APPLICATION OF DECISION SUPPORT SYSTEM FOR THERMAL BALANCE CONTROL IN THE IRONMAKING BLAST FURNACE Bryan D. Wright and Paul Zulli
BlueScope Steel Research Old Port Rd Port Kembla 2505
[email protected]
ABSTRACT BlueScope Steel operates two mid-sized ironmaking blast furnaces producing approximately 14000 tonnes of pig iron per day. The blast furnace process consumes a large amount of energy to convert iron ore into molten iron, and minimising this whilst maintaining process stability is an important technical goal for any steelworks. A decision support system (DSS) was developed to help maintain the thermal balance of the blast furnace by suggesting the timing and amount of changes to one component of the energy input (pulverised coal), potentially allowing a reduction in the overall energy consumption and CO2 production. The DSS recommended an action, or no action, based on a combination of standard operating procedures and knowledge gained from domain experts which was then encoded into rule engines. The acquisition of knowledge from domain experts was an important component of the system and the subsequent ability of the system to help disseminate the “best practice” amongst all operators. The forward chaining rule engines were based on simple “if-then-else” rules. The rule engines were assessed simultaneously every 6 minutes. A key feature of the system was that conflicting recommendations were arbitrated into a single recommendation for action. The system was implemented on-line at No. 6 Blast Furnace between 2007 and 2009. Although no significant improvement in thermal control occurred during the implementation, control room operators felt that the system helped them with thermal balance control. Key learning’s with respect to decision support system development were that production departments need be engaged with the system, the system needs to be helping operators reach ambitious targets and that the knowledge expressed in the system needs to be understandable by non-technologists. INTRODUCTION The ironmaking blast furnace is a large, counter-current reactor with complex thermochemical reactions occurring from 100-2000ºC, with large time lags between changes to input conditions and the resultant output, such as input fuel and output product temperature. Therefore often a significant amount of domain specific knowledge is needed to operate an ironmaking blast furnace. The area of thermal balance control is a critical part of ironmaking blast furnace practice, as improved thermal control provides better quality product and higher efficiency of the overall process which can be potentially used to reduce energy consumption and CO2 production. If the ironmaking blast furnace is allowed to go below a critical thermal level, significant problems can occur, potentially resulting in millions of dollars of lost production. Given the importance of thermal balance control and its reliance on operator knowledge, decision support systems (DSS) have been developed by a number of organisations with the aims of (1) improving the consistency of operator decisions, (2)
B. Wright, P. Zulli enhancing the ability to pass on the best practice (knowledge) and (3) enabling complex data analysis in real time. Examples of such thermal balance control systems are: • • • • •
Arcelor-Mittal (Le Goc, 2004) (Lazaric et al., 2003) (Le Goc, 2003) (Paul Wurth, 2008), Voest-Alpine (Bettinger et al, 2004) (Bettinger et al., 2005) (Kallo et al., 1999) (Edwards et al., 2008) (Martinez et al., 2008) (Horl et al., 2007a) (Horl et al., 2007b) Tata Steel Europe (Warren and Bell, 2005), NSC (NSC, 2008) JFE (Maki et al., 1999)
Tab. 1 provides a brief description of some aspects of the thermal balance control systems of Arcelor-Mittal (SACHEM), Voest-Alpine (VAiron TNG) and Tata-Steel Europe (CORUS UK). These systems have been deployed at a number of different ironmaking blast furnaces and some (SACHEM and VAiron TNG) are available for commercial purchase. In terms of complexity of the system and the focus on knowledge management principles, SACHEM is by far the most advanced of the systems. SACHEM provides a generic framework for problem-solving and knowledge management, independent of thermal balance control. VAiron and Corus UK systems are not as complex as they were developed with a primary focus on thermal balance control only. With respect to control methods in these systems, primarily they are openloop control where an operator needs to intervene for thermal balance control. The more complex systems (SACHEM and VAiron) provide both a mechanism for diagnosis of the problem and providing an associated corrective action. However neither of these systems formalise feedback from the operators on the actual performance of the system. The simpler Corus system doesn’t diagnose problems with thermal balance control and instead suggests corrections based on changes in key process variables. Operator feedback is used however to ensure the knowledge is up-todate and relevant in light of changing process conditions such as different input materials. Tab.1: Review of thermal balance control systems for ironmaking blast furnaces SACHEM
VAiron TNG
Corus UK
No. of BF’s Deployed
>5
> 10
3
Complexity
High
Medium
Low
Knowledge Management Component
High
Low
Low
System Control Method
Open-Loop
Open-Loop and Closed-Loop
Open-Loop
System Focus
Diagnosis and Corrective Action
Diagnosis and Corrective Action
Corrective Action
Feedback into System?
