INSPEC – Integrating sensor based process monitoring and advanced process control

  • Status
  • Start date
    January 1, 2017
  • Runtime
    48 months

INSPEC quantitatively integrates information from state-of-the-art Process Analytical Technology, process measurements and dynamic process models (data driven or physics-based) into Advanced Process Control. This will induce a step-change with respect to process controllability and enables operation in a more energy efficient regime, while reducing off-spec production.

Existing chemometric technology for quantitative analysis of Process Analytical Technology (PAT) data will be extended to facilitate Advanced Process Control (APC). This includes optimization of data pre‑processing to extract control‑relevant information and fusion of PAT data with process measurements.

Current APC methodology will be extended by optimizing the use of all available real‑time process information to enable optimal process operation. This includes technology development for detection process behaviour that deviates from physics and data-driven dynamic models and the development of tools for maintaining the accuracy of these models and model‑based controllers.

Quantitative integration of these models with PAT extends the existing potential of APC as dynamic models will be enriched with information from PAT. This will greatly extend the information for development of control strategies. Key processes of industrial project partners will demonstrate the benefits of the projected developments.

Scientific Output

Tim Offermans (2019), Improving process control of a dairy processing plant using a softsensor on parallel production data streams

M. Porru, L. Ozkan, T. Huppertz, E. Szymanska (2019), Dynamic Modeling of Milk Acidification for Production Design and Process Control

AIChe – L. Ozkan, C.S. Mendez (2019), Blanco On the State and Output Sensitivity of First-Principles Models


Topsector Energie: Energiezuiniger produceren door van procesdata te leren, April 2021

Grant provider

This project is co-funded by TKI-E&I with the supplementary grant 'TKI- Toeslag' for Topconsortia for Knowledge and Innovation (TKI’s) of the Ministry of Economic Affairs and Climate Policy.

Project partners

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