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optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective bayesian optimization and kinetic modeling

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CHEMICAL ENGINEERING SCIENCE 2025年 302卷

作者： Chai, Kejie Xia, Weida Shen, Runqiu Luo, Guihua Cheng, Yingying Su, Weike Su, An Zhejiang Univ Technol Collaborat Innovat Ctr Yangtze River Delta Reg Gre Key Lab Pharmaceut Engn Zhejiang Prov Key Lab Green Pharmaceut Technol Related Equipment Hangzhou 310014 Peoples R China Hangzhou Normal Univ Coll Mat Chem & Chem Engn Hangzhou 311121 Peoples R China Zhejiang Univ Technol Coll Chem Engn State Key Lab Breeding Base Green Chem Synth Techn Key Lab Green Chem Synth Technol Zhejiang Prov Hangzhou 310014 Peoples R China Zhejiang Univ Technol Coll Pharmaceut Sci Hangzhou 310014 Peoples R China Zhejiang Univ Technol Coll Chem Engn Hangzhou 310014 Peoples R China

Heterogeneous continuous flow hydrogenation is important in the chemical industry, yet the simultaneous optimization of yield and productivity has historically been complex. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for preparing 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective bayesian optimization (MOBO) and mechanism-based intrinsic kinetic modeling, executed in parallel to optimize reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield (99%) and productivity (0.64 g/hour) within a limited number of iterations. In comparison, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal result. Thus, while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the optimization landscape but may be impacted by non-chemical kinetic phenomena and requires significant time and resources.

关键词： multi-objective bayesian optimization Kinetic modeling Continuous flow Heterogeneous hydrogenation optimization Inline analysis

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multi-objective bayesian optimization for Laminate-Inspired Mechanically Reinforced Piezoelectric Self-Powered Sensing Yarns

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ADVANCED SCIENCE 2024年第33期11卷 e2402440页

作者： Yang, Ziyue Park, Kundo Nam, Jisoo Cho, Jaewon Choi, Yong Jun Kim, Yong-Il Kim, Hyeonsoo Ryu, Seunghwa Kim, Miso Sungkyunkwan Univ SKKU Dept Adv Mat Sci & Engn Suwon 16419 South Korea Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon 34141 South Korea Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Korea Res Inst Stand & Sci Daejeon 34113 South Korea Sungkyunkwan Univ SKKU SKKU Inst Energy Sci & Technol SIEST Suwon 16419 South Korea

Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF-TrFE) yarn structures are efficiently optimized. By leveraging a multi-objective bayesian optimization-based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high-performance piezoelectric P(VDF-TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self-powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real-time signal detection.

关键词： electrospinning machine learning multi-objective bayesian optimization piezoelectric yarns self-powered sensing

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multi-objective bayesian optimization SUPPORTED BY GAUSSIAN PROCESS CLASSIFIERS AND CONDITIONAL PROBABILITIES

MULTI-OBJECTIVE BAYESIAN OPTIMIZATION SUPPORTED BY GAUSSIAN ...

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ASME International Design Engineering Technical Conferences / 42nd Annual Computers and Information in Engineering Conference (IDETC-CIE) / 48th Design Automation Conference (DAC)

作者： Valladares, Homero Tovar, Andres Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA IUPUI Purdue Sch Engn & Technol Dept Mech & Energy Engn Indianapolis IN USA

ISBN: (纸本)9780791886236

In the last years, there has been an increasing effort to develop bayesian methodologies to solve multi-objective optimization problems. Most of these methods can be classified in two groups: infilling criterion-based methods and aggregation-based methods. The first group employs an index that quantifies the gain that a new design can produce in the current Pareto front while the last group uses a (possibly non-linear) aggregation function and a weighting vector to identify a Pareto design. Most infilling-based methods have been developed to solve two-objective optimization problems. Aggregation-based methods enable the solution of many-objective optimization problems but their performance depends on the set of weighting vectors, which are often selected randomly. This study proposes a novel multi-objective bayesian framework that exploits the rich probabilistic information that can be extracted from Gaussian process (GP) classifiers and the ability of conditional probabilities to capture design preferences. In the proposed framework, a GP classifier is trained to identify design zones that potentially contain Pareto designs. The training process involves the inference of a latent GP that encodes input-space interactions that describe a Pareto design. This latent GP enables the solution of many-objective optimization problems with any standard acquisition function and without the prescription of a weighting vector. Conditional probabilities are utilized to define design goals that promote a uniform expansion of the Pareto front. The proposed approach is demonstrated with two benchmark analytical problems and the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations.

