Abstract
Development of automation control system of synthesis of ammonia
Contents:
3. Relationship with academic programs, plans, themes
4. Prospective scientific novelty
6. Review of existing researches
7. Description of the process of temperature regulation in the column for ammonia synthesis
8. Brief description of the results
Introduction
Production of ammonia is the source of a product that finds application in many vital areas. Synthetic ammonia is the raw material for producing nitric acid, ammonium nitrate, urea and other chemicals containing nitrogen, and used in medicine, refrigeration technology, in agriculture as fertilizer.
The consumption of natural gas is one of the most important factors determining the profitability of production of ammonia. On the production of 1 ton of ammonia domestic units consume 1115-1380 m3 of natural gas. Often, high consumption of natural gas due to the fact that most domestic units are outdated and significantly lower than used in the advanced countries in energy and material and environmental requirements. But in recent years, most enterprises are carried out work on the reconstruction and modernization of production, resulting in the consumption of natural gas and electricity is reduced.
1. Aims and objectives
The objectives of this work is to simplify the regulatory process of heating and cooling in the column for ammonia synthesis, a more simple and understandable model of governance, reducing consumption of raw materials needed to produce one product. To achieve these goals is necessary to solve the following tasks:
- explore the working principles of computer systems of chemical plant;
- review the existing methods of thermal management in a column for ammonia synthesis;
- construct a fuzzy model of thermal management;
- develop tools to implement the fuzzy control model.
2. Theme urgency
Application of fuzzy logic in industrial processes - is the branch of knowledge, which has been recently quite active in developing. The fuzzy logic operates with fuzzy linguistic expressions instead of a large quantity of mathematical formulas and numbers that considerably refines understanding of technological processes, and also reduces complexity in control of the big industrial targets, after all methods which are put in control of ammonia synthesis, are constructed on difficult mathematical calculations, therefore they are frequently difficult for understanding of the human being. Fuzzy logic application in control of a temperature mode in a synthesis column should simplify regulation of processes of heating up and cooling, to make a control model more simple and clear, and also to lower the expenditure of raw materials demanded on production.
3. Relationship with academic programs, plans, themes
This work was carried out during 2010-2011 in accordance with the scientific direction of the Department of Automated Control Systems of Donetsk National Technical University.
4. Prospective scientific novelty
The scientific novelty of this work is to develop a fuzzy system for automated thermal management in a column for ammonia synthesis.
5. Expected practical results
The expert system based on fuzzy rules for regulation of a temperature mode in a column of synthesis of ammonia should be result of performance master's work.
6. Review of existing researches
Currently, there are many companies that deal with process automation, in particular, the synthesis of ammonia. Among the most famous include such foreign companies as Siemens, Emerson, Honeywell.
I would like to highlight the following system of automatic control process for ammonia synthesis:
- DeltaV system of Emerson;
- Experion PKS system of Honeywell;
- SIMATIC system of Siemens.
It should be noted that these systems (and many other modern systems) are robust, reliable, flexible and scalable, which reduces both the initial cost of the system and the cost of its maintenance and upgrades. These systems provide great opportunities for the safe management and optimization of the synthesis of ammonia.
The disadvantages of these systems is their use of standard PID control. PID controllers operate on the basis laid down in these formulas and settings, they are not capable of learning, so they are less and spend perturbations in the system than, for example, the fuzzy controller.
7. Description of the process of temperature regulation in the column for ammonia synthesis
Ammonia is obtained as a result of chemical reactions of synthesis gas in the column on the stage of synthesis. Figure 1 shows four-shelf column with axial orifices. The main stream of gas enters the column below, passes through the annular gap between the casing string 15 and casing catalyst boxes 3 and enters the shell side of heat exchanger 6. Here, the synthesis gas is heated with gas converted to 420 - 440 ° C and held successively the four layers of catalyst 8, 10, 12, 14, between which is served cold bypass gas.
After the fourth layer of the catalyst gas mixture at 500-515 ° C rises along the central pipe 2, passes through the heat exchanger tubes 6, while being cooled to 320-350 ° C, and exits from the column [1].
