Browsing by Author "Broughton, Jonathan"
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Item Open Access Model-based multi-objective optimisation of reheating furnace operations using genetic algorithm(Elsevier, 2018-01-30) Hu, Yukun; Tan, C. K.; Broughton, Jonathan; Roach, Paul Alun; Varga, LizAn effective optimisation strategy for metal reheating processes is crucial for the economic operation of the furnace while supplying products of a consistent quality. An optimum reheating process may be defined as one which produces heated stock to a desired discharge temperature and temperature uniformity while consuming minimum amount of fuel energy. A strategic framework to solve this multi-objective optimisation problem for a large-scale reheating furnace is presented in this paper. For a given production condition, a model-based multi-objective optimisation strategy using genetic algorithm was adopted to determine an optimal temperature trajectory of the bloom so as to minimise an appropriate cost function. Definition of the cost function has been facilitated by a set of fuzzy rules which is easily adaptable to different trade-offs between the bloom desired discharge temperature, temperature uniformity and specific fuel consumption. A number of scenarios with respect to these trade-offs were evaluated and the results suggested that the developed furnace model was able to provide insight into the dynamic heating behaviour with respect to the multi-objective criteria. Suggest findings that current furnace practice places more emphasis on heated product quality than energy efficiency.Item Open Access Nonlinear dynamic simulation and control of large-scale reheating furnace operations using a zone method based model(Elaevier, 2018-02-14) Hu, Yukun; Tan, C. K.; Broughton, Jonathan; Roach, Paul Alun; Varga, LizModern reheating furnaces are complex nonlinear dynamic systems having heat transfer performances which may be greatly influenced by operating conditions such as stock material properties, furnace scheduling and throughput rate. Commonly, each furnace is equipped with a tailored model predictive control system to ensure consistent heated product quality such as final discharge temperature and temperature uniformity within the stock pieces. Those furnace models normally perform well for a designed operating condition but cannot usually cope with a variety of transient furnace operations such as non-uniform batch scheduling and production delay from downstream processes. Under these conditions, manual interventions that rely on past experience are often used to assist the process until the next stable furnace operation has been attained. Therefore, more advanced furnace control systems are useful to meet the challenge of adapting to those circumstances whilst also being able to predict the dynamic thermal behaviour of the furnace. In view of the above, this paper describes in detail an episode of actual transient furnace operation, and demonstrates a nonlinear dynamic simulation of this furnace operation using a zone method based model with a self-adapting predictive control scheme. The proposed furnace model was found to be capable of dynamically responding to the changes that occurred in the furnace operation, achieving about ±10 °C discrepancies with respect to measured discharge temperature, and the self-adapting predictive control scheme is shown to outperform the existing scheme used for furnace control in terms of stability and fuel consumption (fuel saving of about 6%).