Advances and opportunities in machine learning for process data analytics

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330

Untitled work

(2011) · Cited by 5,203

A survey of industrial model predictive control technology

Qin, S. J., & Badgwell, T. A. (2003) · Control Engineering Practice · doi:10.1016/s0967-0661(02)00186-7 · Cited by 5,111

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050

Detecting influenza epidemics using search engine query data

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295

A Robust Fault Classification Method for Streaming Industrial Data Based on Wasserstein Generative Adversarial Network and Semi-Supervised Ladder Network

Zhang, C., Peng, K., Dong, J., Zhang, X., & Yang, K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3262249 · Cited by 22

Abnormal Operating Condition Identification of Industrial Processes Based on Deep Learning With Global-Local Slow Feature Analysis

Feng, Z., Li, Y., Sun, B., & Yang, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267347 · Cited by 22

Smart batch process: The evolution from 1D and 2D to new 3D perspectives in the era of Big Data

Zhou, Y., & Gao, F. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103088 · Cited by 23

Interpret what a Convolutional Neural Network learns for fault detection and diagnosis in process systems

Ye, L., Wu, H., Chen, Y., & Fei, Z. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103086 · Cited by 22

A comprehensive operating performance assessment framework based on distributed Siamese gated recurrent unit for hot strip mill process

Zhang, C., Peng, K., Dong, J., & Miao, L. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109889 · Cited by 22

A two-step multivariate statistical learning approach for batch process soft sensing

Hicks, A., Johnston, M., Mowbray, M., Barton, M., Lane, A., Mendoza, C., Martin, P. A., & Zhang, D. (2021) · Digital Chemical Engineering · doi:10.1016/j.dche.2021.100003 · Cited by 21

An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

Abualigah, L., Alomari, S. A., Almomani, M. H., Zitar, R. A., Migdady, H., Saleem, K., Smerat, A., Snåšel, V., & Ezugwu, A. E.-S. (2025) · Array · doi:10.1016/j.array.2025.100409 · Cited by 19

Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system

Chen, Z.-S., Zhu, Q., Xu, Y., He, Y., Su, Q., Liu, Y. C., & Nagy, Z. K. (2021) · Soft Computing · doi:10.1007/s00500-021-05641-4 · Cited by 19

SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes

Biegel, T., Helm, P., Jourdan, N., & Metternich, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02156-7 · Cited by 18

Variable time delay estimation in continuous industrial processes

Cattaldo, M., Ferrer, A., & Måge, I. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105082 · Cited by 18

A supervised functional Bayesian inference model with transfer-learning for performance enhancement of monitoring target batches with limited data

Liu, J., Hou, G.-Y., Shao, W., & Chen, J. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2022.12.004 · Cited by 18

Input Factor Selection Based on Interpretable Neural Network for Industrial Virtual Sensing Application

Yao, L., Yang, Z., Zhang, Z., Tang, S., Shen, B., & Zeng, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3323006 · Cited by 18

False alarm moderation for performance monitoring in industrial water distribution systems

Hashim, H., Clifford, E., & Ryan, P. C. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101592 · Cited by 18

Knowledge-data-driven process monitoring based on temporal knowledge graphs and supervised contrastive learning for complex industrial processes

Peng, K., Chen, J., Yang, H. M., & Qin, X. (2024) · Journal of Process Control · doi:10.1016/j.jprocont.2024.103283 · Cited by 17

A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing

Ahmed, Y. S., & Milani, A. S. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100591 · Cited by 18

An easy to use GUI for simulating big data using Tennessee Eastman process

Andersen, E. B., Udugama, I. A., Gernaey, K. V., Khan, A. R., Bayer, C., & Külahçı, M. (2021) · Quality and Reliability Engineering International · doi:10.1002/qre.2975 · Cited by 17

Best path in mountain environment based on parallel A* algorithm and Apache Spark

Alazzam, H., AbuAlghanam, O., & Sharieh, A. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04072-0 · Cited by 17

Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Losi, E., Venturini, M., Manservigi, L., Ceschini, G. F., Bechini, G., Cota, G., & Riguzzi, F. (2021) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4053194 · Cited by 17

