Browsing by Author "Tan, C. K."
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Item Open Access Coupling detailed radiation model with process simulation in Aspen Plus: A case study on fluidized bed combustor(Elsevier, 2017-08-31) Hu, Yukun; Wang, Jihong; Tan, C. K.; Sun, Chenggong; Liu, HaoWhile providing a fast and accurate tool for simulating fluidized beds, the major limitations of classical zero-dimensional ideal reactor models used in process simulations become irreconcilable, such as models built into commercial software (e.g. Aspen Plus®). For example, the limitations of incorporating heat absorption by the water wall and super-heaters and inferring thermal reciprocity between each reactor model/module. This paper proposes a novel modelling approach to address these limitations by incorporating an external model that marries the advantages of the zone method and Aspen Plus to the greatest extent. A steady state operation of a 0.3 MW atmospheric bubbling fluidized-bed combustor test rig was simulated using the developed modelling approach and the results were compared with experimental data. The comparison showed that the predictions were in agreement with the measurements. Further improvement is to be expected through incorporating more realistic zoned geometry and more complex reaction mechanisms. In addition, the developed model has a relatively modest computing demand and hence demonstrates its potential to be incorporated into process simulations of a whole power plant.Item Open Access Feasibility study of biomass gasification integrated with reheating furnaces in steelmaking process(DEStech Publication Inc., 2019-11-04) Hu, Yukun; Chowdhury, Jahedul Islam; Katsaros, Giannis; Tan, C. K.; Balta-Ozkan, Nazmiye; Varga, Liz; Tassou, Savvas; Wang, ChunshengThis paper investigates the integration of biosyngas production, reheating furnace and heat recovery steam cycle, in order to use biosyngas directly as fuel in the furnace. A system model was developed to evaluate the feasibility of the proposed system from the perspective of heat and mass balance. To particularly study the impacts of fuel switching on the heating quality of the furnace, a three-dimensional furnace model considering detailed heat transfer processes was embedded into the system through an Aspen PlusTM user defined model. The simulation results show that biosyngas is suitable for direct use as fuel for reheating furnaces. Should CO capture be considered in the proposed system, it has a potential to achieve the capture without external energy input which results in so-called negative emissions of CO.Item Open Access Function value-based multi-objective optimisation of reheating furnace operations using Hooke-Jeeves algorithm(MDPI, 2018-09-03) Gao, Bo; Wang, Chunsheng; Hu, Yukun; Tan, C. K.; Roach, Paul Alun; Varga, LizImproved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO2 emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature.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%).