Industrial Experience inMaximizing Profit of Steel Production by

Integrated Optimization Technology

Benoit David, Yves Goldblatt and Yale Zhang *

N-SIDE S.A., Belgium

ICS 2015, Beijing

AGENDA

1. Company highlights2. Motivation3. Integrated Optimization4. Case studies5. Conclusions

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“N-SIDE’s ambition is to grow as a firm by making optimization tools more widely-used in the business world and contributing to a more intelligent and efficient allocation of human and natural resources.”

Company highlights

• A Belgium-based consulting company focusing on Operations Research and its applications

• 25 applied mathematicians and engineers specializing in modeling and optimization, with domain expertise in steel, energy & pharmaceutical processes

• Good relationship with our worldwide customers

An optimization solution provider

Electricity trading optimization

Clinical trial supply chain optimization

Pharma Power

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A unique solution for Steel Cost Optimization

Integrated optimization of industrial production & Cogen

Energy Process

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The industry is straining under the relentless pressure caused by:• years of excess steelmaking capacity• low profit margins• volatile market for raw materials• tight environmental regulations

Under this situation, the only sustainable strategy for a steel company is to become a low-cost producer.

Tough years for steel industry

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“Untapped” opportunities

Should we increase the production due to good market condition? Which end product to make for more profit? Which facility route to use?

What is the best S content in hot metal, taking into account the trade off between coke production cost (cheaper coal with high S content) and desulfurization cost?

Does desulfurization station have enough capacity to treat high-S hot metal?

Similar question to Si content in hot metal.

Should we increase MnO in sinter in order to reduce consumption of FeMn at SMS?

Should we increase coke strength? What will be the cost impact?

Depending on pellets availability and price, what will be the best production level for sinter?

Should we produce more coke for external sale, or more COG to reduce energy bill?Which raw material to choose,

and at what blend ratio?

How to distribute internal gases given the gas mixing constraint?

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• Coke Plant• Sinter Plant• Pellet Plant• Blast Furnace• Corex• Midrex• Steel Making Shop

• BOF• EAF• AOD/VOD• Casting

• Downstream• Power Plant

End-to-end integrated optimization

Mathematical models: technology + economics

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Productivity model

Blast furnace productivity can be related to:• RM blend• O2 in hot blast• Slag rate• Coke quality (permeability)

Blower productivity related to:• Blast rate• Blast speed

PCI grinder productivity related to:• Drying capacity• Grinding capacity

Miscellaneous

Alkali input constraints• K2O+Na2O

Uniformity constraints

Blending constraints

Loss in pig iron

Charging rate constraints

Max injection capacity can be related to:• RM blend• O2 in hot blast• Slag rate• Coke quality (permeability)

Thermal balance

Complete thermal balance including:• Materials and gases sensible heats• All major chemical reactions (reduction,

combustion, cracking, …)• Heat losses• Temperature profiles (hot blast, flame, reserve

zone, injection fuel, hot metal, slag, flue)• 3 zones (stack, hearth, tuyeres)

Flue gas composition and LCV

Reduction efficiency can be related to:• RM blend• O2 in hot blast• Slag rate• Coke quality (permeability)

Thermal balance at stoves• Gas mix requirements• LCV of gas vs. hot blast T°

Chemical balance

Hot metal / Slag equilibrium for:• Fe, C, Si, S, P, Mn, Ti, Zn, Cr• Ex S: [S]/(S) = f(basicity,T°) • Ex Si: [Si]=f(basicity, T°, productivity)

Residuals yield for:• Cu, Ni, Sn, Mo, Nb, V, B

Slag rate and properties• Ex : CaO/SiO2• Ex: CaO+MgO+Al2O3 / SiO2

Dust and sludge

Blast furnaces

Costs and Revenues

Raw materials

Utilities• O2/N2/Ar• Steam• Electricity• COG/BFG/NG

Operating costs (var/fix) (SAP items)• Maintenance• Refractories• Labor• Overhead

Revenues from recyclings• Slag/dust/sludge• RM fines• Pig iron

CO2 penalty

Typical orders of magnitude

Variables 45,000

Equations 25,000

… of which non-linear 1,000

Solving time < 3 min

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A sophisticated optimization model solved by non-linear programming

• Maximize company’s bottom line profit = revenue generated from both finished goods and by-products – raw material cost – utility/energy cost – operating cost – emission penalty cost

Objective

• Raw material selection, allocation and mix• Intermediate product flow, quantity and quality• Key process parameters• End-product mix and equipment utilization• Recycle utilization and energy consumption

Decision Variables

• Raw material market availability• Process mass & heat balance• Equipment capacity and maintenance requirements• Intermediate and end-product quality requirements• Energy/utility availability and emission regulations

Constraints

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Industrial Applications

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Systematic integration of material flow, energy flow and money flow between various shops all production stages, from raw material to end product capable to address trade-off opportunities b/w shopsStrategic optimization tool for senior management team with high ROI (less than a few months) overall profit being optimized with estimated cost

saving of 2 ~ 6 €/t of steel complementary to Level 1/2 daily operation systemsOptimal decision subject to technical, economical constraints and specific business rules Economic decisions ensuring technical feasibility Incorporating your own plant knowledge

Optimal raw material procurement strategy• true value assessment for optimal blending• contract (price & quantity) negotiation Optimal production flow & process parameters• allocation of hot metal, pellets, scrap, etc.• process lever optimization (S, Si in hot metal)Optimal commercial strategy• order book improvement to respond quickly to

changing market conditions• pricing optimization for end products Optimal investment decision• bottleneck identification• ROI analysis for new investmentBest practice for collaboration to break “silos”

Integrated Profit Optimization Tool

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Raw material evaluation:Limit Marginal Price Attractiveness of the next ton

LMP Calculation Available for all Raw Materials

Limit Marginal price

Optimized quantities

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Sensitivity analysis on Sulfur content in Hot Metal① Draw a curve of changing steel cost w.r.t.

sulfur content between 0.025% and 0.035%② Find target sulfur content for minimum cost③ Understand main trade-off between cost of

coke and desulfurization fluxes④ Recommend an optimal coal mix for decision

making, while respecting process constraints⑤ Estimate of cost savings compared to current

practice

12M€

Today Target

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Context

With recently improving steel demand in America market, a company started to plan increasing production, but this time, they would like to focus more on profitability rather than volume.

=> What will be the optimal production level for the given market conditions?

Case study 1: optimal decision on production level

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Marginal cost of steel slab

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Consumption of each family of raw materials

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Context

A cheap, high-S PCI becomes available in the market.

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Should it be used and if so, what will be the impact on slab cost?

Case study 2: evaluation of impact of new PCI on slab production cost

S (%) BF1 BF2 BF3

Base case without new PCI

0.041 0.040

0.042

Alternative case with new PCI

0.081 0.079

0.082

Financial impact on Difference

hot metal production cost -8.71 €/thm

desulfurization cost +0.66 €/thm

slab production cost -7.34 €/thm

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Optimal consumption of new PCI

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Conclusions

• SCOOP has made significant contributions to many steel plants and achieved annual benefit of 2-6 € per ton of steel.

• 3 benefits from SCOOP application:• Integration to untap new opportunities• Optimization to make smart decisions• Collaboration to break down “silos”

• It enables aggregating the data and the knowledge available at steel plant in order to provide quantitative and qualitative information that enhance decision-making process.

CONTACTwww.n-side.comyzh@n-side.com