{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:43:48Z","timestamp":1769935428832,"version":"3.49.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002862","name":"China University of Petroleum Beijing","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002862","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Chemical Engineering"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1016\/j.compchemeng.2022.107733","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T11:16:44Z","timestamp":1644837404000},"page":"107733","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling"],"prefix":"10.1016","volume":"160","author":[{"given":"Chang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8815-3877","authenticated-orcid":false,"given":"Jianqin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongtu","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wencai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.compchemeng.2022.107733_bib0001","first-page":"854","article-title":"Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT. Engineering Science and Technology","volume":"22","author":"Altobi","year":"2019","journal-title":"Int. J."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2020.106457","article-title":"Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence","volume":"125","author":"Bakdi","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106786","article-title":"Multiclass data classification using fault detection-based techniques","volume":"136","author":"Basha","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0004","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.psep.2021.05.017","article-title":"Numerical analysis of multi-factors effects on the leakage and gas diffusion of gas drainage pipeline in underground coal mines","volume":"151","author":"Cai","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108558","article-title":"Research on fault diagnosis technology of centrifugal pump blade crack based on PCA and GMM","volume":"173","author":"Cao","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0006","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.compchemeng.2015.04.027","article-title":"Data clustering for model-prediction discrepancy reduction \u2013 A case study of solids transport in oil\/gas pipelines","volume":"81","author":"Cremaschi","year":"2015","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.108747","article-title":"Detection of two-point leakages in a pipeline based on lab investigation and numerical simulation","volume":"204","author":"Fu","year":"2021","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0009","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.neucom.2020.12.006","article-title":"RNN-transducer based Chinese sign language recognition","volume":"434","author":"Gao","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115511","article-title":"DeepYield: a combined convolutional neural network with long short-term memory for crop yield forecasting","volume":"184","author":"Gavahi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0011","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neucom.2021.07.040","article-title":"Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts","volume":"461","author":"Hameed","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120153","article-title":"Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression","volume":"224","author":"Hu","year":"2021","journal-title":"Energy"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0013","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.neucom.2019.05.020","article-title":"Status detection from spatial-temporal data in pipeline network using data transformation convolutional neural network","volume":"358","author":"Hu","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0014","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2020.11.009","article-title":"Minor class-based status detection for pipeline network using enhanced generative adversarial networks","volume":"424","author":"Hu","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0015","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1007\/s11269-015-1053-4","article-title":"An Optimization Approach to Leak Detection in Pipe Networks Using Simulated Annealing","volume":"29","author":"Huang","year":"2015","journal-title":"Water Resour. Manage."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2021.109046","article-title":"Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks","volume":"232","author":"J\u00f6rges","year":"2021","journal-title":"Ocean Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0017","doi-asserted-by":"crossref","unstructured":"Kang, J., Park, Y.-J., Lee, J., Wang, S.-H., Eom, D.-S. (2017). Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Transactions on Industrial Electronics, PP, 4279\u20134289.","DOI":"10.1109\/TIE.2017.2764861"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0018","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.measurement.2018.05.053","article-title":"Boosting performance of power quality event identification with KL Divergence measure and standard deviation","volume":"126","author":"Kapoor","year":"2018","journal-title":"Measurement"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2020.107399","article-title":"Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images","volume":"167","author":"Kumar","year":"2020","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.107132","article-title":"Active metric learning for supervised classification","volume":"144","author":"Kumaran","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0021","series-title":"2008 10th International Conference on Control, Automation, Robotics and Vision","first-page":"1178","article-title":"A new failure detection method and its application in leak monitor of pipeline","author":"Li","year":"2008"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115008","article-title":"Study on strategy of CT image sequence segmentation for liver and tumor based on U-Net and Bi-ConvLSTM","volume":"180","author":"Li","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0023","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jprocont.2019.03.012","article-title":"Constrained multimodal ensemble Kalman filter based on Kullback\u2013Leibler (KL) divergence","volume":"79","author":"Li","year":"2019","journal-title":"J. Process Control"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106843","article-title":"Semi-supervised process fault classification based on convolutional ladder network with local and global feature fusion","volume":"140","author":"Li","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2021.107760","article-title":"Scheduling of a branched multiproduct pipeline system with robust inventory management","volume":"162","author":"Li","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107438","article-title":"Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network","volume":"209","author":"Liu","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0027","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.chaos.2016.09.002","article-title":"Chaos characteristics and least squares support vector machines based online pipeline small leakages detection","volume":"91","author":"Liu","year":"2016","journal-title":"Chaos Soliton. Fract."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2019.103249","article-title":"Leakage detection techniques for oil and gas pipelines: state-of-the-art","volume":"98","author":"Lu","year":"2020","journal-title":"Tunnel. Underground Space Technol."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102279","article-title":"Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models","volume":"64","author":"Lu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.jsv.2020.115447","article-title":"Feasibility study on wheelset fatigue damage with NOFRFs-KL divergence detection method in SIMO","volume":"483","author":"Mao","year":"2020","journal-title":"J. Sound Vib."