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Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures

DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Karia, Tanuj Chaparro, Gustavo Chachuat, Benoit Adjiman, Claire S. Imperial Coll London Inst Mol Sci & Engn Sargent Ctr Proc Syst Engn Dept Chem Engn London SW7 2AZ England

The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.

关键词： Computer-aided mixture/blend design Phase stability mixed-integer nonlinear programming Surrogate-based optimisation Classifier Artificial neural network

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Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷 100218页

作者： Nikkhah, Hasan Aghayev, Zahir Shahbazi, Amir Charitopoulos, Vassilis M. Avraamidou, Styliani Beykal, Burcu Univ Connecticut Dept Chem & Biomol Engn Storrs CT 06269 USA Univ Connecticut Ctr Clean Energy Engn Storrs CT 06269 USA UCL Sargent Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level mixed-integer nonlinear problems (DOMINO) framework, a data- driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results ind

关键词： Data-driven optimization Integrated planning and scheduling Bi-level programming mixed-integer nonlinear programming

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Dynamic adjustment strategy of electric bus operations: A spatial branch-and-bound method with acceleration techniques

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 171卷

作者： Yuan, Yin Li, Shukai D'Ariano, Andrea Bosi, Tommaso Yang, Lixing Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Roma Tre Univ Dept Civil Comp Sci & Aeronaut Technol Engn I-00146 Rome Italy

Electric bus systems frequently encounter operational instability, resulting in delays, bunching and disturbed charging schemes. Advanced technologies like sensoring and wireless connectivity strongly support dynamic adjustments to improve the stability of electric bus systems, fostering flexibility responsiveness to changing operating conditions. This article explores the dynamic adjustment problem of electric bus operations to jointly generate bus charging schemes and timetable adjustments in a real-time decision-making process. We propose a mixed- integer nonlinear programming model for each decision stage, explicitly considering factors such as vehicle overtaking, passenger load, capacity limitations, and charging behaviors. To efficiently solve this problem, we design a spatial branch-and-bound method with multiple acceleration techniques of logical inference for bound contraction, bilinear-specific branching, and parallel computation. Indeed, the original mixed-integer nonlinear programming problem can be split into a series of mixed-integer quadratic programming problems with reduced domains. The acceleration techniques proposed could be easily customized to address other mixed-integer nonlinear programs in such branch-and-bound-based schemes. Computational experiments validate the effectiveness of the proposed adjustment strategy yielding feasible solutions that enhance headway regularity, energy savings and service quality. Additionally, our solution method efficiently tackles real-world scenarios of significant complexity, with up to 12 vehicles running 8 loops on a route comprising 54 stops, with an average of 5.11-seconds computational time, outperforming both the common commercial solver and standard spatial branch-and-bound approaches in computational speed.

关键词： Electric bus system mixed-integer nonlinear programming Acceleration techniques

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Superstructure-based optimization of membrane network systems for multicomponent liquid mixture separation

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JOURNAL OF MEMBRANE SCIENCE 2025年 717卷

作者： Verma, Harshit Jassby, David Maravelias, Christos T. Princeton Univ Dept Chem & Biol Engn 50 Olden St Princeton NJ 08540 USA Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08540 USA

The design of membrane networks to recover chemical components from liquid mixtures is intricate due to the large number of potential network configurations. Additionally, nonidealities present in liquid mixtures pose difficulties in describing membrane permeators. In this work, we present a framework for membrane network synthesis to recover valuable components from liquid mixtures using mixed-integer nonlinear programming (MINLP). First, to model membrane permeation, we develop a physics-based nonlinear surrogate model for crossflow membranes. Second, we construct a richly connected superstructure to account for all potential membrane network configurations. Third, we demonstrate how the two aforementioned elements can be integrated into a MINLP model to obtain the optimal membrane network configuration. Finally, through various applications, we illustrate how the proposed approach can achieve globally optimal solutions.

