Modeling, simulation and optimal control of the natural gas Jibissa plant

Transcription

1 Santander, Cantabria, Spain, September 23-25, 28 Modeling, simulation and optimal control of the natural gas Jibissa plant CRISTIAN PATRASCIOIU 1, MIHAELA PETRE 2, ISSA RABAHI 3 1 Computer and Control Department, 2 Chemical Process Department, 3 Natural Gas Treating Jibissa Plant 1,2 Petroleum-Gas University of Ploiesti Bd. Bucuresti 39, Ploiesti ROMANIA cpatrascioiu@upg-ploiesti.ro Abstract: - The paper represents an analyze of the natural gas treating plant Jibissa. The goal of the research is the optimal process control. The paper has 4 parts. The first part contains a structure analysis of the natural gas treating plant Jibissa. There have been identification the input and output variables of each subsystem. The second part is dedicated to modeling of the absorption subsystem. Because the authors have utilized the PRO II software program, the model of absorption subsystem links the unitary process model of the PRO II software program. There are identified the input-output model variables. The third part is dedicated to the simulation of treating process. This part is divided into process sensibility and the simulation of the controllable process. In the last part the authors have developed a control system adapted for the absorption treating plant Jibissa. Key-Words: - natural gas, hydrogen sulphide, modeling, simulation, control 1 Introduction The delivery natural gases at quantity and quality specified by customer represent an important energetic problem. One source of natural gases of Middle Orient is the gases field Jibissa of Siria. The treating natural gases into Jibissa plant makes the decreasing the carbon dioxide, hydrogen sulphide and water from the natural gases. Theses proceeds are know so much in literature [1, 2] and industrial applications too [3, 4]. One of the actual problems of the chemical plants is represented by the elaboration of the control systems for the reduction of the energetic consumption [5]. The authors have studying the opportunity of development of a control system especially for the Jibissa plant. The study have the four components: a) The study of the structure of treating natural gas Jibissa; b) The modeling of the absorption subsystem; c) The simulation of process model; d) The design of the optimal control system. 2 The structure of treating natural gas Jibissa plant The natural gas treating plant has a typical structure presented in figure 1. The treating plant Jibissa has three sections: compression, absorption and drying. In the compression section, the C 3 and C 4 gas hydrocarbons are eliminated without natural gases. Also, the C 5+ liquid hydrocarbons (light gasoline) are eliminated. In the absorption section, the acid gases H 2 S and CO 2 are eliminated using an absorptionregeneration process with DEA solution. In the last section, the sweet gases are drying with molecular sieve. The focus of this paper is the absorption subsystem. This contains an absorption column, a striping column, a separation vessel for the un-absorption gases and the condensed hydrocarbons and an storage vessel. The absorber is equipped by 25 real trays and it using the DEA solution. The stripping column has two packages, each package has 61 mm tool. Fig. 1. The structure of natural gas treating plant. The real composition of natural gases of Jibissa plant and the equivalent composition is presented in table 1. The ethane, propane, butane, pentane and hexane hydrocarbons, dissolved in the DEA solution, are eliminated into expander vessel. For this reason, the previous components have assimilated to methane. The nitrogen has eliminated from Jibissa feed plant because this component not influenced the absorption. ISSN: ISBN:

2 Santander, Cantabria, Spain, September 23-25, 28 Table 1. The real and equivalent Jibissa plant feed Component Real composition [% mol] Equivalent composition [% mol] CO H 2 S N CH C 2 H C 3 H 8.77 ic 4.16 nc 4.28 ic 5.1 nc 5.8 C H 2 O Total Table 2. The specifications of the treated natural gas Description Unit Limit Hydrogen sulphide ppm 4 Carbon dioxide %.5 For the absorption column model, the authors have utilized the Inside-Out algorithm because the water component of the DEA solution is not vaporized at pressure and temperature column. The operating pressure and operating temperature of the striping column have become established usage of the CHEMDIST algorithm. The input-output structure of the proposed model of the absorption subsystem is presented in figure 3. The input variables are the rate flow feed, the feed composition, the DEA rate flow, the specifications of the sweet gas and the specifications of the striping column. The specifications of the treated natural gas is presented in table 2. 3 The modeling of the absorption subsystem The authors have utilized the PRO II simulation program for modeling the absorption subsystem process. Three rigorous distillation algorithms are available from within the COLUMN unit operation, respectively the Inside-Out algorithm, the CHEMDIST algorithm and the SURE algorithm. The simulating model of the absorption subsystem is presented in figure 2. The subsystem model consists in two columns model, an expander vessel model and two exchanger models. Fig. 3. The absorption process model structure. The output variables of the model are sweet gas composition and the duty of striping column re-boiler. The natural gas treating is a controllable process. In this case, the inputs of the process model may be divided into manipulated variables and disturbances. For this reason, the process model structure will have the form presented in figure 4. Fig. 4. The model structure of the controllable process. Fig. 2. The PRO II simulating model of absorption subsystem. The specifications of the sweet gas and the specifications of the striping column, used by the PRO II simulator program, are transformed into internal parameters of simulator. ISSN: ISBN:

