Model-based multi-objective optimisation of reheating furnace operations using genetic algorithm

An 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

Nonlinear dynamic simulation and control of large-scale reheating furnace operations using a zone method based model

Modern 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%)

Function value-based multi-objective optimisation of reheating furnace operations using Hooke-Jeeves algorithm

Improved 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

Modelling and simulation of steel reheating processes under oxy-fuel combustion conditions – Technical and environmental perspectives

This paper investigates the impact of flameless oxy-fuel combustion on the thermal performance of a pilot-scale steel reheating furnace. A comprehensive mathematical model, based on the zone method of radiation analysis, was developed, which takes into account the non-grey behaviour of the furnace atmosphere under oxy-fuel combustion conditions. The model was subsequently used to simulate the temperature profile of an instrumented slab used in the experiment. The results showed that the predicted slab temperature profile along the furnace is in good agreement with measurement. However the model over predicted the absolute slab temperatures due to the influence of formation of oxide scales on the slab surface, which was not taken into account in the current model. When compared to air-fuel combustion simulation, the results of oxy-fuel combustion also indicated a marked improvement in the furnace specific fuel consumption (approximately 16%). This was mainly due to the enhanced radiative properties of the furnace atmosphere and reduced exhaust energy losses as the result of less dilution effect from nitrogen. This resulted in reduction in the overall heating time by approximately 14 min. Furthermore, if the economics of carbon capture is taken into consideration, theoretically, the energy consumption per kilogram of CO2 captured can be reduced from 3.5 to 4.2 MJ kg−1 to 0.96 MJ kg−1. In conclusion, the current studies support the view that oxy-fuel combustion retrofitting to reheating furnaces is a promising option, both from a technical and from an environmental point of view