- Position:
- Researcher
PhD student - Department:
- Department of Information Engineering and Process Control (DIEPC)
- Room:
- NB 647
- eMail:
- Home page:
- https://www.uiam.sk/~mojto
- Phone:
- +421 259 325 349
- ORCID iD:
- 0000-0002-6114-2710
- WoS ResearcherID:
- AAZ-3608-2020
- Google Scholar:
- sjBbi0AAAAAJ
- Availability:
Citations
- Total citations 19
R. Fáber – M. Mojto – K. Ľubušký – R. Paulen: Integrated Data Analytics and Regression Techniques for Real-time Anomaly Detection in Industrial Processes. In 12th IFAC Symposium on Advanced Control of Chemical Processes, pp. 320–325, 2024. Zenodo
- Number of citations 1
- Ren, Yinan – Di, Ruohai – Wang, Peng – Li, Xiaoyan – Li, Liangliang – Li, Jianheng – Zhang, Huan – Zhang, Xinlan: Creep Data Generation Method Based on Physically Constrained Variational Self-encoder. pp. 1491 – 1495, 2025.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-Based Design of Multi-Model Inferential Sensors. Computers & Chemical Engineering, vol. 178, 2023. arXiv Zenodo
- Number of citations 2
- Wang, Feng – Zhao, Hui – Li, Xiaozhi – Bian, Jing: Monitoring and early warning method for abnormal conditions in complex processes based on bidirectional causal reasoning and its application in diesel hydrotreating plants. Journal of Loss Prevention in the Process Industries, no. 105771, vol. 99, 2026.
- Sezer, Emine – Dokuzparmak, Emre – Ozcelik, Hilal – Yasar, Esra – Kaya, Tarik – Guner, Timucin – Akgol, Sinan: Harnessing Machine Learning to Revolutionize Electrochemical Detection of Vitamin E Acetate in E-Liquids. Acs Omega, no. 25, vol. 10, pp. 27098-27111, 2025.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-Driven Indication of Flooding in an Industrial Debutanizer Column. Editor(s): Antonis Kokossis, Michael C. Georgiadis, Efstratios N. Pistikopoulos, In 33rd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 33, pp. 1001–1006, 2023. Zenodo
- Number of citations 1
- Abdullah, A.S. – Ayoob, H.W. – Homod, R.Z. – Mohammed, H.I.: Enhancing liquefied petroleum gas production through debutanizer column optimization. Chemical Engineering Research and Design, vol. 206, pp. 242-250, 2024.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Support Vector Machine-based Design of Multi-model Inferential Sensors. Editor(s): Ludovic Montastruc, Stephane Negny, In 32nd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 32, pp. 1045–1050, 2022.
- Number of citations 2
- Kappatou, Chrysoula D. – Odgers, James – García-Muñoz, Salvador – Misener, Ruth: An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics. Industrial and Engineering Chemistry Research, 2022.
- Liu, T. – Chen, S. – Yang, P. – Zhu, Y. – Mercangoz, M. – Harris, C.J.: Lifelong Learning Meets Dynamic Processes: An Emerging Streaming Process Prediction Framework With Delayed Process Output Measurement. IEEE Transactions on Control Systems Technology, no. 2, vol. 32, pp. 384-398, 2024.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, vol. 153, pp. 107437, 2021. arXiv
- Number of citations 9
- Sanseverinatti, Carlos I. – Perdomo, Mariano M. – Clementi, Luis A. – Vega, Jorge R.: An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process. Macromolecular Reaction Engineering, 2023.
- Ikonen, Teemu J. – Bergman, Samuli – Corona, Francesco: A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process. Chemometrics and Intelligent Laboratory Systems, vol. 241, pp. 104942, 2023.
- Dai, Siyang – Cao, Deshun – Li, Na – Guo, Yian – Wang, Hao: Multiphysics modeling and experimental analysis of corrosion-assisted degradation in industrial pressure transducer packages under thermomechanical fatigue. Materials Chemistry and Physics, no. 129811, vol. 326, 2024.
- Fu, Anqi – An, Tianyu – Li, Wenjing – Qiao, Junfei: An Att-LSTM based soft sensor for wastewater BOD prediction with local explanation and global feature selection using LIME. Engineering Research Express, no. 4, vol. 7, 2025.
- Snegirev, Oleg – Klimchenko, Vladimir – Shtakin, Denis – Torgashov, Andrei – Yang, Fan: Multivariable soft sensor with a predictor of mutually dependent errors applied to an industrial fractionator. Journal of Process Control, no. 103555, vol. 155, 2025.
- Wang, Feng – Zhao, Hui – Li, Xiaozhi – Bian, Jing: Monitoring and early warning method for abnormal conditions in complex processes based on bidirectional causal reasoning and its application in diesel hydrotreating plants. Journal of Loss Prevention in the Process Industries, no. 105771, vol. 99, 2026.
- Wang, Ziyuan – Liu, Yishun – Yang, Chunhua: Spatiotemporal uncertainty-aware predictive control for industrial distributed parameters systems. Computers & Chemical Engineering, no. 109307, vol. 202, 2025.
- Pani, Ajaya Kumar – Soofastaei, Ali: Designing intelligence: Harnessing soft sensors and advanced analytics in petroleum refining for Industry 4.0, 2025.
- Shahid, Muhammad – Zabiri, Haslinda – Taqvi, Syed Ali Ammar: An embedded KPI-based advisory framework for monitoring and diagnosis of soft sensor degradation. Results in Engineering, vol. 27, 2025.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based Industrial Soft-sensor Design via Optimal Subset Selection. Editor(s): Metin Türkay, Rafiqul Gani, In 31st European Symposium on Computer Aided Process Engineering, Elsevier, vol. 31, pp. 1247–1252, 2021.
- Number of citations 2
- Shan, Baoming – Ma, Cuncheng – Niu, Chengqun – Xu, Qilei – Zhu, Zhaoyou – Wang, Yinglong – Zhang, Fangkun: Soft sensor model predictive control for azeotropic distillation of the separation of DIPE/IPA/water mixture. Journal of the Taiwan Institute of Chemical Engineers, vol. 152, 2023.
- Sildir, H. – Boy, O.C. – Sarrafi, S.: A Mixed-Integer Formulation for the Simultaneous Input Selection and Outlier Filtering in Soft Sensor Training. Information Systems Frontiers, 2024.
M. Furka – K. Kiš – M. Horváthová – M. Mojto – M. Bakošová: Identification and Control of a Cascade of Biochemical Reactors. In 2020 Cybernetics & Informatics (K&I), 2020.
- Number of citations 2
- Sunori, S.K. – Ajay Joshi, K. – Arora, S. – Mohan Sati, M. – Khan, F. – Mittal, A. – Juneja, P.: Model Identification and Controller Simulation for Distillation Column. In 5th International Conference on Inventive Computation Technologies, ICICT 2022 - Proceedings, pp. 545-550, 2022.
- Sunori, S.K. – Manu, M. – Arora, S. – Agarwal, P. – Mittal, A. – Juneja, P.: MPC and Fuzzy Logic Controllers Design for Biochemical Reactor Plant. In Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, pp. 1694-1698, 2022.