No
No
Yes
Facet
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B. Wright, P. Zulli
This paper describes the development and implementation of a thermal balance control decision support system for No. 6 Blast Furnace, Port Kembla. It discusses the structure of the forward chaining rule engines, an example of application of the system and issues with implementation. DESCRIPTION OF HLC-DSS The thermal balance control decision support system (further referred to as “HLC-DSS”) was developed using a combination of knowledge from existing critical procedures (herein referred to as “HBL-01”) and knowledge acquired from domain experts. The intent of the system was to recommended changes to the energy input of the blast furnace, in the form of pulverised coal (further referred to as “PCR”) so as to maintain the thermal balance of the system (i.e. the product temperature being within the aim range). The knowledge bases (herein referred to as “KBs”) or “rule engines” were constructed as simple “if-then-else” rules. This facilitated ease of programming and presented the knowledge in a form that could be understood by non-experts. Three separate KBs were developed in HLC-DSS: 1. Safety Net KB: knowledge based on a existing critical procedure HBL-01 which provided recommendations if the thermal balance was significantly away from aim. Fig. 1 shows the rules for this KB which are straight-forward and deals with major process deviations.
Fig. 1: The rules in the Safety Net KB (associated actions are outlined in HBL-01). 2. Trim KB: knowledge acquired from domain experts and coded into rules. This KB recommended small changes to the energy input to maintain optimum thermal balance. All rules in this KB are assessed every 6 minutes. If a rule in the KB is activated, the change to PCR associated with this rule is noted for consideration in the PCR Controller. The arrow direction in Fig. 2 shows recommended change to PCR if that rule is active (up arrow for increasing PCR and down arrow for decreasing PCR). For description of the key process variables in Fig. 2, refer to the Glossary section.
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Fig. 2: Rules in the Trim KB (arrow represents the recommended change in PCR associated with the rule) 3. PCR Controller: a hierarchical KB which arbitrates requests to change PCR from multiple rules in the Trim KB or Safety Net KB and produces a single recommended action to be presented to the control room operator. Fig. 3a schematically describes the relationship between the PCR Controller and Trim and Safety Net KBs. Fig. 3b lists the “if-then-else” rules in order that they are considered. If any rule is active, then no further rules are assessed in this KB. The knowledge for this KB was acquired from domain experts.
(a)
(b)
Fig. 3: (a) The relationship between knowledge bases and PCR Controller and (b) the rules in the PCR Controller. CASE STUDY OF SYSTEM PERFORMANCE A brief analysis is provided below of a case where the daily average HMT is on target however there are significant variations in HMT throughout the day. Fig. 4 shows the operator action (dark blue line – PCR), HLC-DSS recommended action (light blue line – HLC-DSS) and the process response, HMT (yellow line). It is noted that changes in PCR (dark blue line) will result in changes to HMT (yellow line) approximately 2 hours into the future due to lags in the process and measurements. The black line is the aim HMT and the dark dotted line represents +/- 10ºC of aim. The magenta line represents the aim PCR based on a mass balance. 4
B. Wright, P. Zulli In this example, the operator is taking action to change the energy input too late and causes a significant deviation in the product HMT. HLC-DSS is providing recommendations to change the energy input in what would appear to be the correct direction to reduce HMT variability. Eight points are noted on Fig. 4 representing 4 pairs of “recommendation/action and response”. Each of these pairs will be discussed below: 1. Early in the period, HLC-DSS is suggesting “no change” to the energy input. The operator increases the energy input (PCR increases from 52 to 54 t/h) 2. HMT rises above aim to 1520ºC 3. HLC-DSS recommends increasing PCR to 53t/h. Operator maintains energy input at 52 t/h 4. HMT swings to be well below aim at 1470ºC 5. HLC-DSS is detecting the energy input needs to reduce. Operator maintains energy input. 6. HMT rises above aim 7. HLC-DSS recommends further reduction in energy input to bring the HMT back into the aim range. Operator increases the energy input (PCR). 8. HMT remains high and out of the aim range 1525
2
53
8
6
1515
5
3
1520
1510
PCR (t/h)
1505 52
1500
1
1495 1490
51
1485
Valid HMT (degC)
54
1480 50
1475
4
7
1470
49 1465 13/06/2008 14/06/2008 14/06/2008 14/06/2008 14/06/2008 14/06/2008 15/06/2008 15/06/2008 19:12 0:00 4:48 9:36 14:24 19:12 0:00 4:48 PCR
Aim PCR
HLC-DSS
Valid HMT
Fig. 4: Example of system recommendations and operator responses for thermal balance control at No. 6 Blast Furnace.
