Mattia Vallerio

@polimi.it

Tenure Track Assistant Professor - Departimento di Chimica, Materiali e Ingengerica Chimica "Giulio Natta"
Politecnico di Milano

Mattia Vallerio


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https://researchid.co/mvallerio

RESEARCH, TEACHING, or OTHER INTERESTS

Chemical Engineering, Artificial Intelligence, Control and Optimization, Industrial and Manufacturing Engineering
38

Scopus Publications

767

Scholar Citations

13

Scholar h-index

14

Scholar i10-index

Scopus Publications

  • Dynamic SHAP: Time resolved explainable AI for batch processes
    Luca Brunaccioni, Francesco de Fusco, Mattia Vallerio
    Computers and Chemical Engineering, 2026
    • Introduces Dynamic SHAP (D-SHAP) for time-resolved interpretability of batch-process ML models. • D-SHAP reveals when and which variables most influence product quality, closing the accuracy–explainability gap. • Methodology validated on one industrial and two synthetic datasets, confirming broad applicability.
  • Integrated valorization of algae residue and oily sludge into hydrogen and liquid biofuels: detailed process simulation and preliminary operational assessment
    Marcello Maria Bozzini, Francesco de Fusco, Nuria Ferrera-Lorenzo, Mattia Vallerio, Flavio Manenti
    Renewable Energy, 2026
  • Optimal Design of Acid Gas-to-Syngas (AG2S) Technology: Process Optimization and Surrogate Modeling
    Simone Caspani, Luis Felipe Sánchez, Mattia Vallerio, Flavio Manenti
    Industrial and Engineering Chemistry Research, 2026
    High Resolution Image Download MS PowerPoint Slide The management of acid gases (i.e., H 2 S and CO 2 ) is a fundamental requirement in process plants, such as refineries. Current strategies typically do not exploit the hydrogen content in hydrogen sulfide, which is usually burned. The acid gas-to-syngas (AG2S) technology represents an innovative approach for converting a mixture of hydrogen sulfide (H 2 S) and carbon dioxide (CO 2 ) into syngas (H 2 and CO), with potential applications in fuels and chemical synthesis. This work investigates the optimal process design of AG2S in terms of the H 2 S/CO 2 and H 2 S/O 2 feed molar ratios to maximize syngas production. The study combines detailed kinetic and thermodynamic modeling to obtain a comprehensive process simulation, which serves as the basis for generating accurate surrogate models trained on flowsheet simulation data via a design of experiments (DoE) approach. These models allow for a reliable prediction of H 2 S conversion, syngas flow rate, H 2 /CO ratio, and selectivity. The results highlight limitations imposed by relatively low H 2 /CO ratios for downstream applications and illustrate the trade-off between syngas quality and quantity.
  • All you need is noise — from feature selection to explainable industrial AI
    Mattia Vallerio, Antonio del Rio Chanona, Francisco J. Navarro-Brull
    Digital Chemical Engineering, 2026
    Modern chemical plants record thousands of sensor tags, yet only a small fraction meaningfully influence yield, quality, or safety. Identifying those key drivers is often more difficult than building the predictive model itself. In this work, we show that appending one or more Synthetic Noise Features (SNFs), non-informative random variables known a priori , provide a simple reference for judging variable relevance. We show the impact of this model agnostic step across three workflows. In supervised learning, noise features establish an automatic cutoff for the feature importance, guide model regularization and signal when the dataset itself lacks predictive information. In unsupervised learning, they provide an unbiased threshold preventing spurious anomalies and latent dimensions. Finally, we demonstrate the applicability of this approach to small datasets typical of experimental work and Design of Experiments (DoE), including Definitive Screening , Response Surface , and space-filling designs, as well as active learning using Bayesian optimization . By turning nothing but noise into a quantitative benchmark, SNFs offer an immediately deployable safeguard against overfitting and misplaced experimental