Pellets heat treatment optimal control systems – it's real economy for travelling grate pelletizing plant (VNIIMT's straight grate iron ore pelletizing technology) Task To create highly effective pellets heat treatment control system on . It is necessary in this case to receive on the same aggregate the additional productivity increase, to decrease energy consumption (fuel, electric energy) for conversion with the assigned pellets quality. Problem At present time the technological process is lead by qualified personal which carries out the roasting machine (travelling grate pelletizing plant) control quite competently. However, a man is physically unable to percept, interpret, analyze all the information about process coming to him (more then 1000 signals) and to determine the optimal values of control parameters on its base. That is why the mean reserves of the aggregate productivity increase, energy consumption for conversion (duel, electric energy) decrease. Idea to lay this difficult, tedious, requiring great attention work on travelling grate pelletizing plant control on optimal control system, which in real time scale, taking into account the technological situation on control object, will determine such control parameters values with which travelling grate pelletizing plant can achieve minimal specific fuel and electrical consumption, maximal aggregate productivity with the assigned pellets quality and technological limits on control parameters values. Result 1. Specific fuel consumption decrease – 10% (fig.1). 2. Specific electric energy consumption decrease – 6%. 3. Aggregate productivity increase – 3%. 4. Pellets quality improving. 5. Personnel work alleviation. 6. Ecology improving Figure 1. Reduction fuel specific consumption due to optimization OPTIMAL CONTROL SYSTEM ESSENCE Optimal control system basis contains: •Determined mathematical model • Model parameters identification algorithm • Optimization algorithms The optimal control system structure is represented on fig.2. The measured technological process parameters values are given to the forecasting determined mathematical model. The model parameters are constantly being corrected by the identification algorithm. Such correction is carried out by minimization of the indicators values deviations calculated according to the model of indicates values deviations calculated a according to the model and of conforming measured values in order to provide high accuracy of indicators forecasting and maximal adequacy of the model to the real process. The adequate to the process mathematical model calculates and gives to operator the process parameters values not available for direct calculation but determining the ready product quality and energy consumption for conversion (pellets durability, pellets and gas in layer temperature, roasting cart temperature, gases consumption through the layer and the layer porosity in technological zones and others). These are the so called "indirect measurements" of parameters which allow to operator to more efficiently control the technological process. With the help of optimization algorithm, turning to the mathematical model process parameters optimal values are calculated in accordance with the assigned optimization criterion. The optimal control parameters values either directly influence the process, optimizing plant operation indicators (operation mode online), or they are given as an "advice" to the indurating machine operator (operation mode offline) 1. Forecasting determined mathematical model of technological process allows to determined with high accuracy, fast and reliably the parameters values not available for direct measurement ("indirect measurements") to forecast the indicators values (for example, specific fuel consumption) under change of control parameters values. The term determined means that the model is the one built on the knowledge of physics of the process taking place on roasting conveyor machine (travelling grate pelletizing plant). It includes equations and models, adapted to the concrete type of row material and aggregate what provides the maximal adequacy of the model to the real process. 1. Heat exchange in filtrated pellets layer represented by the system of differential equations in partial derivatives,the ration of which are nonlinear functions.
2. Aerodynamics of the filtrated layer. 3. Fuel consumption forecasting model built on the basis of the aggregate heat and material balances equations, adapted to the concrete type of the fuel (solid, liquid, gas) and to the burner facility. 4. Electric energy consumption forecasting model built on the basis of the energy of moving gases. The model is adapted to the concrete type of forced draft facilities. 5. Equations of the roasted pellets quality (disability) forecasting, that are built on the basis of generalized variables methods (similarity theories) and adapted to the concrete type of raw materials. 2. Model parameters identification algorithms Allow to correct the model equations parameters values in real time scale with changing technological situation on the control object. By this the high accuracy of indicators and maximal adequacy of the model to the process is provided. In its turn this provides high efficiency of optimal control tasks solving. 3. Optimization algorithms They allow on the basis of forecasting determined mathematical model in real time scale and taking into account the present technological situation on the control object, to determine such control parameters value with which minimal specific fuel, electric energy consumption or maximal aggregate productivity with the assigned pellets quality and technological limits on control parameters values. optimization algorithms are adapted for solving the following optimization: 1. Productivity maximum G_{sp} >max 2. Specific heat (fuel) consumption minimum Q_{sp}=Q_{sp1}+…+Q_{spn} >min 3. Specific electric energy consumption minimum E_{sp}=E_{sp1}+…+E_{spn} >min with limits in form of equalities and inequalities on the speed of pellets heating and cooling, cart temperature, gases temperature in furnace, temperature in layer, quality (durability) of pellets and others. This allows to provide production high quality, low energy consumption and long life of equipment (roasting carts, forceddraft plants, refractory lining) work. Optimal Control System Practical Realization Optimal control system of pellets heat treatment is realized on the upper level of two level ACS TP (Kazakhstan SokolovSarbaisky mining company, roasting machine of 116 m2 travelling grate pelletizing plant). Automation upper level On the basis of the considered optimal control system and optimization tasks putting in contents of ACS TP of pellets heat treatment the following tasks of the control system upper level are realized: · optimal control of technological process on the criterion of specific fuel consumption minimum; · optimal control of technological process on the criterion of maximal productivity; · forecasting of quality (durability) of roasted pellets; "indirect measurements" of parameters (pellets temperature on the length and height of the treated layer, roasting cast temperature, consumption and gases filtration speeds through the layer in technological zones and many others – more than 3000 parameters in all). Realization of these tasks allowed to receive on SokolovSarbaisky roasting plant: 1. Fuel specific consumption decrease by 10% 2. Electric energy specific consumption by 6 % 3. Aggregate productivity increase by 3 % For these tasks realization the requirements of the basic automation are formulated and realized what considerably widened the basic automation functions and increased the efficiency. Automation basic level On the automation basic level the optimal number of sensors and executive mechanisms is installed. This allowed in realization of the upper level tasks to take into account all the parameters affecting the pellets quality, to economize energy resources having high quality of production. Optional scheme of regulation outlines organization allowed, on the basic level already, to optimize the parameters regulation process, to provide controllability of pellets heat treatment technological process and economize energy resources. In the case the following tasks of travelling grate pelletizing plant parameters regulation are solved: · Raw pellets layer aerodynamic resistance stabilization; · Temperature regulation in gasair chambers; · Temperature stabilization on furnace space; · Stabilization of ratio gasgas and gasair on furnace space; · Gasair mode stabilization on the travelling grate pelletizing plant length; · Protection of roasting aggregate from overheating; · Unloaded pellets temperature leveling; · Control and regulation of separate technological parameters. Solving of each of the enumerated tasks, as a rule, is fulfilled by realizing several regulation outlines. In this case the optimal tasks to 37 regulation outlines of 53 ones are given from the automation upper level (mode online). Result:  more product  expenseses less  all under control 1. Fuel specific consumption decrease by 10% 2. Electric energy specific consumption decrease by 6% 3. Aggregate productivity increase by 3% 4. Pellets quality improving 5. Personnel work alleviation 6. Ecology improving THE PLUS  You get additional spare if you will conduct the lowcost modernization of design of unit: System of optimal control itself will prompt you, as better conduct this modernization for reception most spare Do you want to begin to spare already today, as this do others? Contant us: Research institute of metallurgical heat engineering – OJSC "VNIIMT" Studencheskaya St. 16, Yekaterinburg, 620137, Russia. Innovation and Strategic Development Director Alexey Butkarev Tel. +7 343 3837581 Email: butkarev@yandex.ru Skype: butkarevalexey See more