Unmanned and Autonomous Systems: Future of Automation in Process and Energy Industries

Borghesan, F., Zagórowska, M., & Mercangöz, M. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.555 · Cited by 16

Domain adaptation graph convolution network for quality inferring of batch processes

Zhu, J., Jia, M., Zhang, Y., Zhou, W., Deng, H., & Liu, Y. (2023) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2023.105028 · Cited by 16

Early Children Character Education: Analysis of The Message of The Quran in Forming Human Morality

Zaitun, Z., Armita, P., & Saad, M. F. M. (2023) · Afkaruna Indonesian Interdisciplinary Journal of Islamic Studies · doi:10.18196/afkaruna.v19i2.18203 · Cited by 16

KPI-related operating performance assessment based on distributed ImRMR-KOCTA for hot strip mill process

Zhang, C., Peng, K., Dong, J., & Zhang, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118273 · Cited by 16

Integrated deep learning-production planning-economic model predictive control framework for large-scale processes. A fluid catalytic cracker-fractionator case study

Santander, O., Kuppuraj, V., Harrison, C. A., & Bâldea, M. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107977 · Cited by 16

A novel hybrid neural network for modeling dynamic systems using physics-informed regularization

Thosar, D., Bhakte, A., Li, Z., Srinivasan, R., & Prasad, V. (2025) · Journal of Process Control · doi:10.1016/j.jprocont.2025.103473 · Cited by 15

Linear hybrid models of distillation towers

Rodriguez, C., Mhaskar, P., & Mahalec, V. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108160 · Cited by 15

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

Liu, S., Lei, F., Zhao, D., & Liu, Q. (2023) · Processes · doi:10.3390/pr11061608 · Cited by 15

Classification and Diagnosis of Bioprocess Cell Growth Productions Using Early-Stage Data

Jin, Y. Q., Qin, S. J., Huang, Q., Saucedo, V. M., Li, Z., Meier, A., Kundu, S., Lehr, B., & Charaniya, S. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.9b01175 · Cited by 17

A robust meta‐method for interpreting the out‐of‐control signal of multivariate control charts using artificial neural networks

Bersimis, S., Sgora, A., & Psarakis, S. (2021) · Quality and Reliability Engineering International · doi:10.1002/qre.2955 · Cited by 15

Multivariate batch to batch optimisation of fermentation processes to improve productivity

Barton, M., Duran‐Villalobos, C. A., & Lennox, B. (2021) · Journal of Process Control · doi:10.1016/j.jprocont.2021.11.007 · Cited by 14

Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival data

Nasejje, J. B., Whata, A., & Chimedza, C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0279435 · Cited by 15

Deep Learning Model Predictive Control Frameworks: Application to a Fluid Catalytic Cracker–Fractionator Process

Santander, O., Kuppuraj, V., Harrison, C. A., & Bâldea, M. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.3c01110 · Cited by 14

A comparison of current analytical methods for detecting particulate matter and micro/nanoplastics

Thomas, C., Spatayeva, T., Yu, D., Loh, A., Yim, U. H., & Yoon, J. (2024) · Applied Physics Reviews · doi:10.1063/5.0153106 · Cited by 14

Knowledge and acceptance of artificial intelligence and its applications among the physicians working in military medical centers affiliated with Aja University: A cross-sectional study

Esfandiari, E., Kalroozi, F., Mehrabi, N., & Hosseini, Y. (2024) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_898_23 · Cited by 15

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste

Mafat, I. H., Surya, D. V., Rao, C. S., Kandya, A., & Basak, T. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123277 · Cited by 14

An Innovative Graph Neural Network Model for Detailed Effluent Prediction in Steam Cracking

Zhang, S., Li, H., & Qiu, T. (2021) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c03728 · Cited by 14

A Sustainable Portfolio Construction Model Based on ESG and Deep Learning Algorithms: Evidence from the U.S. Market

Sajadi, S. M. A., Fereydooni, A., Athari, S. A., & Farhadi, S. (2025) · Computational Economics · doi:10.1007/s10614-025-10976-3 · Cited by 13

Transductive transfer broad learning for cross-domain information exploration and multigrade soft sensor application