关键词： Separation network synthesis mixed-integer nonlinear programming Global optimization

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Optimal design of hydrogen-blended natural gas pipeline network considering separation systems

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AICHE JOURNAL 2025年第2期71卷

作者： Gu, Shiya Bai, Yunhai Dong, Yachao Du, Jian Dalian Univ Technol Inst Chem Proc Syst Engn Sch Chem Engn Dalian 116024 Liaoning Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Drilling & Prod Technol Res Inst Xian Shaanxi Peoples R China Natl Engn Lab Explorat & Dev Low Permeabil Oil & G Xian Shaanxi Peoples R China

Blending hydrogen into existing natural gas pipelines is considered the most feasible choice for long-distance, large-scale hydrogen transportation in the early stage of hydrogen economy development. To integrate the optimization of hydrogen-blended natural gas pipeline network and subsequent hydrogen/natural gas separation process, this article presents a mixed-integer nonlinear programming model, aiming to minimize the total annual project net cost. To tackle the computational complexity resulting from the large-scale and nonlinear nature of practical design problems, a decomposition algorithm is tailored to the proposed model. Two case studies demonstrate that compared to stepwise model, the proposed pipeline-separation integrated model offers economic benefits and practical value, incorporating separation processes and satisfying constraints of hydrogen demand, pressure and blending ratio requirements, which achieves an economically optimal design for both pipeline transportation and separation systems, and provides a viable solution for the broader application of hydrogen-blended natural gas networks.

关键词： hydrogen infrastructure design methane-hydrogen separation mixed-integer nonlinear programming pipeline network superstructure-based optimization

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Handling Multiple-Fuel Options in Economic Dispatch of Thermal Power Plants Through a Tight Model Applying Indicator Variables

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Sharifzadeh, Hossein Hakim Sabzevari Univ Dept Elect & Comp Engn Sabzevar Iran

Thermal power plants play a central role in present power systems because of their high efficiency, fast startup capability, and flexibility to integrate the variability of renewable generations. These thermal units can utilize various fuels, including coal, natural gas, and oil, which enhances both the economic performance and security of the overall energy system. Representing the fuel cost functions of these units with multiple fuels (MFs) as binary decisions involves adding binary variables to the economic dispatch (ED) problem. The inclusion of these binary variables and nonlinear cost functions results in a complex NP-hard mixed-integer nonlinear programming (MINLP). This paper provides another perspective on the ED, where indicator variables represent the MF options and determine which cost functions should be set to zero. Based on this perspective, the paper builds a tight model to handle the indicator variables and solve the MINLP ED. Moreover, the paper introduces an iterative solution method with a bound-tightening technique to speed up the solution process. We conducted experimental studies using eight ED case studies with MF options involving up to 1280 generating units. The optimal costs obtained from these case studies demonstrate the effectiveness of the tight model and the iterative solution method for solving the MINLP ED problem. Furthermore, the proposed approach generally outperforms earlier algorithms in terms of solution quality and robustness. Finally, the tight model can speed up the solution process by 18%-45% compared with the standard formulation in the adopted case studies.

关键词： bound tightening economic dispatch indicator variables mixed-integer nonlinear programming multiple-fuel option tight formulation valve point effect

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Optimization of container ship sailing speed considering carbon emissions policies and cargo time value

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ENGINEERING OPTIMIZATION 2025年

作者： Xing, Yuwei Wu, Zerong Yang, Hualong Zheng, Jianfeng Liu, Baoli Dalian Maritime Univ Transportat Engn Coll Dalian Peoples R China

This article addresses the container ship sailing speed optimization problem considering carbon emissions policies and cargo time value. A mixed-integer nonlinear programming model is formulated to minimize total cost. For consistent sailing speeds across different legs of the voyage, a novel analytical solution is introduced. For scenarios where sailing speeds vary, a discretization method is proposed to transform the nonlinear model into an integer linear one, which can be solved by CPLEX. The proposed model is validated via a numerical experiment. The findings indicate that fluctuations in the unit time value of container cargo exert a more significant influence on the average container ship sailing speed than factors associated with carbon emissions policies. Sensitivity analysis reveals that the cap-and-trade policy is more effective than the carbon tax policy in reducing carbon dioxide emissions. These insights are valuable for liner shipping companies to achieve a balance between environmental sustainability and customer satisfaction.

关键词： Container ship sailing speed cap-and-trade policy carbon tax policy cargo time value mixed-integer nonlinear programming

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Benefit Maximizing Network Design in the public sector

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COMPUTERS & OPERATIONS RESEARCH 2025年 178卷

作者： Aboolian, Robert Karimi, Majid Calif State Univ San Marcos Coll Business Adm Dept Operat & Supply Chain Management 333 S Twin Oaks Valley Rd San Marcos CA 92096 USA