REVIEW OF OVERALL SYSTEM PERFORMANCE The HLC-DSS was implemented at No. 6 Blast Furnace, Port Kembla between 2007 to 2009. Approximately 2 years of development preceded its implementation. During this time, knowledge was acquired from domain experts and KBs were developed, refined and tested. Fig.5 shows the thermal balance control key criteria over the period of the operation of the HLC-DSS. It can be concluded that the implementation of the HLCDSS did not significantly improve the thermal balance control at No. 6 Blast Furnace. 5
B. Wright, P. Zulli It is noted that the operators’ thermal balance control was judged against criteria of the daily average HMT being within 5ºC of aim (without regards for deviation of HMT within the day). Over the period analysed, this criteria was met 50% of the time. No target was set to reduce the intra-day variability of HMT. 1550
100
1540
90
1530
80
1520
70
) C g 1510 e d ( T M H e 1500 g a r e v A y ila 1490 D
60
50
40
1480
30
1470
20
1460
10
1450
) C g e d ( T M H f o n o tia iv e D d ra d n a tS y li a D
0
10/ 10/ 06
28/ 04/07
14/ 11/ 07 HMT
1/ 06/08 SD
18/ 12/08
30 per. Mov. Avg. (HMT)
6/ 07/ 09
22/ 01/ 10
10/ 08/10
30 per. Mov. Avg. (SD)
Fig. 5: Thermal balance assessment between 2007 and 2009 at No. 6 Blast Furnace. Despite the lack of improvement in thermal balance control, feedback from control room operators about the performance of HLC-DSS was very positive. An anonymous survey was performed in October 2008 to assess HLC-DSS. 6 of the 10 control operators provided feedback regarding the system. The feedback on the system was that: • • • • • •
They used HLC-DSS to control thermal balance “sometimes” to “always” It worked best in stable operations It performed poorly when the process is undergoing unexpected and large changes but not those deviations were not significant enough to invoke the Safety Net KB The HLC-DSS was helpful between “sometimes” and “always” The HLC-DSS was felt to be “maybe needed” to “always needed” Confidence in HLC-DSS was rated as “sometimes” to “always”
ISSUES WITH IMPLEMENTATION HLC-DSS provided a significant learning opportunity with respect to the difficulty of implementing DSS systems in a production environment. Whilst HLC-DSS was a technical success (i.e. implemented in a production environment and received positive feedback from operators), the system stopped development in 2009 and it was decommissioned in 2010. 6
B. Wright, P. Zulli A key factor in the lack of improvement in thermal balance control was that no target was set to reduce intra-day variability of HMT. Without such a target, the operators did not need to follow the recommendations of the system and could utilise their existing knowledge to meet the existing thermal balance control target. It was felt that if a target was set on HMT variability, rather than daily average, the system would be more relevant to the operators and increased their desire to use the system. At the time of the closure of HLC-DSS, the following points were considered relevant to future DSS developers: • • • • • • •
Focus on the control room operators requirements Ensure operating departments are engaged with DSS and feel responsible for ongoing support Have “ambitious” targets to which the DSS can provide support DSS may involve some element of cultural change and therefore need to have a very long time-frame and sufficient technical resourcing Technologists need to convey the acquired knowledge in a way that is easy to understand for management and control room operators Significant business value can be obtained from the process of knowledge acquisition itself DSS development needs to be evolutionary and flexible
CONCLUSIONS This paper describes the development and implementation of a thermal balance control decision support system for No. 6 Blast Furnace, Port Kembla. Improved thermal balance control could lead to reduced energy usage and CO2 production. Knowledge bases were developed on the basis of existing procedures or via knowledge acquisition from domain experts. The knowledge bases formed three separate forward chaining rule engines which were assessed every 6 minutes to produce a single recommendation to the control room operator. A key feature of the system was that potentially conflicting advice was arbitrated into a single recommendation for action. During the period of the implementation of the thermal balance control decision support system (2007-2009), thermal balance control did not significantly improve. Despite the system being favourably viewed by control room operators, the system was decommissioned in 2010. Key learning’s of the closure of the system are that production departments need be engaged with the system, the system needs to be helping operators reach ambitious targets and that the knowledge expressed in the system needs to be understandable by non-technologists.