关键词： multi-objective bayesian optimization Gaussian process classification Gaussian process regression Sandwich composites Blast mitigation

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CFD-based multi-objective bayesian optimization approach for compact and efficient hydrogen production in steam reforming reactor

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JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY 2024年 139卷 378-389页

作者： Han, Ja-Ryoung Kim, Yo-Han Korea Inst Geosci & Mineral Resources 124 Gawhak Ro Daejeon 34132 South Korea Korea Gas Corp KOGAS Res Inst 1248 Suin Ro Ansan 15328 Gyeonggi Do South Korea

This paper introduces a multi-objective bayesian optimization approach for concurrently optimizing the size and productivity of a steam methane reforming reactor. Seven design variables, resulting in a total of 3,929,310 possible combinations, were explored, leading to three Pareto optimal designs after 100 iterations. Parametric studies were conducted first, and the results of bayesian optimization were suggested with initial samples obtained via Latin Hypercube Sampling. The Pareto optimal designs revealed significant reduction in size and improvement in productivity. Furthermore, adjusting operating conditions, specifically the steam-to-carbon ratio, showed additional improvements of productivity. This study can contribute to efficiently proposing Pareto optimal designs with potential applications in hydrogen supply for compact and highly efficient systems.

关键词： multi-objective bayesian optimization Computational fluid dynamics Steam methane reformer Reactor design Hydrogen productivity

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High-Dimensional multi-objective bayesian optimization With Block Coordinate Updates: Case Studies in Intelligent Transportation System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 884-895页

作者： Wang, Hongyan Xu, Hua Zhang, Zeqiu Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Huawei Technol Beijing 100095 Peoples R China

Many transportation system problems can be formulated as high-dimensional expensive multi-objective problems. They are challenging for Gaussian process-based bayesian optimization methods to find the Pareto fronts due to the curse of dimensionality and the boundary issue in the acquisition function optimization. This paper presents a multi-objective bayesian optimization method with block coordinate updates, Block-MOBO, to solve high-dimensional expensive multi-objective problems. Block-MOBO first partitions the decision variable space into different blocks, each of which includes a low-dimensional multi-objective problem. At each iteration, one block is considered and the decision variables not in this block are approximated by context-vector generation embedded with the Pareto prior knowl-edge thus promoting convergence. To tackle the boundary issue, we present ?-greedy acquisition function in a bayesian and multi-objective fashion, which recommends candidates either from the exploitation-exploration trade-off perspective or with probability ? from the Pareto dominance relationship perspective. We verify the effectiveness of Block-MOBO by comparing it with other multi-objective bayesian methods on two real-world optimization problems in transportation system and three multi-objective synthetic test suites. The experimental results show that Block-MOBO can find more evenly distributed and non-dominated solutions in the whole search space with lower complexity compared with other state-of-the-art approaches. Our analyses illustrate that block coordinate updates and ?-greedy acquisition function contribute to computational complexity reduction and convergence-diversity trade-offs, respectively.