effort in data-driven chemical engineering. • Synthetic Noise Features (SNFs) give a single, model-agnostic rule for variable relevance in supervised, unsupervised, and adaptive-learning workflows. • SNF thresholds act as a penalization signal to prune overgrown trees and scale back neural networks. • In unsupervised learning, SNFs flag noise-dominated latent variables and drive dimensionality reduction for anomaly detection models. • Coupling an SNF with Gaussian-process (GP) or other uncertainty metrics during active learning identifies inert factors, potentially preventing wasteful exploration. • Algorithm-agnostic; can be implemented immediately in industrial settings.
  • Energy-efficient start-up optimization via digital twin for a vegetable broth sterilization process
    Marcello Maria Bozzini, Marco Menegon, Alberto di Loreto, Gaia Lunari, Silvia Serena Mariani, Mattia Vallerio, Laura Piazza, Flavio Manenti
    Journal of Food Engineering, 2026
    The food industry is undergoing a digital transformation driven by the need for greater sustainability, efficiency, and data integration. This study presents a methodology for implementing a digital twin in an industrial food manufacturing process, using the vegetable broth production line as a case study. The workflow integrates process analysis, sensor data collection, and data reconciliation to improve the reliability of process variables and enable accurate simulation. The reconciled data were used to develop a dynamic model in commercial software, capable of simulating different operating conditions. Two start-up strategies, cold start-up and pre-heating, were compared, revealing that pre-heating reduces steam consumption by 62% and start-up time by 63%. These results demonstrate the potential of digital twins in optimizing operational efficiency and energy use in the food industry. Future developments may include real-time data acquisition, integration with control systems, and the use of AI for predictive maintenance and process optimization. • Presented a methodology for digital twin development for the food industry. • Digital twin applied to the vegetable broth sterilization process. • Data reconciliation improves input accuracy for simulation. • Energy-efficient start-up achieved via digital twin simulation.
  • Towards near-ZLD sea water brine treatment: A techno-economic study
    Mattia Vallerio, Loretta Salano, Flavio Manenti
    Desalination, 2026
  • A hybrid surrogate and simulation-based framework for efficient CapEx/OpEx optimization in complex chemical plants
    Luis Felipe Sánchez, Marcello Maria Bozzini, Mattia Vallerio, Flavio Manenti
    Chemical Engineering and Processing Process Intensification, 2026
    Process Intensification aims at the economic and operational efficiency of chemical processes by emphasizing energy integration, unit size reduction, and cost minimization. The optimality of intensified solutions is typically assessed using Process Simulators, especially for complex chemical processes. These tools offer limited reliability and flexibility for the optimization of Capital Expenditures (CapEx), thus restricting their scope to Operational Expenditures (OpEx). As an alternative, external software is commonly required for simulation-based (SIM-OPT) and surrogate-based (SUR-OPT) CapEx/OpEx optimization. This work introduces a framework to select the most efficient optimization methodology based on simulation computational complexity. In addition, it presents a novel methodology (MIX-OPT) providing an efficient trade-off between optimization speed and accuracy. These three approaches were employed to optimize a complex biogas-to-methanol plant. Results showed that SIM-OPT achieved the greatest reduction in the Payback Period (PBP) of the plant (9.28%) with highest computational demand (984 min), SUR-OPT had the shortest computational time (717 min) with moderate PBP reduction (7.89%), and MIX-OPT reached a compromise with a PBP reduction of 8.24% in 884 min. The proposed framework demonstrated that simple simulations benefit from SIM-OPT, complex ones from SUR-OPT, and a wide range of simulations in the middle from the