REFERENCES Le Goc, M. “SACHEM, a Real Time Intelligent Diagnosis System Based on the Discrete Event Paradigm”, SIMULATION, 80, 11, pp. 591 (2004). Lazaric, N., Mangolte, P. and Massue, M. “Articulation and Codification of Collective Know-How in the Steel Industry: Evidence from Blast Furnace Control in France”, Research Policy, 32, pp. 1829 (2003). Le Goc, M. “Extensive Large Scale Real Time Knowledge Based Systems Design: The SACHEM Example”, Presentation at DLS-03, Tuscon (2003). 7
B. Wright, P. Zulli Paul Wurth, http://www.paulwurth.com/content/products/ironmaking-11-furnaceautomation.html (2008). Whitfield, P. “A New Innovation in Blast Furnace Charging SIMETALCIS Gimbal Top”, Proc. AIST Conf., Pittsburgh, USA (2008). Bettinger, D., Pillmair, G., Schurz, B. Schaler, M. and Stohl, K. “Benefits Due to Expert System Controlled Blast Furnace Operation”, Proc. AISTech Conf., 1, pp. 409 (2004). Kallo, S. Inkala, P. and Karjalahti, T. “Practical Use of Models and BF Expert System to Improve Blast Furnace Operation at Rautaruukki Steel, Raahe”, McMaster Symposium on Iron and Steelmaking pp. 132-152 (1999). Bettinger, D., Schurz, B., Stohl, K., Ritamak, O. and Piirainen, I. “Expert System Control of Blast Furnaces – The Next Step”, Steel Times International, October, pp. 14 (2005). Horl, J., Schaler, M. and Stohl, K. “Blast Furnace Optimization – The Next Generation”, Iron and Steel Technology, March, pp. 52 (2007a). Horl, J., Schaler, M. and Stohl, K. “Blast Furnace Optimization, The Next Generation”, La Revue de Metallurgie – CIT, May, pp. 211 (2007b) Martinez, F, Sepulveda, V. and Ballesteros, M. “Improvements in BF5, the AHMSA Experience”, Proc. AIST Conf., Pittsburgh, USA (2008). Edwards, B., Cheng, A., Ebner, B., Street, S., Hausemer, L. “Severstal NA C Blast Furnace 2007 Revamping Equipment Design Transforms C BF into a World Class Operation”, Proc. AIST Conf., Pittsburgh, USA (2008). Warren, P. and Bell, T. “Blast Furnace Thermal Control and Aerodynamics Modelling using Advanced Signal Processing Methods in CORUS UK”, European Coke and Ironmaking Congress, Stockholm pp. We-4 (2005). Maki, Y, Inayama, A., Ino, K. “The Latest Technologies for Process Control and Automation in Blast Furnace”, Kawasaki Steel Research, 31, 4, pp. 216-221 (1999). NSC, http://www.kimitsu.nsc.co.jp/eng/process/process01.html (2008).
GLOSSARY PCR : Pulverised coal rate (tonnes per hour) CO+CO2 : Composition of CO and CO2 in off gas (%) HMB2 SRE: Heat and mass balance model prediction of stack reduction efficiency (%) Charge Rate: Average number of charges to the blast furnace in the previous 4 hours (-) HMB2 HMT: Heat and mass balance model predicted hot metal temperature (ºC) HMT: Measured hot metal temperature (ºC) [%Si]: Measured silicon content of hot metal (%) Production Rate: Calculated production rate from mass balance model (kilogram per hour) Sinter FeO: Nominal iron oxide content of ferrous sinter feed (%) HBL-01: Critical standard operating procedure for controlling thermal balance when the process is significantly away from aim thermal balance 8
B. Wright, P. Zulli
BRIEF BIOGRAPHY OF PRESENTER The author has worked for BlueScope Steel in the area of ironmaking research for more than 15 years.
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