This paper introduces the Benefit Maximization Network Design Problem (BNDP), an innovative framework aimed at optimizing public service systems. By integrating consumer choice models with a focus on minimizing consumer disutility, the BNDP strategically maximizes societal welfare by optimizing the net benefits derived from service utilization, carefully weighing the costs of service capacity against the value delivered to the public. An essential aspect of this study is the development of an epsilon-optimal solution approach, which effectively addresses the intricate optimization challenges inherent in public service system design, including nonlinear equilibrium conditions. This approach efficiently identifies the optimal number, locations, and capacities for service facilities, ensuring practical and effective solutions despite the model's complexity. A practical application of the BNDP is demonstrated through a detailed case study on preventive healthcare in San Diego, showcasing the model's ability to refine operational solutions and significantly enhance societal benefits. The results underscore the importance of prioritizing benefit maximization over simplistic capacity thresholds and participation maximization, leading to either higher levels of consumer participation while using optimal levels of resources or comparable levels of customer participation with significantly fewer resources and reasonable utilization rates. Furthermore, the paper explores the model's applicability in the private sector to maximize corporate profits, highlighting its versatility. The study concludes by advocating for policymakers to prioritize the use of BNDP in establishing optimal budgets for public agencies rather than allocating resources based on exogenous capacities.

关键词： Location models Service system design Elastic demand Congestion Stochastic demand User-choice mixed-integer nonlinear programming Public sector

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A bi-level, many-objective stochastic optimization model for handling simultaneous uncertainties in the operation of multi-carrier integrated energy systems: A hospital case study

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Sustainable Cities and Society 2025年 127卷

作者： Mohammad Kiani-Moghaddam Mohsen N. Soltani Philip D. Weinsier Ahmad Arabkoohsar Department of Energy Aalborg University Esbjerg Denmark Department of Applied Electrical Engineering Bowling Green State University Firelands Huron OH USA Department of Civil and Mechanical Engineering Technical University of Denmark Copenhagen Denmark

The operating multi-carrier integrated energy systems (MIESs) at the building level face challenges from multiple deep uncertainties related to resources, demands, and market prices. These uncertainties complicate decision-making and affect system reliability, cost-efficiency, and sustainability. Tackling these challenges requires advanced optimization models capable of handling many simultaneous uncertainties. This study addresses this need by developing a bi-level, many-objective stochastic optimization model. The upper-level optimization applies information-gap decision theory to evaluate risk-averse and risk-seeking decision policies, ensuring the system’s adaptability under many uncertain parameters. This level uses a non-dominated sorting genetic algorithm III to solve the many-objective optimization problem, generating a many-dimensional Pareto efficient solution set. It then utilizes a hybrid decision-making tool that combines the fuzzy satisfying method with a conservative approach to select the final optimal solution from this set. The lower-level optimization formulates a mixed-integer nonlinear programming problem to minimize the costs of energy, emissions, and multi-carrier energy not supplied while maintaining technical constraints. The model validates its effectiveness across diverse real-world urban settings, including hospital and industrial applications, demonstrating its ability to generate robust operational strategies for MIESs. The results confirm that the proposed model outperforms conventional methods by providing efficient operational strategies for MIESs under uncertainties.

关键词： Bi-level optimization Information-gap decision theory Many-objective optimization mixed-integer nonlinear programming Multi-carrier integrated energy systems Non-dominated sorting genetic algorithm III

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Recipe Determination and Scheduling of Gasoline Blending Operations

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AICHE JOURNAL 2010年第2期56卷 441-465页

作者： Li, Jie Karimi, I. A. Srinivasan, Rajagopalan Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117576 Singapore Inst Chem & Engn Sci Singapore 627833 Singapore

Gasoline is a major contributor to the profit of a refinery. Scheduling gasoline-blending operations is a critical and complex routine task involving tank allocation, component mixing, blending, product storage, and order delivery. Optimized schedules can maximize profit by avoiding ship demurrage, improving order delivery, minimizing quality give-aways, avoiding costly transitions and slop generation, and reducing inventory costs. However, the blending recipe and scheduling decisions make this problem a nonconvex mixed-integer nonlinear program (MINLP). In this article, we develop a slot-based MILP formulation for an integrated treatment of recipe, specifications, blending, and storage and incorporate many real-life features such as multipurpose product tanks, parallel nonidentical blenders, minimum run lengths, changeovers, piecewise constant profiles for blend component qualities and feed rates, etc. To ensure constant blending rates during a run, we develop a novel and efficient procedure that solves successive MILPs instead of a nonconvex MINLP. We use 14 examples with varying sizes and features to illustrate the superiority and effectiveness of our formulation and solution approach. The results show that our solution approach is superior to commercial solvers (BARON and DICOPT). (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 441-465, 2010

关键词： gasoline recipe blending mixed-integer nonlinear programming nonconvex property index

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