novel MIX-OPT approach. • Presented a comprehensive framework for selecting the most efficient simultaneous CapEx/OpEx optimization approach based on simulation complexity. • The framework + compares and integrates three different simulation and surrogate-based optimization methodologies to optimize complex production processes. • A novel optimization methodology combining surrogate modeling and simulation-based optimization is proposed. This approach demonstrated a 10% reduction in computational effort compared to traditional simulation-based optimization, while maintaining a high level of precision, with a 1% deviation from the optimum. • The framework was validated on a complex methanol synthesis simulation by analyzing the strengths and weaknesses of the three optimization methodologies. • Both Operational and Dimensioning parameters can be defined as degrees of freedom for the optimization problem.
  • Computationally-Efficient Environmental and Economic Multi-Objective Optimization of a Methanol Production Process via Surrogate Modeling
    Luis Felipe Sánchez, Mattia Vallerio, Flavio Manenti
    Engineering Reports, 2025
    Process simulation is a powerful tool in the Process Systems Engineering (PSE) field, in particular for optimization tasks. However, the computational times involved in these activities may become prohibitive for complex processes. As an alternative, data‐driven strategies, such as surrogate models, have been widely adopted. Surrogate models are typically trained on data generated from specifically designed simulation runs. The computational efficiency of these designs has been addressed in the literature by minimizing the total number of simulations. However, the execution time of each simulation may be potentially reduced by shortening the transient period between consecutive simulations, for example, by minimizing the Euclidean distance between them. Sorting the simulations of the design to minimize the total traveled distance describes a typical Traveling Salesman Problem (TSP) scenario. This work analyzes the effect of four random and sorted one‐shot experimental designs, composed of 50 samples, on the surrogate model training and surrogate‐based optimization of a methanol synthesis process: DoE 1) Latin Hypercube (LHS), DoE 2) maxmin LHS, DoE 3) maxmin LHS sorted with nearest neighbors, and DoE 4) maxmin LHS sorted with 2‐opt. Results showed that sorted DoEs improved the surrogate model accuracy by reducing its relative error by 0.3%. In addition, the overall computational time required diminished by around 14%. The most efficient experimental design was DoE 4, which was used to train a model later employed to optimize the OPEX and CO emissions of the methanol process, resulting in reductions of 15.0% and 11.4%, respectively.
  • Industrial scale biogas reforming modelling and validation
    Loretta Salano, Mattia Vallerio, Emanuele Moioli, Flavio Manenti
    Chemical Engineering Journal, 2025
    This study investigates the industrial data from a biogas reforming plant for data reconciliation (DR) and process modelling to enhance syngas production. With biogas emerging as a vital renewable feedstock, effective data management and optimization strategies are essential for advancing sustainable chemical synthesis. In this work, we assess plant data quality from a demo-scale biogas steam reforming unit (BSR), identifying bias and control loop inconsistencies that impact measurement reliability. Among the outlier methods implemented, Isolation Forest (IF) achieves a 93% raw data variance reduction, simultaneously isolating anomalies and detecting steady states. Further analysis of the steam-to-carbon (SC) ratio reveals a control loop error, increasing confidence in output measured variables. A DR problem is then solved, enabling closure of the mass balance, with a 4% adjustment of input mass flowrates, highlighting process data discrepancies. Considering the reconciled data, a catalytic tube model is applied to predict BSR product distribution and ensure thermodynamic equilibrium. A Monte Carlo sensitivity analysis shows the importance of methane tenor in biogas and input