{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:14:49Z","timestamp":1774494889165,"version":"3.50.1"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compchemeng.2026.109616","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:16:54Z","timestamp":1772558214000},"page":"109616","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Quality-aware adversarial disentanglement for soft sensor modeling in multimodal processes"],"prefix":"10.1016","volume":"210","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2779-6059","authenticated-orcid":false,"given":"Jingyun","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5605-6820","authenticated-orcid":false,"given":"Chenghui","family":"Mo","sequence":"additional","affiliation":[]},{"given":"Kexin","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9940-2466","authenticated-orcid":false,"given":"Xinzhu","family":"Lin","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.compchemeng.2026.109616_bib0024","doi-asserted-by":"crossref","first-page":"3962","DOI":"10.1109\/TIM.2019.2943824","article-title":"Multivariate regression model for industrial process measurement based on double locally weighted partial least squares","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0026","first-page":"1","article-title":"Soft sensor enhancement for multimodal industrial process data: meta regression gaussian mixture variational autoencoder","volume":"73","author":"Chen","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0018","doi-asserted-by":"crossref","first-page":"35864","DOI":"10.1109\/ACCESS.2020.2974783","article-title":"Soft sensor modeling for unobserved multimode nonlinear processes based on modified kernel partial least squares with latent factor clustering","volume":"8","author":"Deng","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compchemeng.2026.109616_bib0016","first-page":"1","article-title":"Soft sensor development based on unsupervised dynamic weighted domain adaptation for quality prediction of batch processes","volume":"73","author":"Jin","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0020","first-page":"1","article-title":"Soft sensor development based on unsupervised dynamic weighted domain adaptation for quality prediction of batch processes","volume":"73","author":"Jin","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0027","doi-asserted-by":"crossref","first-page":"205863","DOI":"10.1109\/ACCESS.2020.3037730","article-title":"Improved Process monitoring strategy using kantorovich distance-independent component analysis: an application to tennessee eastman process","volume":"8","author":"Kini","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compchemeng.2026.109616_bib0002","first-page":"1","article-title":"Local neuron analysis for monitoring dynamic nonlinear batch processes","volume":"74","author":"Li","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"9","key":"10.1016\/j.compchemeng.2026.109616_bib0012","doi-asserted-by":"crossref","first-page":"15588","DOI":"10.1109\/JSEN.2025.3549494","article-title":"Soft sensor development based on hybrid modeling with ensemble learning for multimode batch processes","volume":"25","author":"Li","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0010","first-page":"1","article-title":"A novel long short-term complementary memory network based on temporal distribution matching for soft sensing in industrial processes","volume":"75","author":"Liao","year":"2026","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"10.1016\/j.compchemeng.2026.109616_bib0021","doi-asserted-by":"crossref","first-page":"8003","DOI":"10.1109\/TII.2021.3058426","article-title":"Model-agnostic meta-learning with optimal alternative scaling value and its application to industrial soft sensing","volume":"17","author":"Lu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109616_bib0008","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1109\/JSEN.2025.3638130","article-title":"Edge-compressed and energy-optimized dual-domain graph network with variational gating for industrial soft sensing","volume":"26","author":"Mao","year":"2026","journal-title":"IEEE Sens. J."},{"issue":"9","key":"10.1016\/j.compchemeng.2026.109616_bib0022","doi-asserted-by":"crossref","first-page":"10231","DOI":"10.1109\/JSEN.2023.3261330","article-title":"Mode information separated \u03b2-VAE regression for multimode industrial process soft sensing","volume":"23","author":"Shen","year":"2023","journal-title":"IEEE Sens. J."