Expertise
Measure – Model – Optimise – Control
Measure
Mastering the Science of Measurement and its practical implementation is crucial for the advanced characterisation of multiphysical processes.
We assist you with:
- Selecting the right measurands and sensors for your specific context
- Conducting thorough in-situ measurement campaigns
- Data treatment, filtering, and post-processing
- Methodically assessing measurement uncertainties and their propagation in accordance with Standards
- Scientifically analyzing your Dataset, including Statistical and Frequency analysis
Roy is equipped with the state-of-the-art Sefram DAS-1800 high-speed data acquisition system, offering 40 synchronized channels with a sampling rate of up to 1MHz (1μs min. time).
Roy conducts core business Data Analysis using Python, R, and MATLAB.
Examples of Realisation
- Harmonics at High Voltage Delivery Point
- Thermal Characterisation of Furnaces
- Efficiencies of Electrochemical Processes
- Ripple of Industrial Rectifiers
Model
Connecting physical variables is a complex task, regardless of whether the system is SISO (Single Input, Single Output) or MIMO (Multiple Input, Multiple Output).
We specialized in developing models tailored to your specific context and application, including:
- Analytical modeling based on physical laws
- Dynamic modeling such as process Transfer Functions and
State-Space models - White-box and Black-box Machine Learning models
- Grey-box modeling combining analytical methods and machine learning algorithms
Roy has a particular focus on all aspects of Energy, Work, and Heat as well as their implications for Resources and Emissions.
Roy primarily develops process models using Python and MATLAB/Simulink.
Examples of Realisation
- Impedance Modeling of Processes
- Energy Efficiency Modeling of Complex Systems
- Thermal Response of Hybrid Electric/Gas Furnaces
- Energetics/Thermodynamics of Boilers
Optimise
Optimisation is crucial for designing efficient and effective systems, whether the goal is minimising costs, maximising performance, or balancing competing objectives.
We specialize in creating optimisation solutions tailored to your specific challenges, including:
- Formalising mathematical optimisation problems to ensure clarity and precision in addressing complex systems
- Linear and nonlinear optimisation for process improvement
- Dynamic optimisation for time-dependent systems
- Heuristic and metaheuristic algorithms for complex, large-scale problems
- Multi-objective optimisation to balance trade-offs in conflicting goals
Roy focuses on applying optimisation techniques to resource management, energy systems, and emissions reduction.
Roy primarily uses open-source solvers and tools like Python to develop practical and scalable solutions.
Examples of Realisation
- Hybrid Gas & Electricity Heating Processes
- Multi-Electrolzyer Hydrogen Production Plants
- Energy Systems with Storage Management
- Power Flexibility and Price Tracking
Control
Most industrial controllers are configured as simple Proportional-Integral (PI) systems with fixed parameters. These setups often overlook critical factors such as variable efficiencies across the operating range, process changes or degradation, and evolving flexibility requirements over time.
We offer expertise in implementing advanced control strategies through:
- Instrumentation and control specifications to meet specific process requirements
- Methodological characterisation of process dynamics to ensure robust performance
- Investigation of control degrees of freedom to identify potential enhancements
- Integration of multiple techniques, including real-time PID control, Model Predictive Control (MPC), and upper-layer optimisation solvers
Roy has extensive experience in implementing control projects, particularly those addressing flexibility needs in energy systems and grids, global energy efficiency optimisation, and emission reduction.
Roy also leads projects worldwide across various industrial and energy sectors, delivering innovative solutions tailored to client requirements.
Examples of Realisation
- Primary (FCR) and Secondary (aFRR) Frequency Control of Production Facilities, Storage Assets and Processes
- Deployment of Multi-Asset Control for real-time Active Energy Optimisation