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第43届中国控制会议

作者： Yang Zhou Zihan Zhang Fan Liu Xin Chen Min Wu Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Future Technology China University of Geosciences

As drilling rate of penetration serves as a critical metric in assessing deep geological drilling, establishing an appropriate rate of penetration model holds significant importance for optimizing and controlling the drilling process. However, the intricate nonlinearity and time-series characteristic pose challenges in modeling a reliable drilling rate model, while the model parameter design exhibits high-dimensional variability. To address these issues, a new model for rate of penetration is proposed to tackle nonlinear and temporal challenges. First, the support vector regression(SVR) method was utilized to construct the rate of penetration model, and a new improved bat algorithm(IBA) was devised to determine the hyperparameters of the rate of penetration model. Subsequently, the long short-term memory neural network(LSTMNN) was used to fine-tune the model's output using time series data. The IBA underwent five enhancements aimed at improving global search capabilities, outperforming other algorithms in the IEEE CEC2005 benchmark functions. The rate of penetration model, developed based on SVR, LSTMNN, and IBA, underwent testing using actual drilling site data. The simulation results demonstrated that the developed model exhibited higher prediction accuracy compared to other methods.

关键词： Rate of penetration improved bat algorithm support vector regression long short-term memory neural network

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

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Frontiers of Environmental Science & Engineering 2023年第7期17卷 133-149页

作者： Zhaocai Wang Qingyu Wang Tunhua Wu College of Information Shanghai Ocean UniversityShanghai 201306China School of Information Engineering Wenzhou Business CollegeWenzhou 325035China

Water quality prediction is vital for solving water pollution and protecting the water *** terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term water quality prediction model was proposed based on variational mode decomposition(VMD)and improved grasshopper optimization algorithm(IGOA),so as to optimize long short-term memory neural network(LSTM).First,VMD was adopted to decompose the water quality data into a series of relatively stable components,with the aim to reduce the instability of the original data and increase the predictability,then each component was input into the iGOA-LSTM model for ***,each component was added to obtain the predicted *** this study,the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and *** experimental results showed that the prediction accuracy of the VMDIGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition(EEMD),the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Nonlinear Autoregressive network with Exogenous Inputs(NARX),Recurrent neural network(RNN),as well as other models,showing better performance in short-term *** current study will provide a reliable solution for water quality prediction studies in other areas.

关键词： Waterquality prediction Grasshopper optimization algorithm Variational mode decomposition long short-term memory neural network

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long short-term memory deep learning model for predicting the dynamic performance of automotive PEMFC system

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Energy and AI 2023年第4期14卷 304-318页

作者： Bowen Wang Zijun Yang Mingxi Ji Jing Shan Meng Ni Zhongjun Hou Jun Cai Xin Gu Xinjie Yuan Zhichao Gong Qing Du Yan Yin Kui Jiao State Key Laboratory of Engines Tianjin University135 Yaguan RdTianjin 300350China Department of Building and Real Estate Research Institute for Sustainable Urban Development(RISUD)Research Institute for Smart Energy(RISE)The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Birmingham Centre for Energy Storage University of BirminghamEdgbastonBirminghamB152TTUnited Kingdom Shanghai Hydrogen Propulsion Technology Co. Ltd.Unit 10BLDG 17Innovation ParkLane 56Antuo Rd.JiadingShanghai China National Industry-Education Platform of Energy Storage Tianjin UniversityTianjinChina

As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive ***-time prediction of output voltage and area specific resistance(ASR)via the on-board model is critical to monitor the health state of the automotive PEMFC *** this study,we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset,and a long short-term memory(LSTM)deep learning model is developed to predict the dynamic per-formance of *** results show that the developed LSTM deep learning model has much better perfor-mance than other models.A sensitivity analysis on the input features is performed,and three insensitive features are removed,that could slightly improve the prediction accuracy and significantly reduce the data *** neural structure,sequence duration,and sampling frequency are *** find that the optimal sequence data duration for predicting ASR is 5 s or 20 s,and that for predicting output voltage is 40 *** sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz,which slightly affects the prediction accuracy,but obviously reduces the data volume and computation amount.

关键词： Automotive PEMFC long short-term memory neural network Dynamic process Output voltage Area specific resistance

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Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and long short-term memory

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Energy and AI 2022年第4期10卷 95-102页

作者： Koki Iwabuchi Kenshiro Kato Daichi Watari Ittetsu Taniguchi Francky Catthoor Elham Shirazi Takao Onoye Osaka University 1-5 YamadaokaSuitaOsaka 565-0871Japan imec Kapeldeef 753001 HeverleeBelgium ESAT KULeuvenKasteelpark Arenberg 103001 HeverleeBelgium EnergyVille Dennenstraat 7B-3600 GenkBelgium University of Twente Enschede7500 AENetherlands

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by long short-term memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.

