Energy Performance Balancing And Optimal Scheduling Strategies And Applications For Steel And Iron Enterprise

Strengthening energy management is an important means to achieve the circular economy. The energy consumption cost of iron and steel enterprise takes up approximately 15%-20%of the total energy consumption cost of industrial sectors. The high energy consumption results in environmental pollution and negative effects on economic benefit, which have been a difficult problem for the iron and steel enterprises. The mains factors that result in the high energy consumption include the confused energy management, improper energy arrangement, inefficient energy scheduling and so forth. This paper aims to solve these problems based on the energy center construction project of a large-scale steel joint enterprise. The energy system, energy product-consume prediction, energy performance balancing, energy scheduling models and its applications are analyzed and studied. The main original research achievements are as follows:(1) Indicate the main problems existing in energy management systemThe distribution and management process of all kinds of energy such as gas, power, water, vapor, oxygen, argon and nitrogen consumed in the main production processes of sintering, coke oven coking, blast furnace ironmaking, converter steelmaking, continuous casting and rolling in a typical steel joint enterprise have been analyzed. It points out that energy product-consume prediction, energy performance balancing and energy scheduling remain to be improved in the current energy management.(2) Energy product-consume characteristic analysis and prediction models based on different characteristicConcerning the two main energy resources, gas and power, and combining the process characteristics analysis with grey relational analysis, the energy consumption characteristics of the users in the main production processes are analyzed. Then, the energy product-consume prediction models are developed according to the product-consume characteristics. For the users whose production and consumption are within a certain scope, the prediction model based on the production plan is built. It forecasts based on the planned production of non-energy. For the users whose production and consumption is of randomness, grey characteristics and has nonlinear relation with its influencing factors, the prediction model based on the RBF neural network is constructed. It adopts the grey accumulated data as the trained data, the time series as the input and the prediction error as the feedback to modify the neural network structure. For the users whose production and consumption are of obvious or linear relation with its influencing factors, the prediction model based on the multi-layer hierarchical regression is built. It combines the linear regression with multi-layer hierarchical, and takes the predicted object as the random dynamic time-varying system. For the users whose production and consumption are of linear change in a short time, but of continuity and cyclical change in a long time, the forecasting model based on the auto-regressive and moving average are developed. It predicts the changes in a latter period with the linear combination of energy product-consume in a former period.The results of simulation based on the practical industrial data show that the four models are of good pertinence, flexibility and accuracy.(3) Energy data reconciliation and performance balancing strategyBased on the prediction models, an energy data reconciliation method is proposed. It adopts the predicted value as the reference value, the balance equation and reconciliation range as the constraint condition, and sets the objective function as minimizing the sum-of-square of differential between the rectified and predicted value to construct the optimization model. The rectified value of energy data is obtained according to the optimal solution of the optimization model. Meanwhile, the method based on the polluted normal distribution gross error diagnosis is adopted to diagnose the error in the energy metrological data. The rectified value of the erotic data is re-solved. A gas automatic performance balancing strategy has been designed based on the data reconciliation method. The functional departments set the compensation parameters, gas users set the instrument stop time, and administrators set the balance operation parameters. Rectified value of metrological data replaces the original measured value to achieve automatic performance balancing of gas system. The results of simulation based on the practical industrial data show that the data reconciliation model and balance strategy are applicable and could improve the scientific and automation level of gas balance.(4) Two energy optimal scheduling modelsBy analyzing the energy network structure, the expression of energy optimal scheduling based on the mathematical programming is deduced. Then, the gas scheduling models based on unit classification and product-consume forecasting are proposed.1) The gas scheduling model based on unit classification classifies the gas users into different units according to their roles and functions in the production process. The optimal scheduling is achieved with unified scheduling goal and different constraint conditions. The model focuses on the entire process from the macroscopic angle and could be used by all gas users.2) The scheduling model based on product-consume prediction adopts the predicted value of the gas product-consume prediction model, fixes the value with the overall energy optimal scheduling model, and sets the fixed value as optimal scheduling value. This model is of higher accuracy by taking the key factors into consideration. However, it can only be used by the users with forecasting model and is influenced by the predicting accuracy. The results of simulation based on the practical industrial data show that the two models could significantly reduce the amount of radiation gas.(5) Implementation and application of energy product-consume predicting and optimal scheduling model in energy management systemBased on the energy product-consume predicting model and optimal scheduling model, the gas auto performance balancing and optimal scheduling system using Windows XP platform, Visual studio integrated development environment and Oracle database is built. It is applied in a iron and steel enterprise as a part of the energy management system, and could achieve auto performance balance of the gas of the whole factory by taking a class as the minimal cycle within five minutes and could generate class scheduling plan automatically within two minutes. It effectively improves the efficiency of the performance balance and the gas utilization, and remarkably increases the economic benefit.