Economic evaluation and design of an electric arc furnace controller based on economic objectives

Oosthuizen, D.J.; Craig, I.K.; Pistorius, Petrus Christiaan

doi:10.1016/s0967-0661(03)00078-9

Cited by 42 publications

(46 citation statements)

References 5 publications

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“…Owing to the highly complex nature of the economic relations in most processes, assumptions usually need to be made in order to facilitate the development of PFs (Oosthuizen et al, 2004;Zhou and Forbes, 2003). The assumptions are:…”

Section: Assumptions Used For Pf Developmentmentioning

confidence: 99%

“…The preferred way to indicate economic performance is by integrating the product of a PF and PDF of a time series of a process variable over a period of interest (Zhou, 2002;Wei et al, 2007;Oosthuizen et al, 2004), as described in Section 3.4. Both the LOAD and SLEV are controlled well and do not deviate from the constraints and thus the relevant economic impact is null.…”

Section: Economic Performance Of the Base Casementioning

confidence: 99%

“…If no considerable difference occurs, blocking will probably affect the accuracy of the experiment results adversely (Fisher and Yates, 1953). It is usually regarded as good practice to keep an experiment as simple as possible (Oosthuizen, 2001).…”

Section: Experimental Designmentioning

confidence: 99%

“…Alternatively, the second option can be used, where the percentage values of DC=DP are divided by a nominal value such as the average operating cost of the process for a given period. This is the case in Oosthuizen et al (2004), where the PFs are expressed as percentage values of additional costs divided by the typical operating cost for a tap of an electric arc furnace. The use of percentage values has the advantage that sensitive production data can be protected and are consequently easier to obtain.…”

Section: Introduction To Milling Circuit Pfsmentioning

confidence: 99%

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Economic performance assessment of two ROM ore milling circuit controllers

Wei¹,

Craig²

2009

Minerals Engineering

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a b s t r a c tEconomic performance assessment of control systems is receiving increasing attention in both academia and industry. A relevant framework is proposed in Bauer and Craig (Bauer, M., Craig, I.K., 2008. Economic assessment of advanced process control -a survey and framework. Journal of Process Control 18 (1), 2-18) based on a comprehensive survey of the methods for economic analysis of advanced process control systems. The framework provides a formal procedure for the economic assessment of control systems. A practically motivated simulation study is used to cast the economic performance assessment of a nonlinear model predictive controller and three single-loop PID controllers for a run-of-mine milling circuit into this framework. Performance functions of the milling circuit, which dynamically relate the circuit process variables to monetary values, are developed based on a survey study on milling circuits and an in-depth literature study.

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Section: Assumptions Used For Pf Developmentmentioning

confidence: 99%

Section: Economic Performance Of the Base Casementioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introduction To Milling Circuit Pfsmentioning

confidence: 99%

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Economic performance assessment of two ROM ore milling circuit controllers

Wei¹,

Craig²

2009

Minerals Engineering

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“…A literature review of the existent EAF models [8][9][10][11][12][13][14][15][16][17][18][19][20] indicates a lack of accurate arc models that could be applied for smart sensing, control or optimization of the energy flows. It is assumed that such model should present specific features that support their industrial application, including minimum input requirement, sufficient accuracy, simple mathematics and short calculation times.…”

Section: Introductionmentioning

confidence: 99%

Low Computational-complexity Model of EAF Arc-heat Distribution

et al. 2015

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The paper presents a computational model and the corresponding algorithm for estimating the arc energy distribution to conductive, convective and radiative heat transfer in an electric arc furnace (EAF). The proposed algorithm uses channel arc model (CAM) in order to compute the distribution of the arc energy through empirical equations (to approximate arc radius), ideal gas law (to approximate arc density) and results of magneto-hydro-dynamic (MHD) models (to approximate arc pressure, temperature and velocity). Results obtained using the proposed algorithm are comparable with other similar studies; however, in contrast to other arc-energy distribution models, this model requires only two input variables (arc length and arc current) in order to calculate the energy distribution. Furthermore, simple algebraic equations used in the algorithm ensure minimal computational load and consequently lead to short calculation times which are approximately one hundred thousand (100 000) times smaller than solving the MHD model equations, making the algorithm suitable for real-time applications, such as smart monitoring and model-based control. The algorithm has been validated by two different approaches. First, the simulation results have been compared to a study dealing with arc-heat distribution in plasma arc furnace; and second, the proposed arc module has been integrated into the frame of a comprehensive EAF model in order to estimate the EAF temperature levels and compare them with operational EAF measurements. Both validations show high levels of similarity with the comparing data.KEY WORDS: arc current; arc heat distribution; arc length; channel arc model; EAF.approximately 75-85% of the total energy in low to medium power furnaces and approximately 50-60% of the total energy in ultra-high power furnaces (UHP).3) Implementing an accurate arc module in a comprehensive model-based EAF control, which ensures optimal control of arc length and slag height can lead to substantial energy saving and associated cost reduction. If a total energy reduction in a 200 ton UHP EAF with approximate annual production of 675 000 tones is 50 kWh/ton, with an assumption of 25 kWh/ton being the electrical energy, with the price of 10 $Cent/kWh, total annual energy savings will be equivalent to 1.7 million $ per year.It is known that the heat generated by the arcs is dissipated into the furnace by all three mechanisms of heat transfer (convection, conduction and radiation); however, the amount of the heat, transferred by each mechanism varies according to several factors, such as arc length, arc current, slag height, stage of melting etc. Development of a computational model of the arc, which allows estimation of the arc-heat distribution should therefore include all three types of heat transfer mechanisms, which can also be used to obtain the overall energy balance of the EAF. Application of such models may provide appropriate tools for optimizing the energy flow or use of model-based control systems in the EAFs.

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