Zhu, J., Jia, M., Zhang, Y., Deng, H., & Liu, Y. (2023) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2023.104778 · Cited by 13

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

He, Z., Fei, Z., Lei, Y., Lin, Z., Wang, M., & Jin, G. (2020) · Polymer Engineering and Science · doi:10.1002/pen.25453 · Cited by 13

Data Protection Issues in Automated Decision-Making Systems Based on Machine Learning: Research Challenges

Christodoulou, P., & Limniotis, K. (2024) · Network · doi:10.3390/network4010005 · Cited by 13

Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach

Pilario, K. E. (2024) · Digital Chemical Engineering · doi:10.1016/j.dche.2024.100163 · Cited by 12

110th Anniversary: Commentary: The Smart Human in Smart Manufacturing

Bequette, B. W. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.9b03544 · Cited by 12

Enhancing happiness and well-being: AI-driven solutions for accessible, inclusive travel experiences for people with disabilities

Marino-Romero, J. A., Ramírez, R. R., Pavón‐Pérez, J. A., & Roets, A. O. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2488391 · Cited by 11

Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty

Francis-Xavier, F., Kubannek, F., & Schenkendorf, R. (2021) · Processes · doi:10.3390/pr9040704 · Cited by 11

Detecting flooding state in extraction columns: Convolutional neural networks vs. a white‐box approach for image‐based soft sensor development

Bayomie, O. S., de Cerqueira, R. F. L., Neuendorf, L. M., Kornijez, I., Kieling, S., Sandermann, T. H., Lammers, K., & Kockmann, N. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107904 · Cited by 11

Neural network identification of water pipe blockage from smart embedded passive acoustic measurements

Baronti, L., Castellani, M., Hefft, D. I., & Alberini, F. (2021) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.24202 · Cited by 12

Coupling ATR-FTIR spectroscopy with multivariate analysis for polymers manufacturing and control of polymers’ molecular weight

Nguyen, T., Shamsabadi, A. A., & Bavarian, M. (2023) · Digital Chemical Engineering · doi:10.1016/j.dche.2023.100089 · Cited by 10

Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

Barros, Ó., Parpot, P., Neves, I. C., & Tavares, T. (2023) · Molecules · doi:10.3390/molecules28247964 · Cited by 10

A novel zone-based machine learning approach for the prediction of the performance of industrial flares

Lou, H. H., Fang, J., Gai, H., Xu, R. Y. D., & Lin, S. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107795 · Cited by 10

A spatiotemporal synergetic operating performance assessment based on reconstructed correlation matrix and dissimilarity analytics for plant-wide hot strip mill process

Zhang, C. J., Zhang, H., Peng, K., Dong, J., Zhang, H., & Zhang, X. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2024.04.124 · Cited by 9

An interpretable chemical process fault diagnosis model: Spatiotemporal feature integration network

Guo, L., Zhang, Z., Kang, J., & Shi, J. (2025) · Chemical Engineering Science · doi:10.1016/j.ces.2025.122588 · Cited by 9

A Hybrid-driven Soft Sensor with Complex Process Data Based on DAE and Mechanism-introduced GRU

Guo, R., Liu, H., Wang, W., Xie, G., & Zhang, Y. M. (2021) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls52934.2021.9455690 · Cited by 9

Design of data-driven model for the pressurizer system in nuclear power plants using a TSK fuzzy neural network

Mahmoud, T. A., Sheta, A. A., Fikry, R. M., Ali, E. H. M., Elaraby, S. M. S., & Mahmoud, M. I. (2022) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2022.112015 · Cited by 9

Hybrid modelling of a batch separation process

Caprio, U. D., Wu, M., Elmaz, F., Wouters, Y., Vandervoort, N., Anwar, A., Mercelis, S., Waldherr, S., Hellinckx, P., & Leblebici, M. E. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108319 · Cited by 9

Machine learning methods for endocrine disrupting potential identification based on single-cell data

Aghayev, Z., Szafran, A. T., Tran, A. T., Ganesh, H. S., Stossi, F., Zhou, L. F., Mancini, M. A., Pistikopoulos, E. N., & Beykal, B. (2023) · Chemical Engineering Science · doi:10.1016/j.ces.2023.119086 · Cited by 9