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0031","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.petrol.2013.08.007","article-title":"Modeling of incompressible flow in short pipes with leaks","volume":"109","author":"Molina-Espinosa","year":"2013","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0033","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.compchemeng.2010.10.011","article-title":"Leak detection in gas pipeline networks using an efficient state estimator. Part II. Experimental and field evaluation","volume":"35","author":"Reddy","year":"2011","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0034","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.measurement.2014.05.012","article-title":"Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM","volume":"55","author":"Sun","year":"2014","journal-title":"Measurement"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0035","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.psep.2019.05.030","article-title":"Investigation on underwater spreading characteristics and migration law of oil leakage from damaged submarine pipelines","volume":"127","author":"Sun","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0036","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2020.05.121","article-title":"Interactive prostate MR image segmentation based on ConvLSTMs and GGNN","volume":"438","author":"Tian","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0037","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.procs.2017.08.239","article-title":"Enhancing assessment of personalized multi-agent system through ConvLSTM","volume":"112","author":"Trifa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0038","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compchemeng.2018.04.008","article-title":"A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development","volume":"115","author":"Wang","year":"2018","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0039","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2020.04.105","article-title":"An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection","volume":"403","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2020.112766","article-title":"A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework","volume":"212","author":"Wang","year":"2020","journal-title":"Energy Convers. Manage."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0041","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2017.01.062","article-title":"Multi-view metric learning based on KL-divergence for similarity measurement","volume":"238","author":"Wang","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2021.107531","article-title":"Identification of key classification features of early cervical squamous cell carcinoma","volume":"93","author":"Wang","year":"2021","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0043","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.eswa.2019.01.005","article-title":"Clustering of interval-valued time series of unequal length based on improved dynamic time warping","volume":"125","author":"Wang","year":"2019","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0044","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.cherd.2020.10.022","article-title":"An efficient decomposition approach for the low-energy scheduling of a straight multiproduct pipeline","volume":"165","author":"Xu","year":"2021","journal-title":"Chem. Eng. Res. Des."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0045","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.knosys.2019.03.013","article-title":"Predicting pipeline leakage in petrochemical system through GAN and LSTM","volume":"175","author":"Xu","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107494","article-title":"An Effective Decomposition Algorithm for Scheduling Branched Multiproduct Pipelines","author":"Yan","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0047","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.proeng.2014.10.517","article-title":"Research of emergency venting time in natural gas pipeline accidents with SPS","volume":"84","author":"Yi","year":"2014","journal-title":"Procedia Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0048","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.jlp.2016.06.018","article-title":"Pipeline leakage detection and isolation: an integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)","volume":"43","author":"Zadkarami","year":"2016","journal-title":"J. Loss Prev. Process Ind."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109960","article-title":"Pipeline small leak detection based on virtual sample generation and unified feature extraction","volume":"184","author":"Zang","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0050","article-title":"Improved PSO-Based Method for Leak Detection and Localization in Liquid Pipelines","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0051","doi-asserted-by":"crossref","first-page":"3488","DOI":"10.1016\/j.egyr.2021.06.011","article-title":"A self-adaptive deep learning algorithm for intelligent natural gas pipeline control","volume":"7","author":"Zhang","year":"2021","journal-title":"Energy Rep."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0052","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.neucom.2021.07.072","article-title":"Conciseness is better: recurrent attention LSTM model for document-level sentiment analysis","volume":"462","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.jngse.2021.104136","article-title":"Effect of accurate prediction of real-time crack tip position on dynamic crack behaviors in gas pipeline","volume":"94","author":"Zhen","year":"2021","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2020.100741","article-title":"Experimental analysis of the effect of nitrogen gas on the H2S stripping process during the pigging operation of a long crude oil pipeline","volume":"22","author":"Zheng","year":"2020","journal-title":"Case Stud. Therm. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcip.2020.100389","article-title":"An online real-time estimation tool of leakage parameters for hazardous liquid pipelines","volume":"31","author":"Zheng","year":"2020","journal-title":"Int. J. Crit. Infrastruct. Prot."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0056","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.psep.2021.04.031","article-title":"Deeppipe: a semi-supervised learning for operating condition recognition of multi-product pipelines","volume":"150","author":"Zheng","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0057","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.psep.2021.09.046","article-title":"Deeppipe: theory-guided LSTM method for monitoring pressure after multi-product pipeline shutdown","volume":"155","author":"Zheng","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107290","article-title":"Deeppipe: a customized generative model for estimations of liquid pipeline leakage parameters","volume":"149","author":"Zheng","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2019.114001","article-title":"Time series prediction for output of multi-region solar power plants","volume":"257","author":"Zheng","year":"2020","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0060","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jclepro.2019.01.032","article-title":"A voyage with minimal fuel consumption for cruise ships","volume":"215","author":"Zheng","year":"2019","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.compchemeng.2022.107733_bib0061","article-title":"Leak detection and location based on ISLMD and CNN in a pipeline","author":"Zhou","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.compchemeng.2022.107733_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2019.106543","article-title":"A MILP model for the detailed scheduling of multiproduct pipelines with the hydraulic constraints rigorously considered","volume":"130","author":"Zhou","year":"2019","journal-title":"Comput. Chem. Eng."}],"container-title":["Computers & Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135422000746?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135422000746?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T18:00:08Z","timestamp":1758823208000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135422000746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":60,"alternative-id":["S0098135422000746"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2022.107733","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2022,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2022.107733","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107733"}}