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bayesian optimization of gray-box process models using a modified upper confidence bound acquisition function

COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Winz, Joschka Fromme, Florian Engell, Sebastian TU Dortmund Univ Emil Figge Str 70 D-44227 Dortmund Germany

Optimizing complex process models can be challenging due to the computation time required to solve the model equations. A popular technique is to replace difficult-to-evaluate submodels with surrogate models, creating a gray-box process model. bayesian optimization (BO) is effective for global optimization with minimal function evaluations. However, existing extensions of BO to gray-box models rely on Monte Carlo (MC) sampling, which requires preselecting the number of MC samples, adding complexity. In this paper, we present a novel BO approach for gray-box process models that uses sensitivities instead of MC and can be used to exploit decoupled problems, where multiple submodels can be evaluated independently. The new approach is successfully applied to six benchmark test problems and to a realistic chemical process design problem. It is shown that the proposed methodology is more efficient than other methods and that exploiting the decoupled case additionally reduces the number of required submodel evaluations.

关键词： bayesian optimization Surrogate modeling Gray-box modeling Process optimization

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bayesian optimization for quick determination of operating variables of simulated moving bed chromatography

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COMPUTERS & CHEMICAL ENGINEERING 2025年 192卷

作者： Jeong, Woohyun Jang, Namjin Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn Daejeon 34141 South Korea Hanwha Solut Corp Daejeon R&D Ctr 76 Gajeong Ro Daejeon 34128 South Korea Univ Southern Calif Mork Family Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA

The Simulated Moving Bed (SMB) is a continuous chromatographic separation process that operates on the principle of counter-current movement between the solid and liquid phases. Due to periodic switching of feed and product ports across numerous connected columns, adjusting SMB operating variables such as feed and product flow rates and switching time to achieve desired separations is challenging. While equilibrium theory can help narrow the search space, obtaining essential information such as accurate adsorption isotherms is crucial. This requirement, combined with often highly stringent production specifications, makes it challenging to identify even a feasible operating condition, let alone an optimal one. Trial-and-error-based approaches are often impractical as reaching cyclic steady state can be time-consuming, and any waste produced during this period can lead to significant economic losses. While rigorous dynamic models are available, they are computationally intensive and often do not accurately mirror actual process behavior. To address these challenges, the use of bayesian optimization (BO) is proposed to sequentially approach optimal SMB operation. Furthermore, it is suggested to employ the simpler True Moving Bed (TMB) model as a prior for the BO, which significantly accelerates convergence. This approach is demonstrated on an SMB process for cresol separation. Initially, the effectiveness of the BO using the TMB model is examined to gain insights into its behavior. Subsequently, we apply BO to the rigorous SMB model, informed by prior knowledge from the TMB model. Our results show that the developed BO framework rapidly converges to the optimal operating parameters that satisfy the purity constraints. We examine the efficiency improvements over various search algorithms and highlight the advantages of using the TMB model as a prior.

关键词： Simulated moving bed bayesian optimization Transfer learning Prior knowledge-guided BO

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bayesian optimization for hyper-parameter tuning of an improved twin delayed deep deterministic policy gradients based energy management strategy for plug-in hybrid electric vehicles

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APPLIED ENERGY 2025年 381卷

作者： Wang, Jinhai Du, Changqing Yan, Fuwu Hua, Min Gongye, Xiangyu Yuan, Quan Xu, Hongming Zhou, Quan Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Hubei Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT England

Hybridization and electrification of vehicles are underway to achieve Net-zero emissions for road transport. The upcoming deep reinforcement learning (DRL) algorithm shows great promise for the efficient energy management of PHEVs, as it provides the potential to achieve theoretical optimal performance. However, brittle convergence properties, high sample complexity, and sensitivity to hyper-parameters of DRL algorithms have been major challenges in this field, limiting the applicability of DRL to real-world tasks. A novel EMS for PHEV based on bayesian optimization (BO) and improved Twin Delay Deep Deterministic Policy Gradient (TD3) algorithm is proposed in this paper, in which BO is introduced to optimize the TD3 hyper-parameters and a non-parametric reward function (NRF) is designed to improve the TD3 algorithm (BO-NRTD3). present work addresses two challenges to contribute to the proposed EMS: (1) By hyper-parameter tuning, TD3 strategy's brittle convergence and robustness characteristics have been significantly improved;and (2) designing the non-parametric reward function (NRF), the TD3 strategy can tackle system uncertainties. These findings are validated by comparing with various cutting-edge DRL and DP strategies using Software-in-the Loop (SiL) and Hardware-in-the-Loop (HiL) tests. The results show that the energy economy of the BO-NRTD3 strategy is up to 98.15% of DP and 4.23% more robust than the parametric reward function TD3 (PR-TD3) strategy.