Synthesis of ammonia was carried out at very high pressures (30-33 MPa) to shift the balance toward the formation of ammonia. The temperature in this case should not be less than 460 ° C (at lower temperatures, the process ceases to be stable) and not more than 530 ° C (to avoid overheating of the catalyst).
Figure1. Four-shelf column for ammonia synthesis capacity of 1360 t / d 1 - hatch for unloading catalyst, 2 - central tube, 3 - body catalyst boxes, 4 - thermocouple case 5 - the door: 6 - heat exchanger, 7, 9, 11, 13 - input bypass gas, 8, 10, 12, 14 - catalyst layers, 15 - body of the column.
It is necessary to carry out the process at high pressure and an optimal catalyst temperature regime, at high space velocities and possibly more than a pure gas to achieve higher productivity of the synthesis process.
To maintain a normal temperature (460 ° C - 530 ° C) in a column of synthesis the following ways are used: changing the intensity of circulation of gas or changing the ratio of gas flows to the column.
Changing the intensity of gas circulation is expedient to use as long as there is no set most profitable unit load on gas. Later load change only when abrupt disturbances of technological regime.
The constant intake of temperature regulation of the synthesis process is to change the relations of the gas flows to the column through the main valve and the cold bypass (sometimes two-pass, and in the columns of shelving attachment - even four). With increasing temperature, observed previously only at the entrance of the gas, opens cold bypass valve until the temperature reaches a preset rate. If, however, with the full opening of the valve temperature continues to increase, to keep it in the proper limits of the main valve cover, which increases the flow of gas coming through the cold bypass.
When the temperature decreases doing the opposite. First fully open the main valve, then gradually cover the cold bypass valves. If these measures have no effect, it is necessary to reduce the amount of gas fed into the column [2].
Operator-technologist produces direct control of the process of temperature regulation in the column for ammonia synthesis, ie, in fact, the regulation is done manually. To automate this process, we must use a system that is able to replace the specialist-expert (in our case, the operator-technologist), ie could make its own decisions based on the received data.
The purpose of this study is to examine the principles of temperature control in an ammonia synthesis column, select methods of construction of the regulatory system and the implementation of the system.
8. Brief description of the results
As mentioned earlier, the regulatory process in many ways is scrolled on the actions of the operator-technologist, his skills and experience. Therefore, fuzzy logic has been chosen to solve the problem of implementing automatic control of the temperature regime in the column for ammonia synthesis, because it is based on production rules, which, in turn, are projected on the basis of expert assessments. Production rules of fuzzy logic are similar in structure to the style of human thinking, which greatly simplifies management of complex technological objects.
Fuzzy inference system designed to transform the input variables in the process output variables using fuzzy production rules. To this end, fuzzy inference systems must contain the fuzzy rule base products and implement fuzzy conclusions based on assumptions or conditions provided in the form of fuzzy linguistic propositions.
Thus, the main stages of fuzzy inference are (Fig. 2):
- formation of the rules base of fuzzy inference systems;
- fuzzification of input variables;
- aggregation of sub-conditions in the fuzzy rules;
- activation or composition of sub-conclusions in fuzzy rules;
- accumulation of conclusions of fuzzy production rules [4].
Fig. 2 Main stages of the fuzzy output (number of frames: 9, number of repeats: 6, duration of frames: 80ms, size: 404x582, 58.2 kb)
Processes of heating and cooling in the column are inertia, so constructing an algorithm for thermal management took into account not only temperature but also its rate of change, which are the input variables of the control algorithm.
We must create a fuzzy rules based on which the temperature is automatically maintained in the optimum range for the reaction. Temperature control will be produced by changing the ratio of gas flows for each of the four pillars of catalyst shelves through the cold bypass.
Input parameters for the control system will be the temperature on the shelf column (X1), the rate of change of the temperature (X2). Output variable is the angle of rotation of cold bypass valve (Y). In this case, the temperature measured in degrees Celsius, the rate of change of temperature - in degrees Celsius per second, the angle valve - in angular degrees. It should be noted that the rotation of the valve to the right means positive direction of the valve, turn left - negative.
Values of temperature (X1) were delivered in compliance with the following linguistic terms:
NB – negative big;
NS – negative small;
Z – close to the norm;
PS – positive small;
PB – positive big.
