Research On The Energy Allocation Plan Of Iron And Steel Enterprises

Iron and steel industry is energy-intensive that it takes up a heavy proportion in the general energy consumption of national economy. In2010, the total energy consumption of Chinese iron and steel industry holds15%of total national energy consumption and23%of total national industrial consumption. The energy cost occupies30%of the production cost of the enterprises, showing greater cost pressure more and more. Thus, energy-saving and cost-reducing are not only of great significance to building resources-saving and environment-friendly society, but also the important methods for enterprises to reduce costs and enhance the market competitiveness of their products. On this background, the paper, taking energy as its breakthrough point, puts forward the problem of the energy allocation plan inside an iron and steel enterprise.The common methods for iron and steel enterprise to save energy are classified into three types:the first, improving techniques; the second, averaging up automation operation and the third, bettering management techniques. The first and the second belong to the scope of hard technology and develop as science and technology advance, while the third method is a kind of soft technology. It is the summary and improvement of previous management experience and technology. It indicates an advanced management philosophy and method. It, taking directly the medium of energy as the object, optimizes the distribution and realizes the optimal allocation.The energy centre is the nervous centre of the energy management of iron and steel industry. Its management activities cover all production procedures and the stages of energy activities of the industry, including long-term energy plan of over one year, monthly and weekly energy consumption plan, and daily dynamic energy allocation plan, with the purpose to guarantee the energy demand of normal production. The energy centre does not manage in individual single energy, but views all kinds of energy involved in iron and steel production as an interrelated unity, makes overall plans and schedules, and realizes maximum production revenue at minimum energy cost.Under the unified management of the energy centre, the gas system, the oxygen system, the steam system and the electrical power system not only need the respective balanced allocation, but also carry the strong correlation of complementation and interdependence, which is a very complicated problem in theory and practice.This paper takes energy as the research background, analyzes the energy operation features of an iron and steel enterprise, uses as reference the related theories of logistics and network flow, considers comprehensively the features of the input and output of the energy medium, in the whole production flow of iron and steel. The purpose is to increase the utilization of the energy, reduce the non-effective diffusion and minimizes the energy cost. Thus, the paper establishes the model of the dynamic allocation plan and optimal scheduling of the energy, solves with CPLEX the real production data and obtains satisfactory result. This paper covers mainly:1) Whole-flow-oriented dynamic allocation plan of energyThe production flow of iron and steel enterprise is long and contains complicated procedures. The energy involved cover over20kinds. On the input end and the output of each procedure, there exists a certain input-output relation of the energy. As the product output of each procedure changes, the input and output of the energy will change at certain rate, thus providing the reference for the optimal allocation of energy. Besides, the energy flow is changing. As time advances, the result produced by the allocation plan during the previous period will influence the energy allocation of the procedures during the next period and other energy could be used to convert and complement the insufficiency of one certain energy medium, owing to the replacing and converting relationship of energy. Aiming at the above, the paper, on the precondition of the satisfied input-output capability and mixing ratio of energy, the safe pipe network pressure of energy and the restriction of energy storage, establishes the mathematical programming model of whole-flow-oriented dynamic allocation plan of energy, realizes the optimal allocation of energy and proves the effectiveness through experiments.2) Allocation plan of gas mixing ratio decision of different gas sourcesBlast furnace gas, coke-oven gas and converter gas are the by-products and important fuel of the enterprise, which could be used to heat the mill furnace, produce steam and generate electricity. Some important procedures of iron and steel production need these gases. Different procedure demands different regulation on the calorific value of burning and flow of the heating gas. Owing to the restriction of one single gas, many enterprises use the mixture of the above three gases and make several plan for each gas user, so as to activate one of the other plans when the gas is insufficient under one plan and continue the production. Aiming at the above feature, this paper takes the minimized sum of gas diffusion cost and gas insufficiency losses as target, establishes0-1mixed integer mathematical programming model of gas mixing ratio decision of different gas sources and proves the effectiveness of the model.3) Steam allocation plan considering production and electricity generationDuring the whole production flow of iron and steel enterprises, a large amount of heat will be released, which mainly exists in the state of steam. Except for electricity generation, the steam is seldom used, thus producing larger thermal losses. The thermal losses of steam include two parts:the losses of steam diffusion and losses of recycling in the state of condensed water after high-quality steam is used insufficiently. Therefore, when steam is being studied, not only the diffusion should be considered, but also the full utilization of the steam enthalpy should be considered preferentially. Aiming at the above, on the precondition of the guaranteed balance of the pipe network and demands of production users, the paper takes as target the maximum electricity generation under the full utilization of steam enthalpy, sets up the mathematical programming model of the dynamic allocation of steam for production and electricity generation to guarantee to full utilization of waste heat and proves the effectiveness of the model.4) Oxygen allocation plan and optimal integrated scheduling of oxygenerator unitsThe allocation plan of oxygen at oxygen-consuming procedures is the typical problem of the combination of oxygenerator units. One oxygenerator unit consists of the oxygenerator, the pressure device, the liquefying evaporator and the liquid oxygen tank. Through selectively starting on and off several oxygen generating units, adjusting the amount of oxygen generation, pressure, liquefying and gasifying of different units, we can reduce oxygen diffusion. After the analysis on generation features of oxygen from generation to storage, taking as objective function the minimization of total cost of diffusion cost, pressure feeding cost, liquefying cost, evaporating cost and startup cost, the paper sets up the mixed integer mathematical programming model of oxygen allocation plan and optimal scheduling of oxygenerator units and proves the effectiveness of the model.5) Prediction of the energy consumption and generation of production processTo guarantee the continuous, safe, economical and high-efficient energy supply during production, the prediction of the generation, consumption, recycling and conversion of process energy is the premise of energy allocation plans and provides basic data for the plans. Blast furnace iron-making is the most complicated and the most typical procedure of energy generation and consumption in iron and steel enterprises. It is of directive significance to the energy prediction of other procedures. On the bases of the above, the paper designs a Support Vector Machine (SVM)-based energy consumption prediction method of iron-making procedures, applies particle swarm optimization to optimize the parameters of SVM, and sets up the correct prediction model to predict more effectively energy consumption tendency and monitor energy storage.