关键词： Energy management strategy Plug-in hybrid electric vehicles Twin Delayed Deep Deterministic Policy Gradients bayesian optimization Non-parametric reward function

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bayesian optimization of tailgate rib structures enhancing structural stiffness under manufacturing constraints of injection molding

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JOURNAL OF MANUFACTURING PROCESSES 2025年 134卷 739-748页

作者： Lee, Hugon Yeo, Jinwook Kong, Keonpyo Myeong, Dujae Jang, Donghoon Lee, Jongyeob Choi, Hyeokhwan Kim, Namkeun Ryu, Seunghwa Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon 34141 South Korea Genesis Closure Engn Design Team Hyundai Motor Grp Hwaseong 19278 South Korea Q JEN Mold Tech Tech Sales Dept Incheon 21700 South Korea Q JEN Mold Tech Mold Design Dept Incheon 21700 South Korea Ulsan Technopk Automot Parts Inst Ctr Ulsan 44222 South Korea Sogang Univ Dept Mech Engn Seoul 04107 South Korea

The shift towards environmentally friendly transportation has driven significant attention to lightweight vehicle design, especially to counterbalance the substantial weight of batteries in electric vehicles. Reinforced polymer composites offer a promising alternative to traditional steel due to their lower density. However, to overcome the inherent stiffness limitations of mass-produced short fiber reinforced polymer composites manufactured through compounding and injection molding, the use of internal reinforcing structures, such as ribs, is essential. This study proposes a cost-effective, data-driven approach to parametrize and optimize rib placement within automotive tailgate components. The primary objective is to maximize structural stiffness, evaluated with industry- standard testing methods for tailgate components, while adhering to mass constraints. Rib structures are parametrized with a focus on simplicity to reduce data requirements, accounting for manufacturing constraints inherent to injection molding and maintaining permutation invariance of rib designs. Given the high cost of evaluating variety of rib configurations, bayesian optimization is applied for efficient data utilization. Gaussian process regression is used as a surrogate model to predict structural stiffness, based on finite element analysis data from various rib configurations. The optimized design is then fabricated as full-scale prototypes through injection molding, and their performance is validated against numerical predictions. This approach exemplifies a practical, data-driven methodology for designing rib structures in complex industrial components, integrating computational design with manufacturing processes.

关键词： Structural design Injection molding bayesian optimization

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bayesian optimization With Formal Safety Guarantees via Online Conformal Prediction

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2025年第1期19卷 45-59页

作者： Zhang, Yunchuan Park, Sangwoo Simeone, Osvaldo Kings Coll London Ctr Intelligent Informat Proc Syst CIIPS Dept Engn Kings Commun Learning & Informat Proc KCLIP Lab London WC2R 2LS England

Black-box zero-th order optimizationis a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a designer sequentially attempts candidate solutions, receiving noisy feedback on the value of each attempt from the system. In this paper, we study scenarios in which feedback is also provided on the safety of the attempted solution, and the optimizer is constrained to limit the number of unsafe solutions that are tried throughout the optimization process. Focusing on methods based on bayesian optimization (BO), prior art has introduced an optimization scheme - referred to as SafeOpt - that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met. In this paper, a novel BO-based approach is introduced that satisfies safety requirements irrespective of properties of the constraint function. This strong theoretical guarantee is obtained at the cost of allowing for an arbitrary, controllable but non-zero, rate of violation of the safety constraint. The proposed method, referred to as Safe-Bocp, builds on online conformal prediction (CP) and is specialized to the cases in which feedback on the safety constraint is either noiseless or noisy. Experimental results on synthetic and real-world data validate the advantages and flexibility of the proposed Safe-Bocp.

关键词： Safety optimization Noise measurement Closed box Noise Uncertainty Europe bayesian optimization online conformal prediction safe exploration

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bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance

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CONTROL ENGINEERING PRACTICE 2025年 154卷

作者： Teodorescu, Catalin Stefan West, Andrew Lennox, Barry Univ Manchester Dept Elect & Elect Engn Oxford Rd Manchester M13 9PL England

Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human-robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high;if not, it will gradually reduce the human operator's authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.