temperature for optimal reactor performance. The syngas quality, defined by the hydrogen and carbon content, experimental results highlight the potential for enhanced syngas efficiency, particularly for fuel synthesis applications, requiring a stoichiometric ratio of 2. This methodology establishes a framework for improving biogas plant reforming operations, offering a foundation for control strategy advancement and future process optimization. • The results from the operation of a biogas steam reformer are provided. • Outliers detection methods are applied to identify nominal steady-state data. • An extensive data reconciliation procedure is applied to obtain significant process data. • The reconciled data are used to validate the developed process model. • The combination of experimental and modelling data are used for further optimization of the unit.
  • Advanced Steady-State Detection in Biogas Plant Data Using Statistical and Machine Learning Techniques
    Salano Loretta, Manenti Flavio, Vallerio Mattia
    Chemical Engineering Transactions, 2025
    The application of biogas in the industrial energy transition has strongly emerged as a relevant feedstock for chemicals and energy production, especially in the context of biogas reforming. Effective steady-state detection is crucial for optimizing the production processes, where stable operation directly impacts hydrogen yield and system efficiency. This study tests three steady-state detection (SSD) techniques on two datasets from a demo-scale biogas plant: (i) a statistical hypothesis testing approach, (ii) a trend-based sliding window method, and (iii) the machine learning-based isolation forest (IF) algorithm. A parameter sensitivity analysis was performed to optimize each technique's performance on the biogas plant data. Results indicate that the statistical methods are strongly influenced by the operative parameters, while the IF detects simultaneously outliers and transient values. The integration of an IF algorithm in the detection framework is suggested to enhance reliability in real-time monitoring of biogas reforming processes. This study shows the potential of advanced SSD methods in analysing biogas plant operations to improve hydrogen production and optimal control. Future research will focus on developing a unified detection framework and refining machine-learning models for real-time implementation.
  • Dynamic simulation and Residence time Optimization of an Autohydrolysis Reactor for the Valorization of Lignocellulosic Waste
    Vallerio Mattia, Spigno Giorgia
    Chemical Engineering Transactions, 2025
  • Industrial Data Science for Batch Manufacturing
    Mattia Vallerio, Carlos Perez-Galvan, Francisco J. Navarro-Brull
    Computer Aided Chemical Engineering, 2024
  • Industrial Data Science for Batch Reactor Monitoring and Fault Detection
    I. Imanol Arzac, Mattia Vallerio, Carlos Perez-Galvan, Francisco J. Navarro-Brull
    Rsc Theoretical and Computational Chemistry Series, 2023
  • Industrial data science - a review of machine learning applications for chemical and process industries
    Max Mowbray, Mattia Vallerio, Carlos Perez-Galvan, Dongda Zhang, Antonio Del Rio Chanona, Francisco J. Navarro-Brull
    Reaction Chemistry and Engineering, 2022
  • Semi-Supervised Cloud Detection withWeakly Labeled RGB Aerial Images using Generative Adversarial Networks
    Toon Stuyck, Axel-Jan Rousseau, Mattia Vallerio, Eric Demeester
    International Conference on Pattern Recognition Applications and Methods, 2022
  • Value chain planning optimization: A data driven digital twin approach
    F. Ferranti, F. Manenti, G. Vingerhoets, M. Vallerio
    IFAC Papersonline, 2021
  • Data-driven digital twin of a chemical production site for production and utilities planning
    Filippo Ferranti, G. Vingerhoets, F. Manenti, Mattia Vallerio
    Chemical Engineering Transactions, 2021
  • Interactive Multi-objective Dynamic Optimization of Bioreactors under Parametric Uncertainty
    Philippe Nimmegeers, Mattia Vallerio, Dries Telen, Jan Van Impe, Filip Logist
    Chemie Ingenieur Technik, 2019
  • Pomodoro: A Novel Toolkit for Dynamic (MultiObjective) Optimization, and Model Based Control and Estimation
