Researcher

Philippe Nimmegeers

Research Expertise:Process-based modelling, control and optimization for the development of sustainable systems I am Philippe Nimmegeers, a postdoctoral researcher at the University of Antwerp in the research groups of Prof. Steven Van Passel (EnvEcon) and Prof. Pieter Billen (iPRACS) since November 2020. My current research is in the field of energy economics and engineering with a focus on quantitative sustainability analysis such as techno-economic analysis and life cycle analysis and the development of more generic methodologies, applied to biorefineries, plastic waste recycling energy systems and chemical and biochemical processes. I have a background in multi-scale (bio)chemical process modeling and model-based (multi-objective dynamic) optimization of (bio)chemical processe under uncertainty, both from my PhD at KU Leuven in the group of Prof. Jan Van Impe. During my 2 years and 1 month as advanced process control engineer at BASF I mainly worked on the development of linear and nonlinear model predictive controllers, mid-fidelity operator training simulators (digital twins) and continuous improvement (lean six sigma green belt). Research keywords: quantitative sustainability assessments (techno-economic assessment, life cycle analysis, ...), energy economics, plastics recycling, model-based multi-objective optimization, resource effectiveness, process modeling, process systems engineering, chemical engineering

Keywords:MATHEMATICAL MODELING, MULTI-VARIATE DATA ANALYSIS, DECISION SUPPORT SYSTEM, TECHNO-ECONOMIC ANALYSIS, UNCERTAINLY QUANTIFICATION, MULTISCALE MODELLING, PROCESS OPTIMIZATION, LIFE CYCLE ASSESSMENT, Chemistry (incl. biochemistry)

Disciplines:Sustainable chemistry not elsewhere classified, Environmental impact and risk assessment, Modelling, simulation and optimisation, (Bio)chemical reactors, Chemical process design, Process control, Sustainable and environmental engineering not elsewhere classified, Development planning and policy, Innovation and technology management, Design of experiments, Mathematical methods, programming models, mathematical and simulation modelling, Analysis of collective decision-making, Information, knowledge and uncertainty

Research techniques:Process modeling: - Design of Experiments (static) Optimal Experiment design (dynamic) - Parameter estimation - Model discrimination - Model selection - Steady state and dynamic process modeling - Regression - Agent-based modeling - Multi-scale modeling Data analytics: - Lean Six Sigma methodology - Qualitative data analysis - Quantitative data analysis - Multivariate statistical process monitoring - ANOVA - Sensitivity analysis Uncertainty quantification and propagation - Monte Carlo simulations - Probability distribution fitting - Polynomial chaos expansion - Sigma points method (unscented transformation) - Linearization Model-based optimization - Dynamic optimization - Stochastic optimization - Multi-objective optimization - Multicriteria decision making - Distributed optimization - Moving horizon estimation - Kalman filtering (KF, EKF, UKF) Advanced Process Control - PID tuning - Advanced Regulatory Control - Model Predictive Control - Operator Training Simulators - Softsensors (Bio)chemical process engineering: - (Bio)chemical reactor design - Separation processes - Design of unit operations Systeembiologie: - (Dynamic) metabolic flux analysis - (Dynamische) flux balance analysis - Network reduction Quantitative sustainability analysis: - Techno-economic analysis - Life cycle analysis - Multi-level statistical entropy analysis

Users of research expertise:- Chemical industry - Industrial biotechnology - Pharmaceutical industry - Agro-industry - Manufacturing - Governments & policy making -Knowledge institutions