Byproduct Gas Scheduling Research Articles

Dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works

The by-product gases, which are generated in ironmaking, coking and steelmaking processes, can be used as fuel for the metallurgical processes and on-site power plants. However, if the supply and demand of by-product gases are imbalanced, gas flaring may occur, leading to energy wastage and environmental pollution. Therefore, optimal scheduling of by-product gases is important in iron and steel works. A BP_LSSVM model, which combines back-propagation (BP) neural network and least squares support vector machine (LSSVM), and an improved mixed integer linear programming model were proposed to forecast the surplus gases and allocate them optimally. To maximize energy utilization, the stability of gas holders and boilers was considered and a concise heuristic procedure was proposed to assign penalties for boilers and gas holders. Moreover, the optimal level of gas holder was studied to enhance the stability of the gas system. Compared to the manual operation, the optimal results showed that the electricity generated by the power plant increased by 2.93% in normal condition and by 22.2% in overhaul condition. The proposed model minimizes the total cost by optimizing the boiler load with less adjustment frequency and the stability of gas holders and can be used as a guidance in dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works.

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

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.

Fuzzy Byproduct Gas Scheduling in the Steel Plant Considering Uncertainty and Risk Analysis

In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling.

A review on the optimal scheduling of byproduct gases in steel making industry

The scheduling of byproduct gases is an important topic in the optimal energy management of steel enterprises. For the past two decades, intensive studies have been implemented on this topic. Because the byproduct gases account for approximately 30% of total energy consumption in steel enterprises, the efficient and effective usage of them is beneficial for both the energy efficiency improvement and the production cost reduction in steel industry. However, due to the complexity of steel-making process, it is difficult to build a comprehensive and reasonable scheduling model to optimise the byproduct gas usage. In this paper, current methods and progresses in byproduct gas scheduling are reviewed and some interesting findings are discussed.

Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing

Most integrated iron and steel corporations have built on–site power plants (OSPPs) to reduce their purchased electricity and thus to decrease the overall electricity cost. Due to their large quantities and easy access, the byproduct gases generated in the steel production process are the main fuels used for the OSPPs. The introduction of time–of–use (TOU) electricity pricing in the steel industry has made it possible to decrease electricity costs through an optimal collaboration between the energy storage equipment (gasholders) and OSPPs. In this paper, a byproduct gas scheduling model based on mixed–integer linear programing (MILP) considering the TOU electricity pricing is proposed. In this model, Pareto optimality and fuzzy sets were used to find the best compromise solution for two conflicting objectives: achieving the gasholder stability and reducing the electricity purchasing cost. In addition, the influence of the operation load on the boiler efficiency was considered to improve the model accuracy. The results show that the optimisation can achieve better peak–valley shifting of the electricity generation and decrease the electricity purchasing cost by 29.7% with improved gasholder stability. Optimisation increased the overall boiler efficiency by 3.3%, indicating that the byproduct gases are effectively and efficiently used. The sensitivity analysis results indicate that the peak–valley shifting of the electricity generation improves with increasing peak–valley price rate (PVR) at the expense of decreasing the overall gasholder stability.

A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry

It is very crucial for the byproduct gas system in steel industry to perform an accurate and timely scheduling, which enables to reasonably utilize the energy resources and effectively reduce the production cost of enterprises. In this study, a novel data-driven-based dynamic scheduling thought is proposed for the real-time gas scheduling, in which a probability relationship described by a Bayesian network is modeled to determine the adjustable gas users that impact on the gas tanks level, and to give their scheduling amounts online by maximizing the posterior probability of the users' operational statuses. For the practical applicability, the obtained scheduling solution can be further verified by a recurrent neural network reported in literature. To indicate the effectiveness of the proposed data-driven scheduling method, the real gas flow data coming from a steel plant in China are employed, and the experimental results indicate that the proposed method can provide real-time and scientific gas scheduling solution for the energy system of steel industry.

A Decomposition Algorithm for Byproduct Gases Scheduling in Iron and Steel Enterprises

Concentrating on byproduct gas system with multiple pipeline networks, this paper established a mathematical model for byproduct gas scheduling (BGS) problem in iron and steel enterprises with consideration of supply priorities of consumption units. A decomposition algorithm is employed to transform the original model into an integer programming and a linear programming. The genetic algorithm is introduced as the solution mainframe to solve the integer programming problem and the linear programming problem iteratively. The result of practical-data-based experiment indicates that the proposed model and algorithm are feasible and effective.

An optimal method for prediction and adjustment on byproduct gas holder in steel industry

To maintain the balance of byproduct gas holder is an important task in optimal scheduling of byproduct energy in steel industry. However, this is often influenced by many factors and is difficult to obtain a precise mechanism model for analysis. In this paper, an optimal method for prediction and adjustment on byproduct gas holder is proposed. Considering the different operation styles of gasholders, both single and multiple gasholders level prediction models are established by machine learning methodology. And, a hybrid parameter optimization algorithm is developed to optimize the model for high prediction accuracy. Then, based on the predicted gasholder level, the optimal adjustment amount is calculated by a novel reasoning method to sustain the gasholder within safety zone. This method has been verified in the Energy Center of Baosteel, China. The results demonstrate that the proposed approach can precisely predict and adjust gasholders and provide a remarkable guidance for reasonable scheduling of byproduct gases.

MILP Model for Plant-Wide Optimal By-Product Gas Scheduling in Iron and Steel Industry

The scheduling of by-product gases in by-product gas system, cogeneration system and production system is studied. All gas-consuming users are divided into three kinds according to their different characters and then a distribution model is built. A dynamic mixed integer linear programming (MILP) model for multiperiod optimization of by-product gas is performed to optimize by-product gas distribution to achieve total cost reduction. Case study shows that 6.2% operation cost is reduced by using the proposed model.

Costings and operational problems of sewage sludge processing

Most integrated iron and steel corporations have built on–site power plants (OSPPs) to reduce their purchased electricity and thus to decrease the overall electricity cost. Due to their large quantities and easy access, the byproduct gases generated in the steel production process are the main fuels used for the OSPPs. The introduction of time–of–use (TOU) electricity pricing in the steel industry has made it possible to decrease electricity costs through an optimal collaboration between the energy storage equipment (gasholders) and OSPPs. In this paper, a byproduct gas scheduling model based on mixed–integer linear programing (MILP) considering the TOU electricity pricing is proposed. In this model, Pareto optimality and fuzzy sets were used to find the best compromise solution for two conflicting objectives: achieving the gasholder stability and reducing the electricity purchasing cost. In addition, the influence of the operation load on the boiler efficiency was considered to improve the model accuracy. The results show that the optimisation can achieve better peak–valley shifting of the electricity generation and decrease the electricity purchasing cost by 29.7% with improved gasholder stability. Optimisation increased the overall boiler efficiency by 3.3%, indicating that the byproduct gases are effectively and efficiently used. The sensitivity analysis results indicate that the peak–valley shifting of the electricity generation improves with increasing peak–valley price rate (PVR) at the expense of decreasing the overall gasholder stability.