    Satyajeet Bhonsale, Dries Telen, Dominique Vercammen, Mattia Vallerio, Jan Hufkens, Philippe Nimmegeers, Filip Logist, Jan Van Impe
    2018
  • A study of integrated experiment design for NMPC applied to the Droop model
    D. Telen, B. Houska, M. Vallerio, F. Logist, J. Van Impe
    Chemical Engineering Science, 2017
  • Towards nonlinear model predictive control with integrated experiment design
    D. Telen, M. Vallerio, S. Bhonsale, F. Logist, J. Van Impe
    Proceedings of the American Control Conference, 2016
  • Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method
    Mattia Vallerio, Dries Telen, Lorenzo Cabianca, Flavio Manenti, Jan Van Impe, Filip Logist
    Chemical Engineering Science, 2016
  • SolACE: An Open Source Package for Nolinear Model Predictive Control and State Estimation for (Bio)Chemical Processes
    Satyajeet S. Bhonsale, Mattia Vallerio, Dries Telen, Dominique Vercammen, Filip Logist, Jan van Impe
    Computer Aided Chemical Engineering, 2016
  • Approximate robust optimal control of nonlinear dynamic systems under process noise
    Dries Telen, Mattia Vallerio, Lorenzo Cabianca, Boris Houska, Jan Van Impe, Filip Logist
    2015 European Control Conference Ecc 2015, 2015
  • Interactive NBI and (E)NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control
    Mattia Vallerio, Dominique Vercammen, Jan Van Impe, Filip Logist
    Computers and Chemical Engineering, 2015
  • Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise
    D. Telen, M. Vallerio, L. Cabianca, B. Houska, J. Van Impe, F. Logist
    Journal of Process Control, 2015
  • An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty
    Mattia Vallerio, Jan Hufkens, Jan Van Impe, Filip Logist
    Expert Systems with Applications, 2015
  • Interactive Multi-Objective Decision-Support for the Optimization of Nonlinear Dynamic (Bio)Chemical Processes with Uncertainty
    Mattia Vallerio, Jan Hufkens, Jan Van Impe, Filip Logist
    Computer Aided Chemical Engineering, 2015
  • Expanding the exploration of the criterion space for multi-objective optimal control problems
    Mattia Vallerio, Dominique Vercammen, Jan Van Impe, Filip Logist
    2014 European Control Conference Ecc 2014, 2014
  • Tuning of NMPC controllers via multi-objective optimisation
    Mattia Vallerio, Jan Van Impe, Filip Logist
    Computers and Chemical Engineering, 2014
  • Multi-objective and robust optimal control of a CVD reactor for polysilicon production
    Mattia Vallerio, Daan Claessens, Filip Logist, Jan Van Impe
    Computer Aided Chemical Engineering, 2014
  • Symmetric algorithmic differentiation based exact Hessian SQP method and software for Economic MPC
    Rien Quirynen, Boris Houska, Mattia Vallerio, Dries Telen, Filip Logist, Jan Van Impe, Moritz Diehl
    Proceedings of the IEEE Conference on Decision and Control, 2014
  • Model-based optimization of the cooling system of an industrial tubular LDPE reactor
    Mattia Vallerio, Filip Logist, Peter Van Erdeghem, Christoph Dittrich, Jan Van Impe
    Industrial and Engineering Chemistry Research, 2013
  • Multi-objective optimal control of chemical processes using ACADO toolkit
    F. Logist, M. Vallerio, B. Houska, M. Diehl, J. Van Impe
    Computers and Chemical Engineering, 2012
  • Model based optimisation of tubular reactors for LDPE production
    Peter M.M. Van Erdeghem, Filip Logist, Mattia Vallerio, Christoph Dittrich, Jan F. Van Impe
    IFAC Proceedings Volumes IFAC Papersonline, 2012
  • Explicit weight selection procedure for optimal control problems with weighted objectives
    Filip Logist, Mattia Vallerio, Jan Van Impe
    IFAC Proceedings Volumes IFAC Papersonline, 2011
  • Model predictive control of a CVD reactor for production of polysilicon rods
    19th International Congress of Chemical and Process Engineering Chisa 2010 and 7th European Congress of Chemical Engineering Ecce 7, 2010
  • Model predictive control of a CVD reactor for production of polysilicon rods
    L. Viganò, Mattia Vallerio, N. M. N. Lima, Lamia Zuñiga Liñan, Guglielmo Manenti, et al.
    Chemical Engineering Transactions, 2010