关键词： High-dimensional expensive multi-objective problems multi-objective bayesian optimization Gaussian process ?-dominance rank convergence-diversity trade-offs

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Evolutionary multi-objective bayesian optimization Based on multisource Online Transfer Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 488-502页

作者： Li, Huiting Jin, Yaochu Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany Univ Surrey Dept Comp Sci Guildford GU2 7XH England

One main challenge in multi-objective bayesian optimization of expensive problems is that only a very limited number of fitness evaluations can be afforded. To address the above challenge, this article introduces multisource online transfer learning into an evolutionary multi-objective bayesian optimization algorithm, facilitating the use of knowledge transferred from multiple computationally cheap problems. According to the dominance relationships of the solutions in the cheap and expensive problems, the source selection and style transfer mapping in online transfer learning are adopted to augment the data for training the Gaussian processes for the expensive problems, in which an adaptive online multisource transfer learning method is proposed based on the relationship between the balance factor parameter and the transfer mapping method. Comparative studies on two sets of widely used multi-objective optimization benchmark problems, two sets of multi-task optimization problems, and one real-world expensive optimization problem confirm the effectiveness of the proposed algorithm.

关键词： multi-objective bayesian optimization expensive problems multisource online transfer learning Gaussian process

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multi-objective bayesian optimization of fused filament fabrication parameters for enhanced specific fracture energy in PLA-carbon fiber composites

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ADVANCED MANUFACTURING-POLYMER & COMPOSITES SCIENCE 2024年第1期10卷

作者： Fulkerson, Olivia Inman, Erik Rane, Aditya Noori, Hadi Deep, Akash Ramesh, Srikanthan Oklahoma State Univ Sch Mech & Aerosp Engn Stillwater OK 74078 USA Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA

Short carbon fiber-reinforced polymers (SCFRPs) are used in automotive and aerospace applications because of their superior strength-to-weight ratio and resistance to fatigue and thermal stresses. Fused filament fabrication (FFF) is considered an efficient fabrication method for SCFRP components, yet optimizing process parameters for specific performance targets is non-trivial and resource consuming. Herein, a multi-objective bayesian optimization (MOBO) framework for FFF-based SCFRP fabrication has been developed to fine-tune process parameters to optimize the trade-off between tensile strength and weight of the products. Initial random experiments were conducted on printed samples with different infill densities, printing speeds, and layer heights to evaluate their impact on specific tensile fracture energy (STFE) and component weight. These parameters were found to influence STFE and weight significantly. Data-driven sequential experimentation was then applied to identify a set of optimal values for these parameters. A surrogate-based optimization framework was utilized, and optimal points were determined through iterative refinement, with the process culminating when further iterations did not significantly enhance weight or STFE, indicating Pareto optimality. The efficacy of the methodology was demonstrated by identifying FFF parameters that achieve Pareto optimal STFE-to-weight ratios in nine iterations. This data-driven approach provides an efficient route to process optimization compared to heuristic and computational methods, streamlining the SCFRP design process via FFF and potentially extending to other composite systems and manufacturing processes. [GRAPHICS] .

关键词： Fused filament fabrication short carbon fiber-reinforced polymers multi-objective bayesian optimization specific fracture energy additive manufacturing parameter optimization

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Surrogate-assisted multi-objective bayesian optimization for improved rheological design of bioinks

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MANUFACTURING LETTERS 2024年 41卷 1405-1414页

作者： Rane, Aditya Hart, Stephanie Ramesh, Srikanthan Deep, Akash Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA Oklahoma State Univ Sch Mat Sci & Engn Tulsa OK 74106 USA

Extrusion bioprinting is a popular biofabrication technique for creating viable biological constructs with applications ranging from regenerative medicine and cancer research to therapeutics screening. A primary challenge within extrusion bioprinting lies in preserving cell viability and function amidst the shear forces generated during extrusion. Therefore, the formulation of shear-thinning bioinks with tailored flow properties is essential to minimize shear stress accumulation during printing. However, this task is complex, as the bioink's flow properties are contingent upon numerous factors, including but not limited to the polymer concentration, spatial cell density in the bioink, molecular weight of the polymer, and the nozzle's geometry. The current methods for adjusting flow properties are heuristic, posing challenges to the rapid development models and automation of the bioprinting workflow. In this study, we introduce a Gaussian Process-based multi-objective bayesian optimization framework that rapidly identifies Pareto-optimal flow property values to minimize hydrodynamic shear stresses during extrusion. The optimization focuses on four flow parameters, minimum viscosity, maximum viscosity, consistency index, and flow behavior index from the power-law model of shear-thinning fluids alongside the nozzle outlet diameter. Following an initial set of 24 experimental runs, our sequential design approach required 21 iterations to achieve convergence on a set of flow parameters that meet the Pareto optimality criteria. The presented strategy, which integrates physics-based numerical models with data-driven optimization models, emerges as a tool for the rapid fine-tuning of bioink flow behaviors. The multi-objective framework also sets the stage for future exploration into cell viability and functional outcomes post-bioprinting. (c) 2024 The Authors. Published by ELSEVIER Ltd. This is an open access article under the CC BY-NC-ND license (http://***

