Projects

COCOP
Full Title: Coordinating Optimisation of Complex Industrial Processes
Aim:

The vision of COCOP is that complex process-industry plants are optimally run by operators with the guidance of a coordinating, real-time optimisation system.

Concept:
The project’s objective is to enable plant-wide monitoring and control by using the model-based, predictive, coordinating optimisation concept in integration with plant’s automation systems.
Start date:
1 October, 2016
CoPro
Full Title: Improved energy and resource efficiency by better coordination of production in the process industries
Aim:

The goal of CoPro is to develop and to demonstrate methods and tools for process monitoring and optimal dynamic planning, scheduling and control of plants, industrial sites and clusters under...

Concept:
CoPro pays special attention to the role of operators and managers in plant-wide control solutions and to the deployment of advanced solutions in industrial sites with a heterogeneous IT environment...
Start date:
1 November, 2016
FUDIPO
Full Title: Future Directions of Production Planning and Optimized Energy- and Process Industries
Aim:

The FUDIPO project will integrate machine learning functions on a wide scale into several critical process industries, showcasing radical improvements in energy and resource efficiency and...

Concept:
The approach is to construct physical process models, which then are continuously adapted using “good data” while “bad data” is used for fault diagnostics. After learning, classification of data can...
Start date:
1 October, 2016
MONSOON
Full Title: MOdel based coNtrol framework for Site-wide OptimizatiON of data-intensive processes
Aim:

MONSOON aims to establish a data-driven methodology to support the identification and exploitation of optimization potentials by applying multi-scale model based predictive controls in production...

Concept:
MONSOON will be developed and evaluated in two sites from the aluminium and plastics domains. The aluminium scenario will be focused on predictive monitoring of potlines, targeting early detection of...
Start date:
1 October, 2016