RECENT SCHOLAR PUBLICATIONS

  • Integrated valorization of algae residue and oily sludge into hydrogen and liquid biofuels: detailed process simulation and preliminary operational assessment
    MM Bozzini, F de Fusco, NF Lorenzo, M Vallerio, F Manenti
    Renewable Energy, 125919 , 2026
    2026
  • Optimal Design of Acid Gas-to-Syngas (AG2S) Technology: Process Optimization and Surrogate Modeling
    S Caspani, LF Sánchez, M Vallerio, F Manenti
    Industrial & Engineering Chemistry Research 65 (15), 8006-8016 , 2026
    2026
  • All you need is noise—from feature selection to explainable industrial AI
    M Vallerio, ADR Chanona, FJ Navarro-Brull
    DIGITAL CHEMICAL ENGINEERING 18, 100290-100290 , 2026
    2026
  • A hybrid surrogate and simulation-based framework for efficient CapEx/OpEx optimization in complex chemical plants
    LF Sánchez, MM Bozzini, M Vallerio, F Manenti
    Chemical Engineering and Processing-Process Intensification, 110638 , 2025
    2025
    Citations: 4
  • Towards near-ZLD sea water brine treatment: A techno-economic study
    M Vallerio, L Salano, F Manenti
    Desalination, 119562 , 2025
    2025
    Citations: 1
  • Computationally‐Efficient Environmental and Economic Multi‐Objective Optimization of a Methanol Production Process via Surrogate Modeling
    LF Sánchez, M Vallerio, F Manenti
    Engineering Reports 7 (11), e70422 , 2025
    2025
  • Energy-efficient start-up optimization via digital twin for a vegetable broth sterilization process
    MM Bozzini, M Menegon, A di Loreto, G Lunari, SS Mariani, M Vallerio, ...
    Journal of Food Engineering, 112822 , 2025
    2025
    Citations: 3
  • Development and Integration of a Co-Current Hollow Fiber Membrane Unit for Gas Separation in Process Simulators Using CAPE-OPEN Standards
    L Salano, I Dagna, M Vallerio, F Manenti
    Systems and Control Transactions, 364-369 , 2025
    2025
  • Industrial scale biogas reforming modelling and validation
    L Salano, M Vallerio, E Moioli, F Manenti
    Chemical Engineering Journal 510, 160871 , 2025
    2025
    Citations: 9
  • Advanced Steady-state Detection in Biogas Plant Data Using Statistical and Machine Learning Techniques
    S Loretta, F Manenti, M Vallerio
    Chemical Engineering Transactions 119, 415-420 , 2025
    2025
  • Dynamic simulation and Residence time Optimization of an Autohydrolysis Reactor for the Valorization of Lignocellulosic Waste
    M Vallerio, S Giorgia, F Manenti, B Andrea
    Chemical Engineering Transactions 119, 271-276 , 2025
    2025
  • Biogas Reforming: Data Reconciliation and Model Validation with Industrial Data
    L Salano, E Moioli, M Vallerio, F Manenti
    Available at SSRN 5042990 , 2024
    2024
    Citations: 1
  • Industrial Data Science for Batch Manufacturing
    M Vallerio, C Perez-Galvan, FJ Navarro-Brull
    Computer Aided Chemical Engineering 53, 2965-2970 , 2024
    2024
    Citations: 4
  • Industrial Data Science for Batch Reactor Monitoring and Fault Detection
    II Arzac, M Vallerio, C Perez-Galvan, FJ Navarro-Brull
    2023
    Citations: 4
  • Industrial Data Science for Batch Manufacturing Processes
    I Arzac-Garmendia, M Vallerio, C Perez-Galvan, FJ Navarro-Brull
    arXiv preprint arXiv:2209.09660 , 2022
    2022
    Citations: 6
  • Industrial data science–a review of machine learning applications for chemical and process industries
    M Mowbray, M Vallerio, C Perez-Galvan, D Zhang, ADR Chanona, ...
    Reaction Chemistry & Engineering 7 (7), 1471-1509 , 2022
    2022
    Citations: 143
  • Data-driven Digital Twin of a Chemical Productionsite for Production and Utilities Planning
    F Ferranti, G Vingerhoets, F Manenti, M Vallerio
    Chemical Engineering Transactions 86, 913-918 , 2021
    2021
    Citations: 5
  • Semi-supervised cloud detection with weakly labeled RGB aerial images using generative adversarial networks
    T Stuyck, AJ Rousseau, M Vallerio, E Demeester, M DeMarsico, ...
    Proceedings of the 11th international conference on pattern recognition … , 2021
    2021
    Citations: 2
  • Value chain planning optimization: a data driven digital twin approach
    F Ferranti, F Manenti, G Vingerhoets, M Vallerio
    IFAC-PapersOnLine 54 (3), 572-577 , 2021
    2021
    Citations: 6
  • Interactive multi‐objective dynamic optimization of bioreactors under parametric uncertainty
    P Nimmegeers, M Vallerio, D Telen, J Van Impe, F Logist
    Chemie Ingenieur Technik 91 (3), 349-362 , 2019
    2019
    Citations: 26