Towards real time monitoring of reacting species and pH coupling electrical resistance tomography and machine learning methodologies

Alberini, F., Bezchi, D., Mannino, I. C., Paglianti, A., & Montante, G. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2021.02.024 · Cited by 9

Real-Time Fault Detection and Diagnosis of CaCO3 Reactive Crystallization Process by Electrical Resistance Tomography Measurements

Aghajanian, S., Rao, G., Ruuskanen, V., Wajman, R., Jackowska-Strumiłło, L., & Koiranen, T. (2021) · Sensors · doi:10.3390/s21216958 · Cited by 9

Data augmentation driven by optimization for membrane separation process synthesis

Addis, B., Castel, C., Macali, A., Misener, R., & Piccialli, V. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108342 · Cited by 10

Latent Variable Regression for Process and Quality Modeling

Zhu, Q., & Qin, S. J. (2019) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iciai.2019.8850772 · Cited by 9

Prioritized fault detection and diagnosis in chemical industry using production loss-guided cost matrix with self-attention mechanism

Bardeeniz, S., Panjapornpon, C., Jitchaiyapoom, T., Wong, D. S., & Yao, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111391 · Cited by 8

Assessing machine learning adoption at the firm level: the moderating effect of the environmental context

Filipe, P., Ruivo, P., & Oliveira, T. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.381 · Cited by 11

Transformation of Students' Career Orientation in the Era of Artificial Intelligence: A Systematic Literature Review

Yupelmi, M., Ganefri, Giatman, M., Krismadinata, K., & Syah, N. (2024) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v13i3.4078 · Cited by 8

A Framework for Identifying and Prioritizing Data Analytics opportunities in Additive Manufacturing

Park, H., Ko, H., Lee, Y.-T. T., Cho, H., & Witherell, P. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006489 · Cited by 8

Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Li, Y., Ma, F., Ji, C., Wang, J., & Sun, W. H. (2022) · Processes · doi:10.3390/pr10010122 · Cited by 8

Process safety consequence modeling using artificial neural networks for approximating heat exchanger overpressure severity

Harhara, A., Arora, A., & Hasan, M. M. F. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108098 · Cited by 8

Spatial weighted graph-driven fault diagnosis of complex process industry considering technological process flow

Zhang, F., Liu, J., Lu, X. H., Li, T., Li, Y., Liu, Y., Tang, L., & Hu, W. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acf665 · Cited by 8

What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

Martínez-Ramón, J. P., & Rodríguez, F. M. M. (2020) · Sustainability · doi:10.3390/su12187396 · Cited by 9

A multi-objective optimization based deep feature multi-subspace partitioning method for process monitoring

Li, Z., Tian, L., & Yan, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120097 · Cited by 7

Integration of chemical process operation with energy, global market, and plant systems infrastructure

Flores‐Cerrillo, J., Swartz, C. L. E., Kumar, A., & Dering, D. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108566 · Cited by 7

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

Cobre, A. D. F., Alves, A. C., Gotine, A. R. M., Domingues, K. Z. A., Lazo, R. E. L., Ferreira, L. M., Tonin, F. S., & Pontarolo, R. (2024) · Internal and Emergency Medicine · doi:10.1007/s11739-024-03547-1 · Cited by 7

Explainable proactive control of industrial processes

Kuk, E., Bobek, S., & Nalepa, G. J. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102329 · Cited by 7

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Li, Y., Jin, H., Dong, S., Yang, B., & Chen, X. (2021) · Sensors · doi:10.3390/s21248471 · Cited by 7

Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

Losi, E., Venturini, M., Manservigi, L., Ceschini, G. F., Bechini, G., Cota, G., & Riguzzi, F. (2021) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4049503 · Cited by 7

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Losi, E., Venturini, M., Manservigi, L., & Bechini, G. (2022) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4055905 · Cited by 7

Bayesian hierarchical modeling for online process monitoring and quality control, with application to real time image data

Radcliffe, A. J., & Reklaitis, G. V. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107446 · Cited by 7