The speed of change of temperature (X2) were delivered in compliance with the following linguistic terms:
NB – negative big;
NS – negative small;
Z – close to zero;
PS – positive small;
PB – positive big.
Output variable of the algorithm - the rotation angle of the valve (Y) correspond to the following linguistic terms:
PB – positive big;
PM – positive medium;
PS – positive small;
Z – close to zero;
NS – negative small;
NM – negative medium;
NB – negative big.
Were established regulatory rules (note that this set of rules is not final and is being finalized):
R1: IF X1 = PB AND X2 = PB, THEN Y = NB;
R2: IF X1 = PB AND X2 = PS, THEN Y = NB;
R3: IF X1 = PB AND X2 = Z, THEN Y = NM;
R4: IF X1 = PB AND X2 = NS, THEN Y = NM;
R5: IF X1 = PB AND X2 = NB, THEN Y = NS;
R6: IF X1 = PS AND X2 = PB, THEN Y = NM;
R7: IF X1 = PS AND X2 = PS, THEN Y = NS;
R8: IF X1 = PS AND X2 = Z, THEN Y = NS;
R9: IF X1 = PS AND X2 = NS, THEN Y = Z;
R10: IF X1 = PS AND X2 = NB, THEN Y = Z;
R11: IF X1 = Z AND X2 = Z, THEN Y = Z;
R12: IF X1 = Z AND X2 = PB, THEN Y = NS;
R13: IF X1 = Z AND X2 = PS, THEN Y = NS;
R14: IF X1 = Z AND X2 = NS, THEN Y = PS;
R15: IF X1 = Z AND X2 = NB, THEN Y = PS;
R16: IF X1 = NS AND X2 = NB, THEN Y = PM;
R17: IF X1 = NS AND X2 = NS, THEN Y = PS;
R18: IF X1 = NS AND X2 = Z, THEN Y = PS;
R19: IF X1 = NS AND X2 = PS, THEN Y = Z;
R20: IF X1 = NS AND X2 = PB, THEN Y = Z;
R21: IF X1 = NB AND X2 = NB, THEN Y = PB;
R22: IF X1 = NB AND X2 = NS, THEN Y = PB;
R23: IF X1 = NB AND X2 = Z, THEN Y = PM;
R24: IF X1 = NB AND X2 = PS, THEN Y = PM;
R25: IF X1 = NB AND X2 = PB, THEN Y = PS.
In solving problems of mathematical modeling of systems using fuzzy sets theory is necessary that a large volume of transactions over the linguistic variables, and therefore to perform the operations of fuzzy membership functions used standard form - the triangular [4].
On the basis of rules of fuzzy inference was drawn table 1.
Values of membership functions of output supplies – the angle valve of cold bypass Y is defined by the operator Mamdani.
The specific value of the control action is determined by the procedure of defuzzification method of center of gravity.
Table 1: Rules of inference
| The speed of change of temperature X2 | Temperature value X1 | ||||
| NB | NS | Z | PS | PB | |
| NB | PB | PM | PS | Z | NS |
| NS | PB | PS | PS | Z | NM |
| Z | PM | PS | Z | NS | NM |
| PS | PM | Z | NS | NS | NB |
| PB | PS | Z | NS | NM | NB |
The graphical view of the dependence of the output variable (the angle of rotation of cold bypass valve) on the input values of temperature and rate of temperature change is shown in Figure 3. Naturally, that the angle of the valve increases with increasing temperature and the rate of its increment (the sign «-» in the angle of rotation of the valve means that the valve moves to the left, ie at a value of -90 ° valve is fully open), and vice versa, decreasing temperature rotation angle of the cold bypass valve is reduced, ie valve gradually closes.
Figure 3 The space of possible solutions
Conclusion
Scientific inquiry and analysis in the field of expert systems, thermal management in a column for ammonia synthesis was conducted. Further actions are determined by the need to develop mathematical and algorithmic models of functioning of the expert system and the development of software architecture that is suitable for practical implementation of the system.
Literature:
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7. Система Experion PKS фирмы Honeywell. Спецификации и технические данные, 2003 г.
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Important remark
Master's work is not completed yet. Date of final completion: December 2011. Full text of the work and materials on the subject can be obtained from the author or his supervisor after this date.