Optimal scheduling of a by-product gas supply system in the iron- and steel-making process under uncertainties

Abstract This paper addresses the real-time by-product gas scheduling in an integrated iron- and steel-making industry with uncertainty in by-product gas flows from a rolling horizon algorithm. Adaptive time-series models determined from real data performs forecast for each producer and consumer of by-product gases in main units of the steel-making plant. The individual consumptions of the blast furnace and coke oven gases are modelled using the seasonal Holt-Winters method with smoothing constants estimated via genetic algorithm, whereas the individual productions of the blast furnace and coke oven are identified from autoregressive and integrated moving-average. LDG gas production is forecasted using a heuristic method that leverages the operational information. The model’s parameters are updated periodically due to the nonlinearities present in the time series. After the forecasting phase, the algorithm performs short-term decisions using a MILP optimization model, that minimizes the imbalance between the random dynamics of by-product fuel generation and consumption and maximizes the energy efficiency. Through computational simulations, we show that the operational stability of gas holders and the electrical energy production increase, whereas the waste of gas in flare stack decreases, when the control horizon of the rolling horizon algorithm is reduced.

[1]  Murat Kulahci,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[2]  Davide Brunelli,et al.  Forecasting data centers power consumption with the Holt-Winters method , 2015, 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings.

[3]  Chonghun Han,et al.  A Novel MILP Model for Plantwide Multiperiod Optimization of Byproduct Gas Supply System in the Iron- and Steel-Making Process , 2003 .

[4]  Moritz Diehl,et al.  A Lyapunov Function for Economic Optimizing Model Predictive Control , 2011, IEEE Transactions on Automatic Control.

[5]  Helen Durand,et al.  A tutorial review of economic model predictive control methods , 2014 .

[6]  Efstratios N. Pistikopoulos,et al.  A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids , 2015 .

[7]  K. Jeong Hwan,et al.  The Development of the Real Time Optimal Byproduct Gas Supply System , 2002 .

[8]  Wenqiang Sun,et al.  Optimization and scheduling of byproduct gas system in steel plant , 2015 .

[9]  Witold Pedrycz,et al.  Hybrid Neural Prediction and Optimized Adjustment for Coke Oven Gas System in Steel Industry , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Edoardo Amaldi,et al.  A rolling-horizon optimization algorithm for the long term operational scheduling of cogeneration systems , 2017, Energy.

[11]  Chonghun Han,et al.  Gasholder level control based on time-series analysis and process heuristics , 2011 .

[12]  Qi Shi,et al.  A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process , 2015 .

[13]  Witold Pedrycz,et al.  Effective Noise Estimation-Based Online Prediction for Byproduct Gas System in Steel Industry , 2012, IEEE Transactions on Industrial Informatics.

[14]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[15]  Qi Zhang,et al.  Multi-Period Optimal Distribution Model of Energy Medium and Its Application , 2011 .

[16]  Ying Liu,et al.  A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry , 2014, IEEE Transactions on Automation Science and Engineering.

[17]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[18]  Gang Li,et al.  An MILP model for optimization of byproduct gases in the integrated iron and steel plant , 2010 .

[19]  Christodoulos A. Floudas,et al.  A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant , 2018 .

[20]  Renhao Jin,et al.  Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China , 2015 .

[21]  Lazaros G. Papageorgiou,et al.  A rolling horizon approach for optimal management of microgrids under stochastic uncertainty , 2017 .

[22]  Pedro M. Castro,et al.  Scope for industrial applications of production scheduling models and solution methods , 2014, Comput. Chem. Eng..

[23]  Gopal P. Sinha,et al.  Strategic and Operational Management with Optimization at Tata Steel , 1995 .

[24]  Yoshikazu Nishikawa,et al.  An optimal gas supply for a power plant using a mixed integer programming model , 1991, Autom..

[25]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[26]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[27]  Wei Wang,et al.  An optimal method for prediction and adjustment on byproduct gas holder in steel industry , 2011, Expert Syst. Appl..

[28]  Hai-ning Kong,et al.  A green mixed integer linear programming model for optimization of byproduct gases in iron and steel industry , 2015 .

[29]  João G. Coelho Pena,et al.  An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process , 2016 .

[30]  Jun Zhao,et al.  A MKL based on-line prediction for gasholder level in steel industry , 2012 .

[31]  Witold Pedrycz,et al.  Data-based predictive optimization for by product gas system in steel industry , 2017, 2017 13th IEEE Conference on Automation Science and Engineering (CASE).