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Investigation of energy-efficient heat pump assisted heterogeneous azeotropic distillation for separating of acetonitrile/ethyl acetate/n-hexane mixture

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Chinese Journal of Chemical Engineering 2023年第3期55卷 20-33页

作者： Xinhao Li Qing Ye Jinlong Li Lingqiang Yan Xue Jian Licheng Xie Jianyu Zhang Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical EngineeringChangzhou UniversityChangzhou 213164China Changzhou University Huaide College Changzhou 213164China College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310000China

The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ternary mixture. The proposed scheme is optimized via the simulated annealing algorithm and minimum total annual cost(TAC) is used as objective functions. To minimize energy consumption,heat pump is added on the basis of optimal heterogeneous azeotropic distillation and heat integration technology is used to further improve the energy recovery. The TAC, gas emission, energy consumption and exergy destruction are used to discuss the economy and environmental protection of *** all the processes, the heat pump with higher preheating temperature(HPT) assisted HAD process by combining with heat integration(HAD-HPT-HI) has best performances on economic, environment,energy and exergy. Compared with conventional HAD process, the HAD-HPT-HI achieves the reductions of 52.17%, 68.86%, 65.87% and 65.46% on TAC, total energy consumption, gas emissions and exergy destruction, respectively.

关键词： Heterogeneous azeotropic distillation Energy-saving sa algorithm Heat pump

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Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOsa hybrid algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 2期56卷 2667-2685页

作者： Li, Dengguo Ye, Zhouling Lu, Pengzhen Wu, Ying Yang, Liu Wang, Jiahao Zhejiang Univ Technol Hangzhou 310014 Peoples R China Jiaxing Nanhu Univ Jiaxing 3140001 Peoples R China Transportat Bur Longquan 323700 Peoples R China

The traditional probabilistic reliability analysis method has problems such as poor convergence, low calculation accuracy, and long time consumption in calculating the reliability of concrete arch bridges due to factors such as the uncertainty of the structural parameters and the performance function being highly nonlinear. This paper proposes a method for calculating the reliability of concrete arch bridges based on the Kriging model and particle swarm optimization algorithm (PSOsa) of the simulated annealing algorithm. This method takes advantage of the Kriging model in small samples and high-dimensional nonlinear data processing capabilities and establishes a response surface model to approximate the actual limit state function. The optimization of the PSO algorithm is realized through the self-adaptive and variable probability mutation operation of the sa algorithm, which enhances the ability of the PSO algorithm to get rid of the local minimum, effectively avoids falling into the local minimum, and finally makes the calculation result tend to the global optimum. It overcomes the problems of slow convergence speed and premature maturity of traditional PSO algorithms. The correctness and effectiveness of the method proposed in this paper are verified through the example analysis and the actual engineering application of a concrete arch bridge. The research results show that the method proposed in this paper has obvious advantages in sample size, calculation accuracy, and iteration times compared with the existing reliability calculation methods for concrete arch bridges. This paper provides a fast and effective method for the structural reliability calculation of concrete arch bridges.

关键词： Bridge engineering Reliability analysis Kriging surrogate model PSO algorithm sa algorithm

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A Hybrid Dung Beetle Optimization algorithm with Simulated Annealing for the Numerical Modeling of Asymmetric Wave Equations

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Applied Geophysics 2024年第3期21卷 513-527,618页

作者： Wei Xu-ruo Bai Wen-lei Liu Lu Li You-ming Wang Zhi-yang College of Information Science and Technology Beijing University of Chemical TechnologyBeijingPRC Key Laboratory of Petroleum Resources Research Institute of Geology and GeophysicsChinese Academy of SciencesBeijingPRC University of Chinese Academy of Sciences BeijingChina Aramco Research Center-Beijing Aramco AsiaBeijingChina

In the generalized continuum mechanics(GCM)theory framework,asymmetric wave equations encompass the characteristic scale parameters of the medium,accounting for microstructure *** study integrates two theoretical branches of the GCM,the modified couple stress theory(M-CST)and the one-parameter second-strain-gradient theory,to form a novel asymmetric wave equation in a unified *** modeling of the asymmetric wave equation in a unified framework accurately describes subsurface structures with vital implications for subsequent seismic wave inversion and imaging ***,employing finite-difference(FD)methods for numerical modeling may introduce numerical dispersion,adversely affecting the accuracy of numerical *** design of an optimal FD operator is crucial for enhancing the accuracy of numerical modeling and emphasizing the scale ***,this study devises a hybrid scheme called the dung beetle optimization(DBO)algorithm with a simulated annealing(sa)algorithm,denoted as the sa-based hybrid DBO(SDBO)*** FD operator optimization method under the SDBO algorithm was developed and applied to the numerical modeling of asymmetric wave equations in a unified *** the DBO and sa algorithms mitigates the risk of convergence to a local *** numerical dispersion outcomes underscore that the proposed SDBO algorithm yields FD operators with precision errors constrained to 0.5‱while encompassing a broader spectrum *** result confirms the efficacy of the SDBO ***,the numerical modeling results demonstrate that the new FD method based on the SDBO algorithm effectively suppresses numerical dispersion and enhances the accuracy of elastic wave numerical modeling,thereby accentuating scale *** result is significant for extracting wavefield perturbations induced by complex microstructures in the medium and the analysis of scale effects.

关键词： Finite-difference Asymmetric wave equation Numerical modeling DBO algorithm sa algorithm

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Analysis of the material distribution system of wise information technology of 120 under deep learning

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JOURNAL OF SUPERCOMPUTING 2021年第9期77卷 9988-10002页

作者： Tang, sai Zhao, Hongrui Wang, Zhuolin Zhu, Yue Hou, Daiyu Wang, Hongwei Harbin Inst Technol Sch Humanities Social Sci & Law Harbin 150001 Heilongjiang Peoples R China Harbin Finance Univ Dept Accounting Harbin Heilongjiang Peoples R China

In order to explore the feasibility of using deep learning in the Wise Information Technology of 120 (WIT120) material distribution system under deep learning, in the study, first, the Hopfield neural network and simulated annealing (sa) algorithm are used to study the path optimization in the WIT120 material distribution system. Aiming at the poor optimization and robustness of the traditional Hopfield neural network and the low optimization efficiency of sa algorithm, in this study, a memory device is added to the sa algorithm, the distribution starting points of the two algorithms are fixed, and an improved hybrid algorithm is designed by combining Hopfield neural network and sa algorithm. Then, the improved hybrid algorithm of the traditional Hopfield neural network and sa algorithm is used in the actual medical material distribution system, and comparative analysis of the distribution route, cost, and scheduling time before and after using the proposed algorithm is performed. Besides, the difference between the proposed algorithm and other algorithms in path optimization and path planning efficiency is compared and analyzed. The results show that the improved hybrid algorithm proposed in this research reduces the total mileage of distribution by 46.9%, costs by 33.9%, and the time required for scheduling by 99% after applied in the medical material distribution system. It is also better than traditional analog algorithms and neural networks in distribution path optimization. The path optimization results show that the shortest path produced by the improved hybrid algorithm in this study is 27.85, which is superior to the traditional sa algorithm, Hopfield neural network, and genetic algorithm (the shortest paths obtained are 41.21, 44.63 and 36.48, respectively). Compared with other optimization algorithms, the path planning efficiency of the hybrid algorithm is the highest (the amount of distribution tasks completed in unit time is 22). In conclusion, the impr

关键词： WIT120 Material distribution system Deep learning Hopfield neural network sa algorithm

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Improved sine algorithm for global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Luo, Yanbin Dai, Weimin Ti, Yen -Wu Yango Univ Coll Artificial Intelligence Fuzhou Peoples R China

An improved sine cosine algorithm (abbreviated Msa) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We changed the method for setting the conversion parameters of the SCA algorithm from a linear decline into a nonlinear decline in order to optimize the timing of global exploration and local exploration. In order to improve the convergence accuracy and to increase the convergence speed of the SCA, an inertia weight is introduced in the position update equation. Ten high-dimensional complex benchmark functions are simulated with five improved SCA algorithms, under the same particle numbers and maximum iteration times. According to our experiments, the Msa algorithm is not only much better than two of the improved SCA algorithms in optimi-zation accuracy but also better than the other three improved SCA algorithms in terms of stability.

关键词： SCA algorithm sa algorithm Inertia weight Convergence accuracy

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Investigation on energy-efficient heterogeneous pressure-swing azeotropic distillation for recovery of cyclohexane and tert-butanol from industrial effluent

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SEPARATION AND PURIFICATION TECHNOLOGY 2023年第PartB期306卷

作者： Li, Xinhao Ye, Qing Li, Jinlong Liu, Yingjie Yan, Lingqiang Jian, Xue Zhang, Jianyu Changzhou Univ Sch Petrochem Engn Jiangsu Key Lab Adv Catalyt Mat & Technol Changzhou 213164 Peoples R China Zhejiang Univ Coll Chem & Biol Engn Hangzhou 310000 Peoples R China

This paper highlights the separation of cyclohexane/tert-butanol from industrial effluent by heterogeneous pressure-swing azeotropic distillation based on the properties of pressure-sensitive distillation boundaries and natural liquid-liquid separation. There is no excess component in the separating process. The optimal parameters of the conventional process are obtained by using the simulated annealing algorithm. Additional heat pump and heat integration strategies are introduced into the process with the optimal operation parameter to save more energy. Evaluation indicators, such as total annual cost (TAC), total energy consumption (TEC), gas emission, and exergy destruction, are used to assess the potential of economic and environment friendly processes. The heat pump assisted HADPSD process employing auxiliary reboilers and heat integration is the optimal energy -saving process, exhibiting decreases of 41.98%, 55.32%, 52.30%, and 55.26% for TAC, TEC, gas emission, and exergy destruction compared to conventional HADPSD.

关键词： Heterogeneous pressure-swing azeotropic distillation Energy saving sa algorithm Heat pump

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An algorithm for finding efficient test-control block designs with correlated observations

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COMPUTATIONAL STATISTICS 2020年第2期35卷 821-836页

作者： Pooladsa, saeid Doosti-Irani, Mahboobeh Isfahan Univ Technol Dept Math Sci Esfahan *** Iran

The theory of optimal test-control block designs provides guidance on treatments allocation in experimental units. However, it is theoretically difficult to find them;therefore, the availability of an efficient and user-friendly algorithm for finding the optimal designs is essential for both researchers and practitioners. This paper describes an algorithm for constructing efficient test-control incomplete block designs with correlated observations. In order to evaluate the algorithm, we compare our results with the optimal designs presented in some published papers. An advantage of our algorithm is its independency to the size of blocks and the structure of correlation. Also, it takes to run between 30 s and 10 min depending on the type of CPU processor and the design.

关键词： PSO algorithm sa algorithm Atc documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{ oddsidemargin}{-69pt} begin{document}$$A_{tc}$$ end{document}-optimal Near-optimal designs Atc documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{ oddsidemargin}{-69pt} begin{document}$$A_{tc}$$ end{document}-efficiency

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A fuzzy DEMATEL - Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects)

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INTERNATIONAL JOURNAL OF MANAGING PROJECTS IN BUSINESS 2020年第5期13卷 1025-1050页

作者： Hashemi, Reyhane Kamranrad, Reza Bagheri, Farnoosh Emami, Iman Univ Sci & Culture Tehran Iran Semnan Univ Dept Ind Engn Semnan Iran Nardis Co Dept Change Management Tehran Iran

Purpose The aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper. Design/methodology/approach This paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) - a technique that assist to solve decision-making problems - and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm. Findings The results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using sa algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success. Practical implications The application of the proposed method is illustrated via a real data set from energy projects. Originality/value We propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.

关键词： Fuzzy DEMATEL Fuzzy Binary Logistic Regression Impact-Probability table Critical risk sa algorithm Optimization

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Application of Genetic algorithm in modeling of Shortest Path problem

Application of Genetic Algorithm in modeling of Shortest Pat...

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Chinese Automation Congress (CAC)

作者： Yang, Zhihui Xia, Huiwen Su, Fuwen Zhao, Jiayu Feng, Fan Wuhan Univ Technol Sch Automat Wuhan Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781728176871

For a long time, people have been looking for the best way to solve problems to improve work efficiency. Shortest path as a figure with a classical problem in the study of network technology in computer science, communications, and operations research, geographic information science and other fields has a very wide range of applications, for solving the problem is not only has important theoretical significance, but also has important practical value, the problem solving algorithm design and improving the research has important significance [1-3]. This paper introduces several commonly used algorithms to solve the shortest path problem, analyses their advantages and disadvantages. Then Genetic algorithm is introduced and the results is compared with Simulated Annealing algorithm. The results of comparison shows that the Genetic algorithm is much better than Simulated Annealing algorithm in this problem. In many existing literatures, the author only elaborates a certain method or solves a specific problem through a certain method, which to a large extent makes the advantages of the method described in the literature unable to be fully displayed. In this paper, a great improvement has been made in this aspect. This Simulated Annealing algorithm is introduced while the Genetic algorithm is described. Simulation results show that Genetic algorithm is more effective than Simulated Annealing algorithm in path planning and result optimization in solving the shortest path problem.

关键词： shortest path problem GA algorithm sa algorithm material allocation and delivery

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A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search algorithms for Automatic Radiomics-Based COVID-19 Diagnosis

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Applied Computational Intelligence and Soft Computing 2025年第1期2025卷

作者： Basma Jumaa saleh Zaid Omar Muhammad Amir As’ari Vikrant Bhateja Lila Iznita Izhar Department of Electronic and Computer Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia utm.my Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia utm.my Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Jaunpur Uttar Pradesh India vbspu.ac.in Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia utp.edu.my

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the virus has posed a grave threat to human health. Automated segmentation of COVID-19 lung computed tomography (CT) scans is a crucial diagnostic tool that aids physicians in providing accurate and timely diagnoses, as it contains significant radiomics information. Given that the specificity for discriminating between the causes of conventional pulmonary features is lower than its sensitivity, the primary goal of this study is to develop and evaluate a CT-based radiomics model capable of distinguishing between COVID-19 and other lung diseases. To address this, we propose an efficient, modified radiomics feature processing method that integrates an optimal aerial perspective (OAP) parameter-based intensity dark channel prior (IDCP) with a 50-layer residual deep neural network (ResNet50 DNN) for autolesion segmentation (ALS-IOAP-DNN). To further enhance COVID-19 lesion estimation, novel optimization strategies, including a hybrid simulated annealing-cuckoo search (sa-CS) algorithm, are introduced alongside the original sa method. The sa-CS algorithm extends sa by preventing entrapment in local minimum and enhancing global exploration. Five benchmark functions are used to accelerate convergence and address the issue of local optima. As a result, the hybrid approach outperforms 10 recent studies on two publicly available datasets (COVID-CT-Dataset and HUST-19), achieving an average accuracy score of 100% across different epochs, along with perfect accuracy, 100% sensitivity, and 100% specificity. The proposed models significantly outperform the baseline model, with accuracy improvements of 13.6% on Data1 and 2.5% on Data2. While the baseline model achieves 88% accuracy on Data1 and 97.6% on Data2, the proposed ALS-IOAP-DNN4 model attains perfect accuracy (100%) on both datasets, demonstrating the effectiveness of ALS and advanced optimization techniques. Furthermore, the use of OAP with IDCP enhances the precis

关键词： computed tomography COVID-19 pandemic CS algorithm radiomics sa algorithm

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