Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study

Increasingly demanding environmental regulations are forcing companies to reduce their impacts caused by their activity while defending the economic viability of their manufacturing processes, especially energy and carbon-intensive ones. Therefore, these challenges must be addressed by posing optimization problems that involve several objectives simultaneously, corresponding to different conditions, and often conflicting between. In this study, the residual gases of an integral steel factory were evaluated and modeled with the goal of developing an optimization problem considering two opposing objectives: CO2 emissions and profit. The problem was first approached in a mono-objective manner, optimizing profit through Mixed Integer Linear Programming (MILP), and then was extended to a bi-objective problem solved by means of the ε-constraint method, to find the Pareto front relating profit and CO2 emissions. The results show that multiobjective optimization is a very valuable resource for plant managers’ decision-making processes. The model makes it possible to identify inflection points from which the level of emissions would increase disproportionately. It gives priority to the consumption of less polluting fuels. The model also makes it possible to make the most of temporary buffers such as the gas holders, adapting to the hourly price of the electricity market. By applying this method, CO2 emissions decrease by more than 3%, and profit amounts up to 14.8% compared to a regular case under normal operating conditions. The sensitivity analysis of the CO2 price and CO2 constraints is also performed.

A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study

Off-gas is one of the by-products of the steelmaking process. Its potential energy can be transformed into heat and electricity by means of cogeneration. A case study using a coke oven and Linz–Donawitz converter gas is presented. This work addresses the gas allocation problem for a cogeneration system producing steam and electricity. In the studied facility, located in northern Spain, the annual production of the plant requires 95,000 MWh of electrical energy and 525,000 MWh of thermal energy. The installed electrical and thermal power is 20.4 MW and 81 MW, respectively. A mixed integer linear programming model is built to optimize gas allocation, thus maximizing its benefits. This model is applied to a 24-h scenario with real data from the plant, where gas allocation decision-making was performed by the plant operators. Application of the model generated profit in a scenario where there were losses, increasing benefits by 16.9%. A sensitivity analysis is also performed. The proposed model is useful not only from the perspective of daily plant operation but also as a tool to simulate different design scenarios, such as the capacity of gasholders.

Optimal Design Model of the Energy Systems in Iron and Steel Enterprises

In steel enterprises, the design of energy systems mainly depends on experience and lacks systematic optimization methods. Therefore, it is of great significance to propose an overall optimization design method for the energy system of steel enterprises. In this paper, a linear model is proposed to select the energy conversion equipment types and distribute energy sources with the aim of maximizing the economic benefits of an energy system. In the model, the choice of energy conversion technology and the distribution of energy source are considered comprehensively. For the S steel plant, the case analysis results show that under the condition of keeping the original energy conversion equipment unchanged and optimizing the distribution of energy source, the economic benefits of the energy system can be increased by 15.77 CNY/t steel, and under the conditions of optimizing the distribution schemes of the energy conversion equipment types and energy sources, the economic benefits of the energy system can be increased by 180.26 CNY/t steel. In addition, the effects of different energy conversion equipment configurations, different energy sources prices and different pollution cost standards on the steel plant energy economic benefits were analyzed.

Optimization Simulation, Using Steel Plant Off-Gas for Power Generation: A Life-Cycle Cost Analysis Approach

This research aims to contribute to the international desires of greenhouse gas mitigation through a study on the optimization of 100 MW off-gas power plants’ subcritical steam cycle, operating in conjunction with steel plants. The performance and fiscal efficiency of the off-gas power plants are based on data collected from Krakatau Steel plants in Indonesia, simulated and analyzed through thermodynamic simulation model (PEPSE). The three scenarios simulated for comparison are: (1) 100 MW off-gas power plant as-is operation, (2) 100 MW off-gas power plant optimized, and (3) 100 MW cogeneration off-gas power plant. The optimization of the off-gas power plant and use of steam extraction was found to have a 45 M and 26 M USD savings in comparison to the base power plant, assuming a 5.5% discount rate and 400 Million USD capital expenditure. The theoretical findings are of use to academia and industry in providing proof of potential cost-savings of integrating off-gas into existing power generation operations.

Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry

An energy system is the one of most important parts of the steel industry, and its reasonable operation exhibits a critical impact on manufacturing cost, energy security, and natural environment. With respect to the operation optimization problem for coke oven gas, a two-phase data-driven based forecasting and optimized adjusting method is proposed, where a Gaussian process-based echo states network is established to predict the gas real-time flow and the gasholder level in the prediction phase. Then, using the predicted gas flow and gasholder level, we develop a certain heuristic to quantify the user's optimal gas adjustment. The proposed operation measure has been verified to be effective by experimenting with the real-world on-line energy data sets coming from Shanghai Baosteel Corporation, Ltd., China. At present, the scheduling software developed with the proposed model and ensuing algorithms have been applied to the production practice of Baosteel. The application effects indicate that the software system can largely improve the real-time prediction accuracy of the gas units and provide with the optimized gas balance direction for the energy optimization.