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multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes

Energy and AI 2022年第1期7卷 120-130页

作者： Xin Yee Tai Raffaella Ocone Steven D.R.Christie Jin Xuan Department of Chemical Engineering Loughborough UniversityLoughboroughLE113TUUK School of Engineering&Physical Science Heriot-Watt UniversityEdinburghEH144ASUK Department of Chemistry Loughborough UniversityLoughboroughLE113TUUK

Catalytic chemical processes such as hydrocracking,gasification and pyrolysis play a vital role in the renewable energy and net zero *** to the complex and non-linear behaviours during operation,catalytic chemical processes require a powerful modelling tool for prediction and optimisation for smart operation,speedy green process routes discovery and rapid process ***,challenges remain due to the lack of an effective modelling and optimisation toolbox,which requires not only a precise analysis but also a fast ***,we propose a hybrid machine learning strategy by embedding the physics-based continuum lumping kinetic model into the data-driven artificial neural network *** hybrid model is adopted as the surrogate model in the multi-objective optimisation and demonstrated in the benchmarking of a hydrocracking *** results show that the novel hybrid surrogate model exhibits the mean square error less than 0.01 by comparing with the physics-based simulation *** well-trained hybrid model was then integrated with non-dominated-sort genetic algorithm(NSGA-II)as the surrogate model to evaluate and optimise the yield and selectivity of the hydrocracking *** Pareto front from the multi-objective optimisation was able to identify the trade-off curve between the objective functions which is essential for the decision-making during process *** work indicates that adopting the hybrid machine learning strategy as the surrogate model in the multi-objective optimisation is a promising approach in various complex catalytic chemical processes to enable an accurate computation as well as a rapid optimisation.

关键词： Hybrid surrogate model Hybrid machine learning multi-objective optimisation Catalytic chemical process

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Data-driven placemaking: Public space canopy design through multi-objective optimisation considering shading, structural and social performance

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Frontiers of Architectural Research 2022年第2期11卷 308-323页

作者： Jeroen van Ameijde Chun Yu Ma Garvin Goepel Clive Kirsten Jeff Wong School of Architecture The Chinese University of Hong KongHong KongChina Lightweight Works Hong KongChina APS Research Ltd. Hong KongChina

In the context of ongoing densification of cities and aging urban populations,public spaces are a crucial infrastructure to support the physical and mental wellbeing of urban *** design of public space furniture elements is often standardised,and not considered in relation to environmental conditions and mechanisms of social *** article presents a digital workflow to generate site-specific designs for shaded public seating,considering the relationships of local public places to their surroundings.A strategy for customised and site-specific design is developed through the use of multiple software tools,employing evolutionary algorithms and multi-objective *** method is applied to a small public space canopy prototype installed within a public housing estate in Hong Kong,incorporating additional criteria to achieve a low-cost and light-weight *** multiple stages of refinement and optimisation,a material,structural and social performance-driven outcome was achieved that creates a shaded space for public seating,people watching and social *** part of a larger research agenda exploring architectural form-finding and environmental psychology,the project represents potential new applications in the emerging field of socially driven computational design.

关键词： Public space Tensile membrane structures Structural design Environmental performance multi-objective optimisation Evolutionary algorithms

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Does architectural design require single-objective or multi-objective optimisation?A critical choice with a comparative study between model-based algorithms and genetic algorithms

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Frontiers of Architectural Research 2024年第5期13卷 1079-1094页

作者： Ran Zhang Xiaodong Xu Ke Liu Lingyu Kong Xi Wang Linzhi Zhao Abudureheman Abuduwayiti School of Architecture Southeast UniversityNanjing 210096China

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function optimisation Algorithm(RBFOpt),with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies(CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.

关键词： Architectural design optimisation Single-objective optimisation multi-objective optimisation Energy efficiency Early design decision

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Evolutionary multi/Many-objective optimisation via Bilevel Decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1973-1986页

作者： Shouyong Jiang Jinglei Guo Yong Wang Shengxiang Yang School of Automation Central South UniversityChangsha 410083China Department of Computing Science University of AberdeenAberdeen AB243FXUK School of Computer Science Central China Normal UniversityWuhan 430079China School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective ***,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation *** paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective *** bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower *** subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the *** bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective *** analysis and component analysis have been also carried out to further justify the proposed algorithm.

