Challenges for the Coal Industry in the EU Polish-Spanish workshop. Marco López Fernández. Katowice, Poland, 9-10 May 2018

Transcription

1 Challenges for the Coal Industry in the EU Polish-Spanish workshop Marco López Fernández Katowice, Poland, 9-10 May 2018

2 Index 1. Gas Natural Fenosa 2. NO x improvements with advanced sensors and smart controls 3. Case study: Meirama 4. Case study: La Robla 5. Conclusions

3 Gas Natural Fenosa

4 1 Gas Natural Fenosa Company presentation Gas Natural Fenosa is a leading company vertically integrated in gas and electricity 1. Figures at 31/12/17 4

5 1 Gas Natural Fenosa The group in the world 5

6 1 Gas Natural Fenosa Business profile 6

7 1 Gas Natural Fenosa Accolades 7

8 2 Coal Power Plants GNF has a competitive mix of technologies There are 15.4 GW of installed power in combined cycle plants, nuclear power plants, coal-fired plants, fuel-gas plants, hydroelectric plants and wind farms In Spain, our power plants amount to almost 13 GW: 7 GW natural gas combined cycles 2 GW of hydroelectric plants 2 GW of coal-fired plants 1.1 GW of renewable energy 0.6 GW of nuclear power. Meirama 1 unit 580 MW Anllares(2/3) 1 unit 243 MW (In closure process) Narcea COAL La Robla Anllares La Robla 2 units 655 MW 4 sites 6 units MW Narcea 2 units 530 MW 8

9 NO x improvements with advanced sensors and smart controls

10 2 NO x improvement with advanced sensors and smart controls Furnaces are frequently operated with an unnecessarily high proportion of excess air which represents an efficiency penalty and promotes NO x formation Sensors for combustion processes have been limited in the past by their reliability in the boiler environment. Newer technologies such as tunable diode laser absorption spectroscopy are allowing the temperature and composition of flue gas to be mapped. At the same time, devices for online monitoring of the coal and air flow distribution to individual burners enable combustion stoichiometry to be precisely controlled in real time. These additional data allow the control system to balance combustion and reduce excess air to optimum levels, improving efficiency and NO x emissions. These systems usually make use of complex algorithms such as neural networks which can be trained on operational data to build up an empirical model of the plant, which is able to continuously identify the optimum combination of control actions for a given set of demands on the plant. 10

11 Meirama

12 3 Meirama Power Plant History One of the four coal thermoelectric owned by Gas Natural Fenosa Generación : Located in the north west of Spain, about 35 km from the Port of La Coruña. The plant went into operation on 1980, designed and built to burn brown lignite of a nearby mine, already exhausted. Between 2008 and 2009, the boiler was transformed in order to use imported coals, extending its life beyond that of the lignite field and adapting to the new environmental regulations. 12

13 3 Meirama Power Plant NO x Emissions reduction program After boiler transformation in 2008, normal NO x emission level was around 550 mg/nm 3 To reach lower NO x emission had a negative impact on CO emission (limited to 125 mg/nm 3 ) an unburned coal on ashes (limited to 5%). In January 2017, a test campaign was developed with assistance of INERCO. It was shown that under stable conditions it would be possible to reach 380 mg/nm 3 at full load and even less of 200 mg/nm 3 at minimum load (depending on the fire levels/mills on service). CO emissions were kept on acceptable value ( mg/nm 3 ) and unburned coal in ashes remained below 5%. Through 2017, new operation guidelines and settings were implemented with a significant reduction on emission levels. Permanent attention from operation personal was required and poor results during transient and load changes were obtained, since all changes were made manually (automatic NO x /CO controls were set to manual mode). 13

14 3 Meirama Power Plant NO x Emissions reduction program 800 Foco -NOx Valor (mg/nm ) y = -0,8029x /16 01/17 03/17 04/17 06/17 08/17 09/17 14

15 3 Meirama Power Plant Siemens P3000 System General Overview In order to automatize the NO x control operation and get better responses during transients and load changes, P3000 system from Siemens was implemented during second half of 2017 P3000 takes in account data from a new laser measurement system located in the boiler, above OFA injection section and 461 signals coming from existing DCS and produces set point corrections ( biasses ) to DCS control loops Laser Measures Data Analysis (Siemens T3000) Hybrid Closed Loop Control (Siemens + ABB) 15

16 3 Meirama Power Plant Siemens P3000 System General Overview 16

17 3 Meirama Power Plant Siemens P3000 System General Overview P3000 (Siemens) Modules DCS ABB Laser Measures Total OFA Flow 1x Bias OFA Flow OFA OFA Secondary Air Flow Vertical Shift Secondary Air Flow Balancing at Burners OFA Flow Shift North/South O 2 at boiler economizer 16x Bias Secondary Air Flow 1x Bias OFA Shift 1x Bias de O 2 Burners N4 N3 N2 N1 Sec. Air Vertical Shift of Coal Between Burner Level 4x Bias Mill Load M1 M2 M3 M4 Mills Classifiers Speed 4x Bias Classifiers Speed Coal and Primary Air

18 3 Meirama Power Plant Siemens P3000 Lay Out Twelve transmitter/receptor pairs are used All sensors are connected to a Matrix Distribution Cabinet (MDC), located at boiler building. Control Cabinet is located in electronic room, close to main control room. It communicates with MDC 18

