Novel Mathematical Modelling and Optimization Dig Significant Profit and Carbon Emissions Reduction for the Iron and Steel Industry

Significance 

Among the most energy intensive industries globally, iron and steel are at the fore front, and likewise is their carbon dioxide emission rate. By-product gases have already been established as vital players in providing alternative energy for production units and utility system in the iron and steel industry. Furthermore, it has already been proven that with optimal distribution of these byproduct gases in the iron and steel plants, the energy cost and carbon dioxide emissions can be considerably reduced. Unfortunately, such optimal distribution is not trivial because it involves modelling of production units, industrial boilers, steam turbines, combined heat and power units, and waste heat and energy recovery units. Additionally, significant realistic operational features such as byproduct gas mixing, byproduct gas level control in dedicated gasholders, different levels of steam requirement, minimum heating and energy requirement, maximum allowable burner switches and dynamic demands of by-product gases, steam and electricity and varying consuming rates of by-product gases in production units should be taken into account. All these collectively result in a large-scale complex combinatorial problem.

Researchers at the University of Manchester, Dr. Jie Li and Dr. Li Sun in collaboration with Dr. Yujiao Zeng and Professor Xin Xiao at the Institute of Process Engineering, Chinese Academy of Sciences with Dr. Hechang Li at Shougang Jingtang United Iron & Steel Co. Ltd. and Professor Christodoulos A. Floudas at the Texas A&M Energy Institute developed a novel multi-period mixed-integer linear programming model for optimal distribution of byproduct gases, steam, and power in an iron and steel plant. In their work, they purposed to address all the limitations in the existing literature and incorporated all realistic operational features. Their work is currently published in the research journal, Energy.

The research method employed begun with the development of a multi-period mixed integer linear programming model for optimal distribution of byproduct gases, steam and power in production units, boilers, turbines, combined heat and power units, and waste heat and energy recovery units simultaneously. Next, the researchers assumed the consuming rates of byproduct gases in production units to be variables and the demands of by-product gases, steam and electricity in production units to be piecewise constant in order to capture realistic dynamic features. They then introduced new binary variables so as to determine electricity purchase or sale decision with each having different price. Several important practical features such as fuel selection, gasholder level control, ramp rate variation, piecewise constant generation rates of byproduct gases, and piecewise constant demand profiles of byproduct gases, steam and electricity were also incorporated to make their model viable.

The authors observed that the optimal operating cost is obtained within 2 CPU seconds for an industrial example using the proposed model, which is reduced by 6% compared to that from actual operation. This significant improvement mainly arises from the reduction of the purchased electricity and coal, reduction of gasholder level deviation from the normal level, and reduction of burner on/off switches. In addition, it was observed that zero byproduct gas emission is achieved, gasholder levels are controlled in their safe operational zone at any time, and natural gas is not required to be purchased from the proposed model, which is consistent with those from actual operation.

 

 

Novel Mathematical Modelling and Optimization Dig Significant Profit and Carbon Emissions Reduction for the Iron and Steel Industry. Advances in Engineering
Figure 1 Superstructure of the distribution of byproduct gases, steam and electricity for the industrial example

About the author

Dr Yujiao Zeng is currently an associate professor in the Institute of Process Engineering (IPE), Chinese Academy of Sciences (CAS). She received her Bachelor Degree in automation engineering from Central South University in China in 2007. Then, she went to Automation Research and Design Institute of Metallurgical Industry in Beijing for master study and earned her Master Degree in Automation engineering in 2010. After that, she pursued her PhD study in Central Iron and Steel Research Institute in Beijing and obtained her Ph.D degree in Metallurgical automation engineering in 2015. She was appointed as a postdoctoral research associate in the Institute of Process Engineering, Chinese Academy of Sciences from July 2015 to July 2017.

Her current research interests include distribution energy systems operation, planning and scheduling, computational intelligence, optimization and control, and their application to Industrial energy systems. She has delivered many presentations in some important international conferences such as WCCI and WCICA. She has obtained 3 patents, published more than 10 peer-reviewed journal papers in high profile journals such as Energy, Electric Power Components and Systems and International Journal of Iron and Steel Research.

About the author

Dr Xin Xiao is currently working in the Institute of Process Engineering (IPE), Chinese Academy of Sciences (CAS) as Professor, Assistant Director and the Director of the Development Priority Office. He obtained his PhD degree from IPE in 2000. Then he was appointed as a postdoctoral research associate in Tsinghua University (2000-2002), then Join IPE, CAS till now. From 2009 to 2010 he collaborated with Professor Christodoulos A. Floudas on Global Optimization (GO) in Princeton University on hybrid energy process innovation as visiting fellow. He has nearly 20 years research experiences on process synthesis and design for biochemical process and energy system.

His research expertise mainly related to the application of GO in (1) energy strategic planning which serves as member of advisory working group for China energy 13th five-year-plan, (2) large scale refinery scheduling based on data-driven modeling and GO framework for entire petrochemical planning operations with dramatic profit incensement, (3) other areas such as achieving 50% weight deducing for lightweight gearbox development, hybrid process innovation and food water energy nexus for agriculture land use.

