On a learning system for industrial automation : Model-based control and diagnostics for decision support

Access to energy is fundamental to economic and technological advancement. Hence, the more the world develops, the greater the demand for energy becomes. Evidently, the production and consumption of energy alone account for more than 80% of global anthropogenic greenhouse gas (GHG) emissions. There is broad scientific consensus that efficiency improvements in energy production and consumption must come first on the path to reducing global GHG emissions. As the largest producer and consumer of energy, the industrial sector faces tremendous challenges due to stringent environmental regulations, intense price-based global competition, rising operating costs and rapidly changing economic conditions. Therefore, increasing energy and resource efficiency while improving throughput and asset reliability is a matter of utmost importance. Satisfying such demanding objectives requires an optimal operation, control and monitoring of plant assets and processes. This is one of the main driving forces behind developing digital solutions, methods, and frameworks that can be integrated with old and new industrial automation platforms. The main focus of this dissertation is to investigate frameworks, process models, soft sensors, control optimization, and diagnostic techniques to improve the operation, control, and monitoring of industrial plants and processes. In this thesis, a generic architecture for control optimization, diagnostics, and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies, one representing large-scale assets and another representing a fleet of small-scale assets, are considered to demonstrate the genericness of the proposed system architecture. In this thesis, a very energy-intensive chemical pulping process represents the case study of large-scale assets, and a micro gas turbine (MGT) fleet for distribu
FUDIPO