关键词： 3D bioprinting Sequential Design Gaussian Process multi-objective bayesian optimization

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Empirical study of evolutionary computation-based multi-objective bayesian optimization for materials discovery

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SOFT COMPUTING 2023年第15-16期28卷 8807页

作者： Ohno, Hiroshi Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi 4801192 Japan

multi-objective bayesian optimization (MOBO) is broadly used for applications with high cost observations such as materials discovery. In BO, a derivative-free optimization algorithm is generally employed to maximize the acquisition function. In this study, we present a method for acquisition function maximization based on a (1 + 1)-evolutionary strategy in MOBO for materials discovery, which is a simple and easy-to-use approach with low computational complexity compared to conventional algorithms. In MOBO, weight vectors are used for scalarizing MO functions, typically employed to convert MO optimization into single-objective optimization. The weight vectors at each round of MOBO are generally obtained using either stochastic (random sampling) or deterministic methods based on searched results. To clarify the effect of both the scalarizing methods on MOBO, we examine the effectiveness of random sampling methods versus two deterministic methods: reference-vector-based and self-organizing map-based decomposition methods. Experimental results from four test functions and a hydrogen storage material database as a concrete application show the effectiveness of the proposed method and the random sampling method. These results implied that the proposed method was useful for real-world MOBO experiments in materials discovery.

关键词： multi-objective bayesian optimization Evolutionary strategies Random scalarizations Hydrogen storage materials Materials informatics

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Parallel multi-objective bayesian optimization approaches based on multi-fidelity surrogate modeling

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年 143卷

作者： Lin, Quan Hu, Jiexiang Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Peoples R China

Aerospace product design optimizations, such as micro-aerial vehicle fuselage design, often involve multiple objectives. multi-objective bayesian optimization (MOBO) is an efficient approach in solving problems concerning multiple conflicting objectives. Conventional MOBO approaches are often limited by sequential optimizations, which can only add one sample in each iteration. The parallel computing strategy is a desirable way to accelerate the optimization process by adding a batch of samples in one iteration. Unfortunately, existing parallel MOBO approaches are constrained to handling single-fidelity data, thereby missing out on the potential benefits of utilizing auxiliary low-fidelity data. To further improve the optimization efficiency of the parallel MOBO approach, this paper proposes two parallel MOBO approaches based on multi-fidelity surrogate modeling. Specially, the cheap auxiliary low-fidelity data can be utilized to improve the performance of the parallel MOBO approach by multi-fidelity surrogate modeling. The updating points and fidelity levels are determined by a modified hypervolume excepted improvement function, and two parallel computing strategies are developed for multi-point sampling. Additionally, a constraint handling strategy is developed for problems with constraints by adopting the probability of feasibility functions. The proposed approaches are demonstrated through numerical benchmark examples and two real-world applications involving the multi-objective optimizations of a microaerial vehicle fuselage and a metamaterial vibration isolator. Results in the real-world applications show that the proposed approaches significantly improve the optimization efficiency with faster convergence speed and exhibit superior overall performance compared with the state-of-the-art MOBO methods.

关键词： multi-objective bayesian optimization multi-fidelity surrogate Parallel computing Micro-aerial vehicle fuselage

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