Grantee Research Project Results

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: Chung, Serena
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) RFA Text |  Recipients Lists
Research Category: Pollution Prevention/Sustainable Development , Endocrine Disruptors , Sustainable and Healthy Communities

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.

Table 1.

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

Progress and Final Reports:

Original Abstract

1999

Final Report