Applications of Machine Learning to Optimizing Polyolefin Manufacturing

Liu, Y. A., & Sharma, N. (2023) · Wiley eBooks · doi:10.1002/9783527843831.ch10 · Cited by 7

Feature transfer projection with low-rank and dual graph embedding for multimode process monitoring

Xu, X., Yang, C., Meng, X., Fu, Y., Luo, C., & Xia, C. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.107272 · Cited by 6

A novel white component analysis for dynamic process monitoring

Huo, K., Huang, D., & Shang, C. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.102998 · Cited by 6

New Recruitment Approach Based on Logistic Regression Model

Almajed, I. H. A., Nassreddine, G., & Younis, J. A. (2024) · Mesopotamian Journal of Computer Science · doi:10.58496/mjcsc/2024/002 · Cited by 6

The BowTie as a Digital Twin: How a BowTie Looks Different from a Data Perspective

Singh, P. S., van Gulijk, C., & Sunderland, N. (2024) · Safety · doi:10.3390/safety10020034 · Cited by 6

Catalyst development for the tri‐reforming of methane ( TRM ) process by integrated singular machine learning models

de Souza, P. A. L., Afzal, R. M., Camacho, F. G., & Mahinpey, N. (2024) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25397 · Cited by 6

Accuracy of COVID-19 diagnostic tests via infrared spectroscopy: A systematic review and meta-analysis

Cobre, A. D. F., Fachi, M. M., Domingues, K. Z. A., Lazo, R. E. L., Ferreira, L. M., Tonin, F. S., & Pontarolo, R. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.125337 · Cited by 6

Determining robust reaction kinetics from limited data

Ozbuyukkaya, G., Parker, R. S., & Veser, G. (2021) · AIChE Journal · doi:10.1002/aic.17538 · Cited by 6

Global-local based wavelet functional principal component analysis for fault detection and diagnosis in batch processes

Liu, J., Wang, D., & Chen, J. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104279 · Cited by 6

Supervised functional modeling method for long durations of batch processes with limited batch data

Liu, J., Hou, G.-Y., & Chen, J. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.116991 · Cited by 6

Multiscale theoretical tools for in silico macromolecular chemistry and engineering

Edeleva, M., Arráez, F. J., Wu, Y., Xie, L., Figueira, F. L., Marien, Y. W., Zhou, Y., Luo, Z., Steenberge, P. H. M. V., & D’hooge, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90995-2.00012-6 · Cited by 6

A scale-variable attention transformer for industrial process fault diagnosis

Chen, W., Ye, L., Fang, X., Guo, H., & Li, J. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109471 · Cited by 5

A topology model based on common and specific feature separation for multimode process monitoring

Tan, S., Wang, Y., Shi, H., Song, B., & Tao, Y. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103052 · Cited by 5

Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering

Dowling, A. W. (2024) · Systems and Control Transactions · doi:10.69997/sct.114705 · Cited by 5

Linear Chain Conditional Random Field for Operating Mode Identification and Multimode Process Monitoring

Wang, F. (2022) · ACS Omega · doi:10.1021/acsomega.2c04005 · Cited by 5

A Hybrid Metaheuristic Model for Efficient Analytical Business Prediction

Elveny, M., Nasution, M. K. M., & Syah, R. B. Y. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140848 · Cited by 8

An inductive transfer regression framework for small sample modeling in power plants

Zhu, B., Ren, S., Wang, P., & Si, F. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2024.08.020 · Cited by 6

Artificial Intelligence in the chemical industry—why my industry puzzles over our vendors' struggles

Colegrove, L. F. (2020) · Journal of Advanced Manufacturing and Processing · doi:10.1002/amp2.10052 · Cited by 5

CACHE/ASEE Survey on Computing in Chemical Engineering

Hesketh, R. P., Grover, M. A., & Silverstein, D. L. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--34249 · Cited by 5

Application of Machine Learning for Product Batch Oriented Control of Production Processes

Meiners, M., Mayr, A., Thomsen, M., & Franke, J. E. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.006 · Cited by 5

Financial Distress Analysis Using Altman Z-Score, Springate and Zmijewski in Retail Companies on the IDX

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