Synchronizing process variables in time for industrial process monitoring and control. - Tim Offermans; Ewa Szymanska; Lutgarde M. C. Buydens; Jeroen J. Jansen (Comput. Chem. Eng., 2020)

Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data. - Geert H. van Kollenburg; Roel Bouman; Tim Offermans; Jan Gerretzen; Lutgarde M. C. Buydens; Henk-Jan van Manen; Jeroen J. Jansen (Comput. Chem. Eng., 2021)

Automatically optimizing dynamic synchronization of individual industrial process variables for statistical modelling. - Tim Offermans; Ewa Szymanska; Geert H. van Kollenburg; Lutgarde M. C. Buydens; Jeroen J. Jansen (Comput. Chem. Eng., 2021)

Batch Endpoint Prediction using Local Mixture of Batch Time Experts. - Francisco Souza 001; Tim Offermans; Jeroen J. Jansen (MetroXRAINE, 2022)

Retrospective quality by design r(QbD) for lactose production using historical process data and design of experiments. - Leonardo Galvis; Tim Offermans; Carlo G. Bertinetto; Andrea Carnoli; Emina Karamujic; Weiwei Li; Ewa Szymanska; Lutgarde M. C. Buydens; Jeroen J. Jansen (Comput. Ind., 2022)

Contextual Mixture of Experts: Integrating Knowledge into Predictive Modeling. - Francisco Souza 001; Tim Offermans; Ruud Barendse; Geert J. Postma; Jeroen J. Jansen (IEEE Trans. Ind. Informatics, 2023)

Process expert knowledge is essential in creating value from data-driven industrial soft sensors. - Tim Offermans; Ewa Szymanska; Francisco Alexandre A. Souza; Jeroen J. Jansen (Comput. Chem. Eng., 2024)