关键词： bayesian optimization Gaussian process regression Shared control Obstacle avoidance Teleoperation Semi-autonomy Nuclear robotics ROS

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bayesian optimization over the probability simplex

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2025年第1期93卷 77-91页

作者： Candelieri, Antonio Ponti, Andrea Archetti, Francesco Univ Milano Bicocca Dept Econ Management & Stat Milan Italy Univ Milano Bicocca Dept Comp Sci Syst & Commun Milan Italy

Gaussian Process based bayesian optimization is largely adopted for solving problems where the inputs are in Euclidean spaces. In this paper we associate the inputs to discrete probability distributions which are elements of the probability simplex. To search in the new design space, we need a distance between distributions. The optimal transport distance (aka Wasserstein distance) is chosen due to its mathematical structure and the computational strategies enabled by it. Both the GP and the acquisition function is generalized to an acquisition functional over the probability simplex. To optimize this functional two methods are proposed, one based on auto differentiation and the other based on proximal-point algorithm and the gradient flow. Finally, we report a preliminary set of computational results on a class of problems whose dimension ranges from 5 to 100. These results show that embedding the bayesian optimization process in the probability simplex enables an effective algorithm whose performance over standard bayesian optimization improves with the increase of problem dimensionality.

关键词： bayesian optimization Wasserstein distance Gaussian Processes

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bayesian optimization in Bioprocess Engineering-Where Do We Stand Today?

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BIOTECHNOLOGY AND BIOENGINEERING 2025年第0期 2025 Mar 5页

作者： Gisperg, Florian Klausser, Robert Elshazly, Mohamed Kopp, Julian Brichtova, Eva Prada Spadiut, Oliver Christian Doppler Lab Inclus Body Proc 4 0 Vienna Austria Tech Univ Wien Inst Chem Environm & Biosci Engn Res Area Biochem Engn Vienna Austria

bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing exploration and exploitation. Although Design of Experiments has traditionally been the preferred method for optimizing bioprocesses, AI-driven tools have recently drawn increasing attention to bayesian optimization within bioprocess engineering. This review presents the principles and methodologies of bayesian optimization and focuses on its application to various stages of bioprocess engineering in upstream and downstream processing.

关键词： active learning bayesian optimization bioprocess engineering machine learning model-based optimization

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bayesian optimization OF HYPER-PARAMETERS AND REWARD FUNCTION IN DEEP REINFORCEMENT LEARNING: APPLICATION TO BEHAVIOR LEARNING OF MOBILE ROBOT

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2025年第2期21卷 469-480页

作者： Nishimura, Takuto Sota, Ryosuke Horiuchi, Tadashi Matsue Coll Natl Inst Technol Adv Engn Fac 14-4 Nishi Ikuma Matsue Shimane 6908518 Japan Nara Inst Sci & Technol Grad Sch Sci & Technol 8916-5 Takayama Cho Ikoma Nara 6300192 Japan

. Deep reinforcement learning is a machine learning method that combines deep learning and reinforcement learning. Deep Q-Network (DQN) is one of the typical methods of deep reinforcement learning. DQN uses Convolutional Neural Network (CNN) which can extract features from the input images. We have applied DQN method to the mobile robot navigation problem. The values of hyper-parameters, including the network structure of DQN, and the reward function used in the DQN algorithm, have been determined empirically. In this study, we attempt to optimize the values of hyper-parameters and reward function of deep reinforcement learning by using bayesian optimization. We realized to optimize the values of hyper-parameters including the network structure of DQN, and the reward function by Optuna, a framework of bayesian optimization. We confirmed that our method using the values of hyper-parameters and reward function obtained by Optuna have more goal achievements and fewer action steps to achieve the goal among test runs after learning than those by empirical method.

关键词： Deep reinforcement learning bayesian optimization Hyper-parameters Reward function Mobile robot Behavior acquisition

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bayesian optimization Of NeuroStimulation (BOONStim)

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BRAIN STIMULATION 2025年第2期18卷 112-115页

作者： Oliver, Lindsay D. Jeyachandra, Jerrold Dickie, Erin W. Hawco, Colin Mansour, Salim Hare, Stephanie M. Buchanan, Robert W. Malhotra, Anil K. Blumberger, Daniel M. Deng, Zhi-De Voineskos, Aristotle N. Campbell Family Mental Hlth Res Inst Ctr Addict & Mental Hlth Toronto ON Canada Univ Toronto Dept Psychiat Toronto ON Canada Univ Maryland Sch Med Maryland Psychiat Res Ctr Dept Psychiat Baltimore MD 21228 USA Zucker Hillside Hosp Div Psychiat Res Div Northwell Hlth Glen Oaks NY USA Donald & Barbara Zucker Sch Med Hofstra Northwell Dept Psychiat Hempstead NY USA Feinstein Inst Med Res Ctr Psychiat Neurosci Manhasset NY USA Ctr Addict & Mental Hlth Temerty Ctr Therapeut Brain Intervent Toronto ON Canada Duke Univ Sch Med Dept Psychiat & Behav Sci Durham NC USA Natl Inst Mental Hlth Intramural Res Program Expt Therapeut Branch Noninvas Neuromodulat Unit Bethesda MD USA

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