3 Santander, Cantabria, Spain, September 23-25, 28 4 The simulation of the natural gas treating process The simulating of the natural gas treating is necessary to determine the sensibility of process, to calculate the gains of the input-output chains and to make the process control algorithm. All simulation calculus are base a reference operating point, table 3. Table 3. Reference operating point Parameter Unit Value Natural gas rate flow kmol/h 2542 CO 2 in natural gas % mol H 2 S in natural gas % mol 1.82 DEA rate flow kmol/h CO 2 in sweet gas ppm.2 H 2 S in sweet gas ppm.4 Steam of striping re-boiler kmol/h Process sensibility To determine the process sensibility, the authors have simulated 6 various operating cases of the absorption subsystem. The influence of the input variables to the three output variables has been studying. The conclusions of this study are: The quality of the sweet gas (CO 2 and H 2 S concentration) has a nonlinear variation in rapport to rate flow feed, figure 5. The aspect of this variation of the CO 2 composition is generated by the high DEA rate flow. The absorption subsystem may be operated for a large domain of the rate flow feed with the constant reference DEA flow rate. This situation is possible because the reference DEA rate flow is very high. The consequence of this situation is that the real operating cost is greater than the necessary operating cost. CO2 [ppm] Rate flow feed [kmol/h] Fig. 5. The dependence of the CO 2 concentration versus the feed flow rate. The steam flow rate variants versus the feed flow rate because the quantity of CO 2 and H 2 S within the feed increases, figure 6. The total increased of steam flow rate is very small (5%) in rapport to variation of the feed flow rate. The conclusion of Steam [kmol/h] this dependence is that the operating cost is dependent only in rapport versus DEA flow rate Rate flow feed [kmol/h] Fig. 6. The dependence of the steam flow rate versus the flow feed rate. The H 2 S concentration of the treated natural gas versus the feed flow rate is a nonlinear function, figure 7. The DEA flow rate, associated the reference operating point, assures the elimination of the hydrogen sulphide correspondent to the feed flow rate increased with 3% in rapport to reference operating point. This operation modality increasing energy lost into striping column up to 3%. H2S [ppm] Feed flow rate [kmol/h] Fig. 7. The dependence of the H 2 S concentration versus the feed flow rate The simulation of the controllable process The most important manipulated variable of the process is the DEA flow rate, figure 4. The authors have studying the controllable process sensibility by simulated the absorption-stripping process for the various DEA flow rate. The conclusions of the study of the controllable process are: For the feed flow rate correspondent to the reference operating point, the concentration of the carbon dioxide and hydrogen sulphide into treating gas is not influenced by the DEA flow rate greater than 91 kmol/h, figure 8 and figure 9. In rapport to reference operating point, characterized by kmol/h DEA flow rate, the process is operated by 25% excess DEA flow rate, respectively 333 kmol/h excess. ISSN: ISBN:

4 Santander, Cantabria, Spain, September 23-25, 28 CO2 [ppm ] DEA flow rate [Kmol/h] Fig. 8. The dependence of the CO 2 concentration versus the DEA flow rate. H2S [ppm] DEA flow rate [Kmol/h] Fig. 9. The dependence of the H 2 S concentration versus the DEA flow rate. Steam [Kmol/h] The steam flow rate have a linear variation in rapport with DEA flow rate, figure 1. This characteristic indicates that the DEA flow rate is the most important factor of the operating cost DEA flow rate[kmol/h] Fig. 1. The relation between the striping steam flow rate and the DEA flow rate. In conclusion, for the decreasing the operating cost is necessary to decrease the DEA flow rate. The restriction of this operation is maintaining the concentration of the carbon dioxide and hydrogen sulphide at the specification values. 5 Design the optimal control system The reduction of the energetic consumption of the absorption-striping subsystem may be making by the design an optimal control system. The objective function of the stationary state is follow: Φ ( DEA) = DEA, (1) where DEA represents the DEA flow rate. The restriction system is defined by the accomplishing the natural gas specifications: y H 2S 4 ; [ppm] (2) y CO 5. [% mol] (3) 2. An another restriction is defined by the mathematical model of the absorption subsystem [ y, y ] H( F, y,dea) H 2 S CO2 = F, (4) where y H2S and y CO2 represent the hydrogen sulphide and carbon dioxide concentration of the treating natural gas; H mathematical model of the absorption subsystem; F feed flow rate; y F the vector of the feed composition. The minimization of the objective function (1), using the restriction system (2)-(4), has generated the numerical solution opt DEA = 95. [kmol/h] Using this solution, the output of the mathematical model of the absorption subsystem is y H 2S = 2. 96< 4 [ ppm]. yco2 = 2< 5 [ ppm] This result confirms that the mathematical model (1)-(4) may by using to the optimal control system. 6 Conclusion In this paper the authors have analyzing the natural gas treating plant Jibissa. In the first part has analyzing the structure of the natural gas treating plant. There have been identification the input and output variables of each subsystem. The second part contains the model of the absorption subsystem. The model is building using the PRO II software program. The most important part is dedicated to the simulation of absorption subsystem. There has studding the process sensibility and the simulation of the controllable process. The last part is dedicate for the developed a control system, specially adapted for the absorption treating plant Jibissa. ISSN: ISBN:

5 Santander, Cantabria, Spain, September 23-25, 28 References: [1] Robu V.I., Distillation. Fractionation, Editura Tehnica, 1963 (Romanian). [2] Stratula C., Treating Gases, Editura Stiintifica si Eciclopedica, 1984 (Romanian). [3] Kohl A. Nielsen R.- Gas Purification, Gulf Publishing, [4] Tofik Khanmamedov, Superior gas sweetening, Hydrocarbon Engineering, dec., 23, p.1-4. [5] Weiland R. and Dingman J., Column design using mass transfer rate simulation, PTQ, spring 22, p ISSN: ISBN: