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A New Method for Determining Coal Seam Permeability based on a Gas-Solid Coupling Model and PSO plus LM Hybrid optimization algorithm

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NATURAL RESOURCES RESEARCH 2023年第3期32卷 1265-1282页

作者： Liu, Quanlin Li, Zhonghui Wang, Enyuan Kong, Xiangguo Feng, Xiaojun Liu, Yubing Wang, Dongming Zhang, Chaolin China Univ Min & Technol Key Lab Gas & Fire Control Coal Mines Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Safety Engn Xuzhou 221116 Jiangsu Peoples R China Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Shaanxi Peoples R China

Previous underground test methods for coal seam permeability are usually based on radial flow theory, which ignores the impact of coal deformation and permeability dynamic evolution. Thus, these methods have some limitations in theoretical reliability, result stability and method applicability. Therefore, this paper derives a gas-solid coupling model considering pore sorption strain. Based on this model and a hybrid optimization algorithm, which combines particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithm, a novel method for determining coal seam permeability, namely the GP method, is proposed. The feasibility and reliability of this method were verified by numerical experiments and field tests, respectively. The findings indicate that the proposed PSO + LM algorithm was superior to PSO algorithm and LM algorithm in terms of convergence and computational efficiency. In field application, the test value of the GP method was closer to the true value of coal seam permeability, and its theoretical model can better reflect the change of borehole gas flow, whether compared with the traditional radial flow method from the global perspective or compared with other classical permeability models from the local perspective. Therefore, the GP method has the potential to become an effective test method for coal seam permeability. This study has certain reference significance for the acquisition of mechanical parameters and gas parameters.

关键词： Coal seam permeability Underground test Coupling model optimization algorithm

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QoS-Aware Cloud Service optimization algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft Computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

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Synchronous Inversion of Coal Seam Gas Pressure and Permeability Based on a Dual Porosity/Dual Permeability Model and Surrogate optimization algorithm

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NATURAL RESOURCES RESEARCH 2023年第5期32卷 2115-2136页

作者： Liu, Quanlin Li, Zhonghui Wang, Enyuan Feng, Xiaojun Kong, Xiangguo Wang, Dongming China Univ Min & Technol Key Lab Gas & Fire Control Coal Mines Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Safety Engn Xuzhou 221116 Jiangsu Peoples R China Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Shanxi Peoples R China

Rapid and accurate acquisition of permeability and gas pressure is crucial for gas development and disaster prevention in coal mines. However, these two parameters are currently challenging to obtain simultaneously using traditional testing methods. Moreover, due to the theoretical basis of the radial flow equation, most methods are only applicable to cross-seam borehole, which brings great limitations to the application of the methods. Therefore, this paper derives a new dual porosity/dual permeability model considering the impacts of time-dependent Fick diffusion, matrix mechanical and sorption strain. According to this coupling model and the surrogate optimization (SO) algorithm, a synchronous inversion method of the gas pressure and permeability is suggested. Then, the dependability of this method is validated by field tests. The findings demonstrate that, in contrast to other theoretical models and optimization algorithms, the new model can more precisely depict the actual change of borehole gas flow, and the SO algorithm can find a global optimal solution with higher accuracy in less time. Compared with traditional methods, the new method has the advantages of strong universality, short testing cycle and high automatic level. Therefore, this method has the potential to be an effective tool for obtaining coal seam gas parameters.

关键词： Gas pressure Permeability Coal seam Synchronous inversion Coupling model optimization algorithm

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CHP Economic Dispatch Considering Prohibited Zones to Sustainable Energy Using Self-Regulating Particle Swarm optimization algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第3期44卷 1147-1164页

作者： Ara, Afshin Lashkar Shahi, Nastaran Mohammad Nasir, Mohammad Islamic Azad Univ Mat & Energy Res Ctr Dezful Branch POB 313 Dezful Iran

Economic dispatch is the optimal scheduling for generating units with technical constraints. Combined heat and power economic dispatch (CHPED) refers to minimization of the total energy cost for generating electricity and heat supply to load demand. This planning model integrates heat and power energy to balance energy supply and demand, mitigate climate change and improve energy efficiency of sustainable cities and green buildings. In this paper for the first time, self-regulating particle swarm optimization (SRPSO) algorithm is utilized for solving the CHPED problem by considering valve point effects and prohibited zones on fuel cost function of pure generation units and electrical power losses in transmission systems. The main advantage of SRPSO algorithm to PSO algorithm is the inertia weight flexibility with respect to search conditions. In this algorithm, unlike PSO algorithm that inertia weight reduces in each iteration, this value increases or reduces proportional to particles' positions, which will lead particles to achieve optimal value with higher speed. The capability and effectiveness of the proposed algorithm are evaluated on a large-scale energy system using MATLAB environment. The results obtained by SRPSO algorithm are outperformed by other optimization methods from the economic, sustainable energy and time consumption point of view.