关键词： Dynamic pricing Electricity price forecast Wavelet transform long short-term memory neural network

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Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China

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Artificial Intelligence in Geosciences 2021年第1期2卷 165-170页

作者： Qi Zhang Ziwei Chen Yuan Zeng Hang Gao Qiansheng Wei Tiaoyu Luo Zhiguo Wang The Third Gas Production Plant of PetroChina Changqing Oilfield Branch WushenqiInner Mongolia017000PR China School of Mathematics and Statistics Xi'an Jiaotong UniversityXi'anShaanxi710049PR China

The Sulige tight gas field is presently the largest gas field in *** to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige,the number of production wells has exceeded 3,000,keeping the stable gas supply in the ***,the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation ***,on the basis of the three datadriven time series approaches,the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in *** jointed deep long short-term memory and fully connected neural network(DLSTM-FNN)model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest(RF)and support vector regression(SVR).After the daily production predictions of thousands of wells in Sulige,the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.

关键词： Prediction of production time series long short-term memory neural network Random forest Support vector machine

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Intelligent production optimization method for a low pressure and low productivity shale gas well

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Petroleum Exploration and Development 2022年第4期49卷 886-894页

作者： ZHU Qikang LIN Botao YANG Guang WANG Lijia CHEN Man College of Artificial Intelligence China University of PetroleumBeijing 102249China College of Safety and Ocean Engineering China University of PetroleumBeijing 102249China College of Information Science and Engineering China University of PetroleumBeijing 102249China Sichuan Shale Gas Exploration and Development Co.LTD Neijiang 641100China China National Petroleum Corporation Southwest Oil and Gas Field Company Sichuan Changning Natural Gas Development Co.Ltd Changning 644000China

Shale gas wells frequently suffer from liquid loading and insufficient formation pressure in the late stage of *** address this issue,an intelligent production optimization method for low pressure and low productivity shale gas well is *** on the artificial intelligence algorithms,this method realizes automatic production and monitoring of gas *** method can forecast the production performance of a single well by using the long short-term memory neural network and then guide gas well production accordingly,to fulfill liquid loading warning and automatic intermittent *** with adjustable nozzle,the method can keep production and pressure of gas wells stable automatically,extend normal production time of shale gas wells,enhance automatic level of well sites,and reach the goal of refined production management by making production regime for each *** tests show that wells with production regime optimized by this method increased 15%in estimated ultimate reserve(EUR).Compared with the development mode of drainage after depletion recovery,this method is more economical and can increase and stabilize production effectively,so it has a bright application prospect.

关键词： shale gas low pressure and low productivity gas well production optimization artificial intelligence long short-term memory neural network adjustable nozzle

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Adaptive Fault-tolerant Federated Filter with Fault Detection Method Based on Combination of LSTM and Chi-square Test

Adaptive Fault-tolerant Federated Filter with Fault Detectio...

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第40届中国控制会议

作者： Xuan Xiao Jiaxin Liu Beijing Institute of Technology

In order to solve the problem that the global estimation accuracy is affected by the gradual changing fault of federated filter subsystem,the features of gradual changing fault and the advantages of a long short-term memory（LSTM） neural network classification algorithm are *** this basis,a fault detection method combining residual Chi square detection algorithm with long short-term memory neural network detection method is proposed,which can effectively detect the gradual changing fault and abrupt faults of sub filters and reduce the impact of faults on global estimation *** simulation results show that this fault detection method is better than the traditional mathematical model diagnosis methods and the convolutional neural network（CNN） detection methods when the subsystem gradual changing fault occurs.

关键词： Integrated navigation fault detection long short-term memory neural network

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基于LSTM—GA混合模型的患者预约排队策略优化

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管理科学 2022年第7期

作者：魏若楠江驹徐海燕

在医疗运作管理领域，合理的资源分配能够帮助更多的患者尽早就医，降低患者病情恶化和死亡的风险。本文设计了预约排队策略对患者占有资源的顺序进行分配，建立了基于长短时记忆（long Shot term-memory，LSTM）神经网络和遗传算法（Ge... 详细信息

在医疗运作管理领域，合理的资源分配能够帮助更多的患者尽早就医，降低患者病情恶化和死亡的风险。本文设计了预约排队策略对患者占有资源的顺序进行分配，建立了基于长短时记忆（long Shot term-memory，LSTM）神经网络和遗传算法（Genetic Algorithm，GA）的混合模型以优化排队策略。首先利用大数据和深度学习分析患者到达和医院服务情况，建立LSTM神经网络学习数据特征并预测未来数据，相比于排队论常用的随机分布方法取得了更好的效果。其次设计了基于排队系统仿真的排队策略优化算法，利用改进GA得到最优排队策略。实证研究表明，文本的方法可以明显降低患者的等待时间，最高可达59%。最后对排队策略进行敏感性分析，结果表明排队策略有效作用于仿真的各个时段。

关键词：系统优化运作管理排队策略遗传算法长短时记忆神经网络 system optimization operation management queuing strategy genetic algorithm long short-term memory neural network

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