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2000 Progress Report: Optimal Operation of Electric Arc Furnaces (EAF) to Minimize the Generation of Air Pollutants at the Source

EPA Grant Number: R826736
Title: Optimal Operation of Electric Arc Furnaces (EAF) to Minimize the Generation of Air Pollutants at the Source
Investigators: Ramirez, W. Fred
Institution: University of Colorado at Boulder
EPA Project Officer: Richards, April
Project Period: October 1, 1998 through October 1, 2001
Project Period Covered by this Report: October 1, 1999 through October 1, 2000
Project Amount: $109,305
RFA: Technology for a Sustainable Environment (1998)
Research Category: Pollution Prevention/Sustainable Development

Description:

Objective:

The objective of this research project is to model and optimize an electric arc furnace to minimize environmental impact and maximize productivity.

Progress Summary:

Professor Ramirez has held a National Science Foundation (NSF)/EPA Technology for a Sustainable Environment (TSE) grant for the past 3 years that initiated the modeling and optimization of electric arc furnaces to minimize environmental impact and maximize productivity (CTS-9816804 and R82673-01-0). A mathematical model (Matson, Ramirez, and Safe, 1997) based upon dynamic material and energy balances, coupled with equilibrium chemistry, has been developed and tested on two very different electric arc furnaces. Plant A is a very aggressive operation that uses 88 megawatts (MW) of electrical power, a total carbon addition of 8,000 lbs., and a total lancing oxygen of 113,000 SCF. Its tap to tap time is 55 minutes. Plant B is much less aggressive and uses 60 MW of electrical power, a total carbon addition of 3,000 lbs., and an aggressive lancing oxygen amount of 125,000 SCF. Its tap to tap time is 88 minutes. Figure 1 shows the predictive capability of the model for Plant A, while Figure 2 shows the predictive capability for Plant B. In both cases, the fundamental model is capable of predicting the CO2 and O2 production rates well. The CO and H2 production rates are captured very well for Plant A and less well for Plant B. All in all, the predictive abilities of the model are good, especially considering the inaccuracies in the industrial data used. In addition to modeling, we have carried out some initial dynamic optimization studies (Matson and Ramirez, 1999; Ramirez and Matson, 2000). We have applied the technique of Iterative Dynamic Programming to determine optimal carbon addition rates, oxygen lancing rates, and excess burner oxygen amounts to minimize a performance index that minimizes carbon monoxide production, iron oxide production to maximize yield, and tap to tap time. The performance index is given as Equation 1, and the weighting factors wi were chosen so that each term of the performance index is of the same order of magnitude based upon standard operation.

The penalty function, P, is zero as long as the bath temperature at the final time is above the tapping temperature.

The results of the dynamic optimization are very impressive and are given in Table 1. For Plant A, we are able to reduce the amount of carbon monoxide produced by 99.4 percent. This is a tremendous result in pollution prevention. The iron oxide at the final time is reduced by 99.8 percent, which means that the final product has a yield of almost unity. The tap to tap time was actually increased by 9.7 percent, which is the penalty one must pay to gain so significantly in CO and FeO reduction. The performance index of Equation 1 was reduced by 52 percent. Similar results were obtained for Plant B, with a 92 percent reduction in CO production, a 61 percent reduction in iron oxide, and a 0.3 percent reduction in tap to tap time. Overall, the performance index was reduced by 32 percent. These are very impressive results that imply that significant pollution prevention opportunities exist in applying optimal control theory to electric arc operation.

Figure 1.

Figure 2.

Table 1.

Plant A Plant B
Base Case
Optimal
Base Case
Optimal
Controls
Lancing
113,770 SCF
14,140 SCF
150,221 SCF
117,500 SCF
Carbon Injection
9560 lb.
3201 lb.
4149 lb.
6075 lb.
Excess O2
0 SCF
23,294 SCF
0 SCF
55,000 SCF
Performance
CO
0.788
0.005
0.271
0.021
(-99.4%)
(-92%)
FeO
0.503
0.001
1.198
0.467
(-99.8%)
(-61%)
Time
1
1.097
1.6
1.595
(+9.7%)
(-0.3%)
Performance Index
2.29
1.103
3.069
2.083
(-52%)
(-32%)

Future Activities:

Optimization using other objectives is planned.

Journal Articles:

No journal articles submitted with this report: View all 3 publications for this project

Supplemental Keywords:

electric arc furnace, dynamic optimization, mathematical modeling. , Industry Sectors, Sustainable Industry/Business, Scientific Discipline, RFA, Technology for Sustainable Environment, Sustainable Environment, Manufacturing - NAIC 31-33, Environmental Engineering, Environmental Chemistry, Ecology and Ecosystems, Economics and Business, cleaner production, waste reduction, green design, electric arc furnaces, steel production, Common Sense Initiative, engineering, waste minimization, industrial innovations, carbon injection, waste monitoring, air pollution control, energy efficiency, innovative technology, material balance models, emission controls, pollution prevention

Progress and Final Reports:
Original Abstract
Final Report

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The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.

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