关键词： Combined heat and power (CHP) optimization algorithm Cogeneration units Economic dispatch (ED) Self-regulating particle swarm optimization

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Predicting density log from well log using machine learning techniques and heuristic optimization algorithm:A comparative study

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Petroleum Research 2024年第2期9卷 176-192页

作者： Mehdi Rahmati Ghasem Zargar Abbas Ayatizadeh Tanha National Iranian Drilling Company Well Logging DepartmentAhwazIran Faculty Member of Petroleum University of Technology Iran Department of Petroleum Engineering Tarbiat Modares UniversityTehranIran

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve *** recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect *** study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon *** is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant *** tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm optimization(PSO),Imperialist Competitive algorithms(ICA),and Genetic algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log ***,our results highlight that the Genetic algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of *** comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

关键词： Density log Machine learning approaches Artificial neural networks(ANN) Adaptive neuro-fuzzy inference system(ANFIS) optimization algorithm

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Flight Conflict Resolution Simulation Study Based on the Improved Fruit Fly optimization algorithm

IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS

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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2024年第3期5卷 200-209页

作者： Sun, Yulong Ding, Guoshen Zhao, Yandong Zhang, Renchi Wang, Wenjun North Automat Control Technol Inst Software Dept Taiyuan 030006 Peoples R China

Due to the increasingly widespread application of unmanned aerial vehicle (UAV), the study of flight conflict resolution can effectively avoid the collision of different UAVs. First, describe flight conflict resolution as an optimization problem. Second, the improved fruit fly optimization algorithm (IFOA) is proposed. The smell concentration judgment is equal to the coordinate instead of the reciprocal of the distance in order to make the variable accessible to be negative and occur with equal probability in the defined domain. Next, introduce the limited number of searches of the Artificial Bee Colony algorithm to avoid falling into the local optimum. Meanwhile, generate a direction and distance of the fruit fly individual through roulette. Finally, the effectiveness of the algorithm is demonstrated by computational experiments on 18 benchmark functions and the simulation of the flight conflict resolution of two and four UAVs. The results show that compared with the standard fruit fly optimization algorithm, the IFOA has superior global convergence ability and effectively reduces the delay distance, which has important potential in flight conflict resolution.

关键词： optimization Autonomous aerial vehicles Statistics Sociology Standards Safety Military aircraft Flight conflict resolution fruit fly optimization algorithm (FOA) optimization algorithm path planning unmanned aerial vehicle (UAV)

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Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation

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INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024年第2期68卷 237-251页

作者： Sharafi, Milad Samadianfard, Saeed Behmanesh, Javad Prasad, Ramendra Urmia Univ Dept Water Engn Orumiyeh Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

关键词： Evaporation optimization algorithm Statistical analysis Water resource management

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Research on neural network optimization algorithm for building energy consumption prediction

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2018年第3期18卷 695-707页

作者： Chen, Song Ren, Ting-Ting Wu, Zhong-Cheng Chinese Acad Sci High Field Magnet Lab Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Anhui Jianzhu Univ Coll Mech & Elect Engn Hefei Anhui Peoples R China

Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized by mind evolutionary algorithm, network optimized by genetic algorithm, network optimized by particle swarm algorithm and network optimized by adaptive weight particle swarm algorithm are used to forecast the energy consumption. The optimal values of the learning rate and hidden layer node number are choosen. The characteristics of various kinds of optimization algorithm are compared. The neural network optimized by adaptive weight particle swarm algorithm is proved to be the most accurate in predicting energy consumption.

关键词： BP neural network optimization algorithm energy consumption prediction

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An ANN Based optimization algorithm for Diffracted Laser Beam Shaping

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JOURNAL OF COASTAL RESEARCH 2020年第Spe104期000卷 255-260页

作者： Li, Pengzhong Zheng, Yubiao Luo, Liang Tongji Univ Sch Mech Engn Shanghai 200092 Peoples R China Tongji Univ Sino German Sch Postgrad Studies Shanghai 200092 Peoples R China

To overcome the lack of flexibility in laser beam shaping in current industrial applications, a new improved artificial neural network algorithm for diffracted laser beam shaping is proposed. Aiming at the existing problems in laser beam shaping, the Unet neural network algorithm is improved from the label image and convolution operation. By clarifying its training and application steps, the improved neural network algorithm is pre-trained firstly and then formally trained (full training). The result shows that the UNet neural network algorithm can gradually realize the laser beam shaping with the spatial light modulator and find the mapping relationship between the input image (phase diagram) and the output image (laser contour diagram).

关键词： Diffracted laser beam shaping optimization algorithm UNet

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An optimization algorithm for estimation of microbial survival parameters during thermal processing

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INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 2012年第1-2期154卷 52-58页

作者： Chen, Guibing Campanella, Osvaldo H. N Carolina Agr & Tech State Univ Ctr Excellence Postharvest Technol Kannapolis NC 28081 USA Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA

Isothermal microbial survival curves are usually described by either linear or nonlinear time-dependent models, from which non-isothermal survival curves can be generated if the parameters describing the survival kinetics of the microbial population are known. In order to estimate these parameters, an algorithm based on the steepest decent optimization method was developed. The algorithm searches the values of the survival parameters which minimize the sum of the squared differences between the experimental data and the calculated values provided by the model. The difference of the proposed algorithm with a typical optimization technique is that each data point used is not necessarily coming from the same thermal treatment: instead, data from different non-isothermal processes can be used. The developed algorithm was tested by using published non-isothermal survival data of Salmonella. The data showed that the survival curves can be described by the Weibull model, an already accepted and frequently used nonlinear model. Salmonella's survival parameters were estimated from the end points and all data points, respectively, of three non-isothermal survival curves. The results obtained showed that the number of survival data points must be sufficiently large to obtain true or statistically sound values of the survival parameters. A suitable way to achieve this is to implement the algorithm using all data points of multiple non-isothermal survival curves or a large number of end points of non-isothermal treatments. Mathematically, the developed algorithm should be applicable to any microbial survival kinetics accurately describing the inactivation of the microorganisms because no specific survival kinetics has to be pre-assumed to run the algorithm. Published by Elsevier B.V.

关键词： optimization algorithm Microbial survival parameters Non-isothermal treatment

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