关键词： Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation

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Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

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Building Simulation 2024年第5期17卷 819-838页

作者： Yeqin Shen Yubing Hu Kai Cheng Hainan Yan Kaixiang Cai Jianye Hua Xuemin Fei Qinyu Wang School of Arts Jiangsu UniversityZhenjiang212013China School of Architecture and Urban Planning Nanjing UniversityNanjing210093China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China

This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of *** integrated model consists of five base models and a meta-model,which significantly improves the prediction ***,the R2 value was improved by 9.19% and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47%,79.88%,67.32%,and 57.02%,respectively,compared to the single prediction *** to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the *** the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the ***,the optimal design solution reduces the energy use intensity by 31.6 kWh/m^(2),improves the useful daylight index by 39%,and modulated the thermal comfort index,resulting in a decrement of 0.69℃ for the summer season and an enhancement of 0.64℃ for the winter season,***,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.

关键词： building ecological performance ensemble learning multi-objective optimisation sustainable design explainable machine learning

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Artificial intelligence in renewable systems for transformation towards intelligent buildings

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Energy and AI 2022年第4期10卷 171-189页

作者： Yuekuan Zhou Sustainable Energy and Environment Thrust Function HubThe Hong Kong University of Science and Technology(Guangzhou)NanshaGuangzhou511400GuangdongChina Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyClear Water BayHong Kong SARChina HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute FutianShenzhen

Carbon-neutrality transition in building sectors requires combinations of renewable systems and artificial intelligence (AI) for robustness, reliability, automation, and flexibility. In this study, a comprehensive review on AI applications in renewable systems, have been conducted, to report the current progress, tendency and challenges. Underlying learning mechanisms of AI-based various applications have been studied, in terms of modelling techniques on solar power forecasting, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations and intelligent buildings. Furthermore, AI techniques in renewable energy utilisations have been clearly reviewed, with respect to solar potential evaluation, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations. Results showed that, compared to physical models, the data-driven models show superiority in modelling simplification and modification, high prediction accuracy, and computational efficiency. In order to improve the system operational robustness under multi-level scenario uncertainty, a generic approach with artificial intelligence was proposed, including nonlinear data-driven model development, uncertainty-based energy performance prediction, stochastic uncertainty and statistical analysis. Furthermore, artificial intelligence can also be applied in renewable systems for security, reliability and stability. This study provides a clear roadmap on historical development, recent advances, cutting-edge techniques and progress, and future challenges of AI applications in renewable energy systems, paving path for developing smart and resilient renewable energy systems for decarbonization in buildings.

关键词： Renewable Energy Power Forecasting Fault Detection and Diagnosis multi-objective optimisation Machine Learning

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A novel MOPSO-based ADRC controller for multi-objective control strategy of the high-precision tracking system

A novel MOPSO-based ADRC controller for multi-objective cont...

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第43届中国控制会议

作者： Haolin Wang Jiuqiang Deng Tong Guo Qianwen Duan Yao Mao Qiliang Bao National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Institute of Optics and Electronics Chinese Academy of Sciences School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences University of Chinese Academy of Sciences

In this paper, a novel parameters tuning method based on the multi-objective adaptive evolutionary particle swarm optimization(MOAEPSO) algorithm is proposed for the active disturbance rejection control(ADRC) strategy of the high-precision tracking system(HTS). In the HTS, the conflict exists between disturbance rejection ability and dynamic tracking ability, thereby it is necessary to select reasonable controller parameters to solve this problem. To obtain an optimal trade-off between disturbance rejection ability and dynamic tracking ability, the proposed method transforms the parameters tuning problem into a multi-objective optimization problem. Besides that, different from the conventional MOPSO algorithm, because of particle swarm variation and novel nonlinear objective functions, the MOAEPSO algorithm obtains faster algorithm convergence speed and global optimal solution search capability. Through extensive simulation analysis and experimental verification, compared with the ADRC of the conventional parameters tuning method, the proposed method has better control performance, which proves its effectiveness and superiority.

关键词： High-precision tracking system(HTS) multi-objective adaptive evolutionary particle swarm optimization(MOAEPSO) algorithm Parameters tuning Active disturbance rejection control(ADRC) multi-objective optimisation

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