},{"issue":"23","key":"10.1016\/j.compchemeng.2026.109616_bib0015","doi-asserted-by":"crossref","first-page":"39352","DOI":"10.1109\/JSEN.2024.3475417","article-title":"Soft sensor based on causal secondary variable selection for multimode batch processes","volume":"24","author":"Sui","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0019","first-page":"1","article-title":"Development of soft sensor based on sequential kernel fuzzy partitioning and just-in-time relevance vector machine for multiphase batch processes","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"10.1016\/j.compchemeng.2026.109616_bib0004","doi-asserted-by":"crossref","first-page":"9846","DOI":"10.1109\/TII.2025.3609103","article-title":"Adaptive wavelet normalization network for soft sensor modeling in nonstationary industrial processes","volume":"21","author":"Wang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109616_bib0011","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/TII.2024.3495779","article-title":"Deep spatial\u2013temporal slow feature transfer network for multimode chemical process soft sensing on imbalanced data","volume":"21","author":"Wang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"4","key":"10.1016\/j.compchemeng.2026.109616_bib0014","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1109\/TASE.2021.3138925","article-title":"Data-driven fault detection in industrial batch processes based on a stochastic hybrid process model","volume":"19","author":"Windmann","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109616_bib0007","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/TII.2024.3475419","article-title":"DAMPNN: Dynamic adaptive message passing neural network for industrial soft sensor","volume":"21","author":"Yan","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109616_bib0001","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TII.2024.3488777","article-title":"A difference metric attention with position distance-based weighting for transformer in data sequence modeling of industrial processes","volume":"21","author":"Yang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"8","key":"10.1016\/j.compchemeng.2026.109616_bib0005","doi-asserted-by":"crossref","first-page":"5811","DOI":"10.1109\/TII.2025.3547031","article-title":"Gaussian-based interval-aware transformer with interval embedding for data sequence modeling with irregular sampling frequency in industrial processes","volume":"21","author":"Yang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"4","key":"10.1016\/j.compchemeng.2026.109616_bib0003","doi-asserted-by":"crossref","first-page":"5253","DOI":"10.1109\/TII.2023.3329684","article-title":"Attention-based interval aided networks for data modeling of heterogeneous sampling sequences with missing values in process industry","volume":"20","author":"Yuan","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109616_bib0009","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TII.2024.3507937","article-title":"A Domain-knowledge embedded framework for soft sensing in complex industrial processes with cascading equipment","volume":"21","author":"Yue","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0025","doi-asserted-by":"crossref","first-page":"118749","DOI":"10.1109\/ACCESS.2019.2936542","article-title":"Soft sensor model development for cobalt oxalate synthesis process based on adaptive gaussian mixture regression","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"issue":"7","key":"10.1016\/j.compchemeng.2026.109616_bib0017","doi-asserted-by":"crossref","first-page":"4654","DOI":"10.1109\/TII.2021.3120509","article-title":"Domain adaptation mixture of gaussian processes for online soft sensor modeling of multimode processes when sensor degradation occurs","volume":"18","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"5","key":"10.1016\/j.compchemeng.2026.109616_bib0023","doi-asserted-by":"crossref","first-page":"3363","DOI":"10.1109\/TSMC.2025.3540028","article-title":"Deep co-training partial least squares model for semi-supervised industrial soft sensing","volume":"55","author":"Zheng","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern.: Syst."},{"issue":"7","key":"10.1016\/j.compchemeng.2026.109616_bib0006","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/TFUZZ.2025.3562333","article-title":"Fuzzy hierarchical stochastic configuration networks for industrial soft sensor modeling","volume":"33","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.compchemeng.2026.109616_bib0013","first-page":"1","article-title":"Multidimensional attention-based GAIN for missing data imputation in multimode batch processes","volume":"74","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426000694?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426000694?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:12:02Z","timestamp":1774491122000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426000694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":27,"alternative-id":["S0098135426000694"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109616","relation":{"is-supplemented-by":[{"id-type":"uri","id":"https:\/\/github.com\/lichen0102\/multi-mode-fault-diagnosis-datasets-with-te-process","asserted-by":"subject"}]},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quality-aware adversarial disentanglement for soft sensor modeling in multimodal processes","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109616","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109616"}}