upstream network and operates in island mode, an energy storage system (ESS) and load response program reduces the system's vulnerability and minimizes the cost of load shedding. This model aims to observe the impact of ESS and demand response programs (DRP) on system performance in numerical conditions and during extreme weather events as a strategy to increase grid resilience. Under normal operating conditions, the return on investment for ESS occurs within one year, and the economic profit of the microgrid with the addition of DRP is about $160,000. Additionally, in critical conditions, the ESS supplies 40% of the loads shut down during the fault, which increases to 66% with the addition of the load response program.

关键词： Resiliency Microgrid Optimal Operation Load Response Program Energy Storage System mixed-integer nonlinear programming Demand Response Program

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