An improved LP model for energy optimization of the integrated iron and steel plant with a cogeneration system in China

Abstract

In an integrated iron and steel plant with a cogeneration system, recycled energy is continuously transported into the cogeneration system and the electricity is continuously generated, and both of them could not be stored for a long time. Moreover, the generation and consumption of electricity is irregular, which may bring about more unexpected imbalances. Therefore, it is a crucial issue to schedule the entire energy system by optimizing the operation of energy utilization, which includes the raw energy in the production system, the generation electricity in the cogeneration system and the recycled energy in these two systems. In this paper, an improved Linear Programming model for energy optimization in the integrated iron and steel plant with a cogeneration system is established. The improved model focuses on controlling the whole energy flow and scheduling the whole energy consumption in the entire energy system between the production system and cogeneration system through optimizing all kinds of energy distribution and utilization in an integrated iron and steel plant with a cogeneration system. Case study shows that the improved model offers the optimal operation conditions at the higher energy utilization, lower energy cost and lower pollution emissions.

Keywords

Integrated iron and steel plant / energy optimization / linear programming / recycled energy

References

[1] Andersen J.P., Hyman B.. Energy and material flow models for the USsteel industry. Energy, 2001, 26: 137-159.

[2] Cai J., Wang J., Lu Z., Yin R.. Material flow and energy flow in iron & steel industry and correlation between them. Journal of Northeastern University (Natural Science), 2006, 27: 979-982.

[3] China I., Steel As.s.o.c.i.a.t.i.o.n.. Comprehensive Statistical Data on 50 Years of China’s Iron & Steel Industry. China Metallurgical Industry Press, 2003

[4] Dai C., Li Y.P., Huang G.H.. An interval-parameter chance-constrained dynamic programming approach for capacity planning under uncertainty. Resources, Conservation and Recycling, 2012, 62: 37-50.

[5] Dai T.J.. The influence of iron flow on iron resource efficiency in the steel manufacturing process. Resources, Conservation and Recycling, 2011, 55: 760-771.

[6] Davidson M.R., Dogadushkina Y.V., et al. Mathematical model of power system management in conditions of a competitive wholesale electric power (capacity) market in Russia. Journal of Computer and Systems Sciences International, 2009, 48: 243-253.

[7] Dong C., Huang G.H., Cai Y.P., Xu Y.. An interval-parameter minimax regret programming approach for power management systems planning under uncertainty. Applied Energy, 2011, 88: 2835-2845.

[8] General Administration of Quality Supervision, InspectionQuarantine of the Peoples Republic of China AQSIQ. Emission Standard for Air Pollutants from Steel Smelt Industry, 2012

[9] Gu Z., Xu A., Chang J., Li S., Xiang Y.. Optimization of the production organization pattern in Tangshan Iron and Steel Co., Ltd. Journal of Iron and Steel Research, 2014, 21(S1): 17-22.

[10] Huang G.H., Baetz B.W., Patry G.G.. Grey integer programming: an application to waste management planning under uncertainty. European Journal of Operational Research, 1995, 83: 594-620.

[11] Jiang Z., Zhang X., Jin P., Tian F., Yang Y.. Energy-saving potential and process optimization of iron and steel manufacturing system. International Journal of Energy Research, 2013, 37: 2009-2018.

[12] Kim J.H., Yi H., Han C., Park C., Kim Y.. Plant-wide multiperiod optimal energy resource distribution and byproduct gas holder level control in the iron and steel making process under varying energy demands. Computer Aided Chemical Engineering, 2003, 15: 882-887.

[13] Kim J.H., Yi H.-S., Han C.. A Novel MILP model for plantwide multiperiod optimization of byproduct gas supply system in the iron-and steel-making process. Chemical Engineering Research and Design, 2003, 81: 1015-1025.

[14] Kong H., Qi E., He S., Li G.. MILP model for plant-wide optimal by-product gas scheduling in iron and steel industry. Journal of Iron and Steel Research. International, 2010, 17: 34-37.

[15] Kong H., Qi E., Li H., Li G., Zhang X.. An MILP model for optimization of byproduct gases in the integrated iron and steel plant. Applied Energy, 2010, 87: 2156-2163.

[16] Larsson M., Wang C., Dahl J.. Development of a method for analysing energy, environmental and economic efficiency for an integrated steel plant. Applied Thermal Engineering, 2006, 26: 1353-1361.

[17] Liu X., Da Q., Hang S.. Interval number coefficients transportation problem model and its fuzzy goal programming solution. Journal of Industrial Engineering / Engineering Management, 1999, 13: 6-8.

[18] Ma G., Cai J., Zeng W., Dong H.. Analytical research on waste heat recovery and utilization of China’s iron & steel industry. Energy Procedia, 2012, 14: 1022-1028.

[19] Michaelis P., Jackson T.. Material and energy flow through the UKiron and steel sector. Part 1: 1954-1994. Resources, Conservation and Recycling, 2000, 29: 131-156.

[20] Michaelis P., Jackson T.. Material and energy flow through the UKiron and steel sector: Part 2: 1994-2019. Resources, Conservation and Recycling, 2000, 88: 2835-2845.

[21] Ni D.W.. Energy optimization and performance evaluation of steel supply chain based on circular economy. Dissertation, 2011

[22] Potter A., Mason R., Naim M., Lalwani C.. The evolution towards an integrated steel supply chain: a case study from the UK. International Journal of Production Economics, 2004, 89: 207-216.

[23] Price L.. China’s Top-1000 Energy-Consuming Enterprises Program: Reducing Energy Consumption of the 1000 Largest Industrial Enterprises in China. Lawrence Berkeley National Laboratory, 2008

[24] Strzalka R., Erhart T.G., Eicker U.. Analysis and optimization of a cogeneration system based on biomass combustion. Applied Thermal Engineering, 2013, 50: 1418-1426.

[25] Sun W., Cai J.J.. Material flow, energy flow and energy flow network in iron and steel enterprise. Post-Consumer Waste Recycling and Optimal Production, 2012 243-258.

[26] Thollander P., Mardan N., Karlsson M.. Optimization as investment decision support in a Swedish medium-sized iron foundry–a move beyond traditional energy auditing. Applied Energy, 2009, 86: 433-440.

[27] Wang K., Wang C., Lu X., Chen J.. Scenario analysis on CO₂ emissions reduction potential in China’s iron and steel industry. Energy Policy, 2007, 35: 2320-2335.

[28] Wei Y.-M., Liao H., Fan Y.. An empirical analysis of energy efficiency in China’s iron and steel sector. Energy, 2007, 32: 2262-2270.

[29] Yokoyama R., Ito K., Matsumoto Y.. Optimal sizing of a gas turbine cogeneration plant in consideration of its operational strategy. J Eng Gas Turbines Power, 1994, 116: 32-38.

[30] Yu Q., Lu Z., Cai J.. Calculating method for influence of material flow on energy consumption in steel manufacturing process. Journal of Iron and Steel Research, International, 2007, 14: 46-51.

[31] Zäpfel G., Wasner M.. Warehouse sequencing in the steel supply chain as a generalized job shop model. International Journal of Production Economics, 2006, 104: 482-501.

[32] Zhang L., Wu L., Zhang X., Ju G.. Comparison and optimization of mid-low temperature cogeneration systems for flue gas in iron and steel plants. Journal of Iron and Steel Research, 2013, 20: 33-40.