19 3 Meirama Power Plant T3000 System configuration 19

20 3 Meirama Power Plant Siemens P3000 Interface with existing DCS 20

21 3 Meirama Power Plant Combustion optimization bias

22 3 Meirama Power Plant Path status monitoring system

23 3 Meirama Power Plant Results. OFA Manual Dumpers Adjustment Before After Optimization Higher OFA Flow Higher O 2 concentration in center area Higher CO concentration close to the furnace walls More homogeneous distribution of O 2 and CO Lower CO concentration close to the furnace walls

24 3 Meirama Power Plant Results. Emissions during Load Changes Emission reduction in load changes Faster adjustments during load changes CO Emission Power

25 3 Meirama Power Plant Results. Mean NO x Emissions Reduction NO x Emissions -mg/nm³ , ,8 398, P3000 OFF P ON (1º setting) - 6 Nov P3000 ON from 14 Nov kcal/kg coal P3000 OFF P3000 ON MW load 4 Mills Low to Mid load - 2 Mills

26 3 Meirama Power Plant Results. Mean NO x Emissions Reduction 550MW 570MW 470MW 435 mg/nm3 390 mg/nm3 Carbón B-9 N2 (%) 1,9 PCIb (Kcal/Kg) mg/nm3 P Off P ON

27 La Robla

28 4 Robla Power Plant Plant data Power output: 350 MWe Comissioned in 1980 Down-Shot Firing boiler Steam Capacity: 1078 t/h Six Riley Power Inc. Ball Tube Mills, five necessary for full load 24 DS Hitachi burners (Low NO x ) Over Fired Air DCS T

29 4 Robla Power Plant Goals & Strategy Gas Natural Fenosa decided to implement GE Combustion Opt in order to: Reduce NO x emission Improve Boiler efficiency Contain CO under 100 mg/nm 3 The system makes use of optimization technologies Learn complex process relationships Determine the optimal fuel and air setpoints for specified goals and constraints Adjust the available fuel and air variables in real-time 29

30 4 Robla Power Plant Combustion Opt The system is based on: Neural network-based optimization: Nonlinear, multivariable steady-state models used to identify the best combinations of variables under varying conditions. Used for the majority of the manipulated variables which can be adjusted over time to balance unit operations. Model predictive control (MPC): Uses dynamic models to predict future changes. It is used for the major gross-air controls, which must respond quickly to plant conditions. Expert rules: Opportunistically defined rules about what to do under specific conditions to achieve goals Then the system adjusts the DCS biases to position dampers, overfire air and other controllable parameters at their optimal settings for given sets of conditions, objectives and constraints 30

31 4 Robla Power Plant Combustion Opt Controlled Variables (CVs): The plant variables we want to control (NO x, CO, Heat rate, O 2 ) Manipulated Variables (MVs): The process variables optimizer moves to affect controlled variables. Have minimum and maximum limits in which they can be moved (airs, Over Fired Air ) Disturbance Variables (DVs): Those variables that affect the controlled variables, but can not be adjusted by the optimizer (ambient conditions, load ) 31

32 4 Robla Power Plant Combustion Opt Process optimization starts with a model Predict NO x emissions, based upon inputs (manipulated variables and disturbance variables) The Optimizer then uses the model in reverse Searches the possible manipulated variable settings to find the ones that minimize the predicted NO x Control and process constraints are respected Can have different goals in mind for each objective: NO x (Down), CO (Limit), Efficiency (Up) Sends the recommended changes to the DCS Works with the fine-tuning biases 32

33 4 Robla Power Plant Combustion Opt Neural optimization moves MV s slowly taking into account all objectives of the optimizer Neural net model is a mathematical model of MV and DV inputs to predict the CV output (objectives) Model learns or changes as new data is presented during it s nightly training and testing cycle Neural optimization manages tradeoffs if MVs are moved for benefit of objective 1 then what will be the effect on other objectives 33

34 4 Robla Power Plant Combustion Opt Integration with the DCS is simple: It does not require additional instrumentation (only a CO sensor was installed) It can be connected to Plant Information (PI) and to DCS through OPC Server The system has remote connections via VPN 34

35 4 Robla Power Plant Combustion Opt The system has to be enabled by the operators, and HM Interface allows: See what the Neural optimizer is working on See what the Neural optimizer has been working on for the last week See what the limits are on the Neural MV s See the neural model s prediction of NO x Use the neural model of NO x to analyze an event 35

36 4 Robla Power Plant Combustion Opt Final tuning of the system is still ongoing, so definitive results has not been yet obtained On average, we consider that 10% NO x reduction is achievable, maintaining the boundary conditions Further improvement in boiler efficiency is also an outcome of the system 36

37 Conclusions

38 5 Conclusions Making use of new instrumentation and data analytics the NO x emissions can be significantly improved. To take advantage of these technologies, a good understanding of the combustion processes and an adequate knowledge of the boiler is required. This improvements can represent a significant OPEX savings in urea or ammonia if secondary NO x measures are installed, or even a CAPEX reduction in NO x abatement technologies are not still implemented. Further improvements on heat rate can be also achieved, since the excess O 2 is reduced. 38

39 Thanks - Dziękuję Copyright Gas Natural SDG, S.A.