MOST CITED SCHOLAR PUBLICATIONS

  • Industrial data science–a review of machine learning applications for chemical and process industries
    M Mowbray, M Vallerio, C Perez-Galvan, D Zhang, ADR Chanona, ...
    Reaction Chemistry & Engineering 7 (7), 1471-1509 , 2022
    2022
    Citations: 143
  • Tuning of NMPC controllers via multi-objective optimisation
    M Vallerio, J Van Impe, F Logist
    Computers & Chemical Engineering 61, 38-50 , 2014
    2014
    Citations: 100
  • Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise
    D Telen, M Vallerio, L Cabianca, B Houska, J Van Impe, F Logist
    Journal of Process Control 33, 140-154 , 2015
    2015
    Citations: 69
  • Multi-objective optimal control of chemical processes using ACADO toolkit
    F Logist, M Vallerio, B Houska, M Diehl, J Van Impe
    Computers & Chemical Engineering 37, 191-199 , 2012
    2012
    Citations: 68
  • An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty
    M Vallerio, J Hufkens, J Van Impe, F Logist
    Expert Systems with Applications 42 (21), 7710-7731 , 2015
    2015
    Citations: 63
  • Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method
    M Vallerio, D Telen, L Cabianca, F Manenti, J Van Impe, F Logist
    Chemical Engineering Science 140, 201-216 , 2016
    2016
    Citations: 59
  • Symmetric algorithmic differentiation based exact Hessian SQP method and software for economic MPC
    R Quirynen, B Houska, M Vallerio, D Telen, F Logist, J Van Impe, M Diehl
    53rd IEEE Conference on Decision and Control, 2752-2757 , 2014
    2014
    Citations: 46
  • A study of integrated experiment design for NMPC applied to the Droop model
    D Telen, B Houska, M Vallerio, F Logist, J Van Impe
    Chemical Engineering Science 160, 370-383 , 2017
    2017
    Citations: 28
  • Interactive multi‐objective dynamic optimization of bioreactors under parametric uncertainty
    P Nimmegeers, M Vallerio, D Telen, J Van Impe, F Logist
    Chemie Ingenieur Technik 91 (3), 349-362 , 2019
    2019
    Citations: 26
  • Model predictive control of a CVD reactor for production of polysilicon rods
    L Viganò, M Vallerio, F Manenti, NMN Lima, L Zuniga, GM Linan
    CHEMICAL ENGINEERING 21 , 2010
    2010
    Citations: 26
  • Multi-objective and robust optimal control of a CVD reactor for polysilicon production
    M Vallerio, D Claessens, F Logist, J Van Impe
    Computer Aided Chemical Engineering 33, 571-576 , 2014
    2014
    Citations: 20
  • Interactive NBI and (E) NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control
    M Vallerio, D Vercammen, J Van Impe, F Logist
    Computers & Chemical Engineering 82, 186-201 , 2015
    2015
    Citations: 18
  • Pomodoro: A novel toolkit for dynamic (multiobjective) optimization, and model based control and estimation
    S Bhonsale, D Telen, D Vercammen, M Vallerio, J Hufkens, ...
    IFAC-PapersOnLine 51 (2), 719-724 , 2018
    2018
    Citations: 13
  • Solace: An open source package for nolinear model predictive control and state estimation for (bio) chemical processes
    SS Bhonsale, M Vallerio, D Telen, D Vercammen, F Logist, J Van Impe
    Computer Aided Chemical Engineering 38, 1971-1976 , 2016
    2016
    Citations: 12
  • Industrial scale biogas reforming modelling and validation
    L Salano, M Vallerio, E Moioli, F Manenti
    Chemical Engineering Journal 510, 160871 , 2025
    2025
    Citations: 9
  • Model-based optimization of the cooling system of an industrial tubular LDPE reactor
    M Vallerio, F Logist, P Van Erdeghem, C Dittrich, J Van Impe
    Industrial & Engineering Chemistry Research 52 (4), 1656-1666 , 2013
    2013
    Citations: 9
  • Model based optimisation of tubular reactors for LDPE production
    PMM Van Erdeghem, F Logist, M Vallerio, C Dittrich, JF Van Impe
    IFAC Proceedings Volumes 45 (15), 786-791 , 2012
    2012
    Citations: 7
  • Industrial Data Science for Batch Manufacturing Processes
    I Arzac-Garmendia, M Vallerio, C Perez-Galvan, FJ Navarro-Brull
    arXiv preprint arXiv:2209.09660 , 2022
    2022
    Citations: 6
  • Value chain planning optimization: a data driven digital twin approach
    F Ferranti, F Manenti, G Vingerhoets, M Vallerio
    IFAC-PapersOnLine 54 (3), 572-577 , 2021
    2021
    Citations: 6
  • Data-driven Digital Twin of a Chemical Productionsite for Production and Utilities Planning
    F Ferranti, G Vingerhoets, F Manenti, M Vallerio
    Chemical Engineering Transactions 86, 913-918 , 2021
    2021
    Citations: 5