The public defence of Moksadur Rahman's licentiate thesis in Energy and environmental engineering
Doctoral thesis and Licentiate seminars
The public defence of Moksadur Rahman's licentiate thesis in Energy and environmental engineering will take place at Mälardalen University on October 30, 2019, at 13.00 in room Pi, house R, Västerås.
Faculty examiner: Professor Agostino Gambarotta
Reserve is Professor Mirko Morini, University of Parma
Driven by the intense competition, increasing operational cost and strict environmental regulations, modern process and energy industry needs to figure out the best possible way to adapt in order to maintain profitability. In an attempt to satisfy counteracting objectives of improving the product quality and process efficiency while reducing the production cost and plant downtime, optimization of control and operation of industrial systems are essential. Use of optimization not only can improve the control and monitoring of assets but also it can offer better coordination among different assets. Thus, can lead to extensive savings in the energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving force behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to avail the benefits of optimal control and operation.
On this note, the main focus of this dissertation can be narrowed down to the use of different process models, soft-sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. Thereby, a generic architecture for optimal control, diagnostics and decision support system, referred here as a learning system, is proposed in this research.
Different components of the proposed learning system are investigated in the significant part of this research. Two very different case studies of energy-intensive pulp and paper industry and promising micro-CHP industry are selected for the demonstration of the learning system. In this research, one of the main challenges arises from the fact that both the case studies are quite different from each other in terms of size, functions, quantity and structure of the existing automation system. Typically, there are only a few pulp digesters could be located in a Kraft pulping mill, but there could be hundreds of units in a micro-CHP fleet.
The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods for continuous pulp digesters are studied. On the other hand, mathematical modelling, model adaptation and diagnostics techniques are explored for micro-CHP fleet.