He has obtained 7 patents, published more than 30 peer reviewed papers in which the work on Duckweed to IECR was reported by American Chemical Society Press Release on March 6th, 2013. Besides these, he accomplished more than 30 strategic researches and submitted nearly hundred corresponding internal reports.

About the author

Dr Jie Li is currently a Lecturer in the Centre of Process Integration of School of Chemical Engineering and Analytical Science at The University of Manchester and a responsible editor in Computers and Applied Chemistry. He obtained his PhD degree from National University of Singapore in 2009. Then he was appointed as a postdoctoral research associate in Princeton University (2009-2011), research scientist in the Institute of Process Engineering, Chinese Academy of Sciences (2011-2013),associate research scholar in Princeton University (2013-2014) and postdoctoral research associate in Texas A&M University (January 2015 – December 2015). His research expertise includes process modelling and global optimization related to process synthesis and design, process operations management: planning and scheduling, and robust optimization. He has published more than 30 peer-reviewed journal papers in high profile journals. He has delivered many presentations in some important international conferences such as AIChE Annual Meeting, PSE conference, ESCAPE.

He has been invited to deliver seminars from several famous universities, research institutions and companies in the world including Imperial College London, SABIC, The University of Manchester, Chinese Academy of Sciences, and Southeast University. He has been a chair of the session of planning and scheduling in Computing and Systems Technology Division (CAST) of AIChE Annual Meeting in 2016-2017. He is also serving as a referee for many international journals such as Computers and Chemical Engineering, AIChE Journal, Industrial and Engineering Chemistry Research. He has received several awards including outstanding reviewer of Computers and Chemical Engineering (2015), Third-prize paper award from Chinese Process Systems Engineering Conference (2014), and selected as one-hundred talent person program from the Institute of Process Engineering, Chinese Academy of Sciences (2011).

About the author

Dr Li Sun, Lecturer in Chemical Engineering in the School of Chemical Engineering and Analytical Science at the University of Manchester. She has been in the University of Manchester since 2009, having worked as a research associate prior to becoming a Lecturer in 2015. Before joining the University of Manchester, Li worked as an Associate Professor at Dalian University of Technology in China, having obtained her PhD in Chemical Engineering from there in 2004.

Her research areas lie in processes and utility systems integration and sustainable site system optimization under uncertainty. She has published 30 peer reviewed journal papers in high profile journals, and gave presentations and posters in more than 40 conferences. Li successfully completed thirties funds and industrial projects, and have won three research related prizes.

About the author

Dr Christodoulos A. Floudas was the late Director of the Texas A&M Energy Institute, and the Erle Nye ’59 Chair Professor for Engineering Excellence at the Artie McFerrin Department of Chemical Engineering at Texas A&M University (Passed away on August 14th, 2016). He is a world-renowned authority in mathematical modeling and optimization of complex systems.

His research interests lie at the interface of chemical engineering, applied mathematics, and operations research, with principal areas of focus including multi-scale systems engineering for energy and the environment, chemical process synthesis and design, process operations, discrete-continuous nonlinear optimization, local and global optimization, and computational chemistry and molecular biology. Professor Floudas is the author of two graduate textbooks, Nonlinear Mixed-Integer Optimization (Oxford University Press, 1995), and Deterministic Global Optimization (Kluwer Academic Publishers, 2000).

He has co-edited ten monographs/books, has over 300 refereed publications, delivered over 330 invited lectures, seminars, and named lectureships. He is the recipient of numerous awards and honors for teaching and research including the NSF Presidential Young Investigator Award, 1988; the Engineering Council Teaching Award, Princeton University, 1995; the Bodossaki Foundation Award in Applied Sciences, 1997; the Best Paper Award in Computers and Chemical Engineering, 1998; the Aspen Tech Excellence in Teaching Award, 1999; the 2001 AIChE Professional Progress Award for Outstanding Progress in Chemical Engineering; the 2006 AIChE Computing in Chemical Engineering Award; the 2007 Graduate Mentoring Award, Princeton University; Member of National Academy of Engineering, 2011; One thousand Global Experts, China 2012-2015; SIAM Fellow, 2013; TIAS Fellow and Eminent Scholar, 2013-14; AIChE Fellow, 2013; National Award and HELORS Gold Medal, 2013; Honorary Doctorate, Abo Akademi University, Finland, 2014; Thompson Reuters Highly Cited Researcher, 2014 (for 2002-2012, 11 years); Member of TAMEST (The Academy of Medicine, Engineering, and Sciences of Texas), 2015; Corresponding Member of the Academy of Athens, 2015.

Reference

Yujiao Zeng, Xin Xiao, Jie Li, Li Sun, Christodoulos A. Floudas, Hechang Li. A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant. Energy, volume 143 (2018) pages 881-899.

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