Optimization of Combustion Process in Coal-Fired Power Plant with Utilization of Acoustic System for In-Furnace Temperature Meas
Total Page:16
File Type:pdf, Size:1020Kb
Applied Thermal Engineering 123 (2017) 711–720 Contents lists available at ScienceDirect Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng Research Paper Optimization of combustion process in coal-fired power plant with utilization of acoustic system for in-furnace temperature measurement ⇑ Łukasz S´ladewski a, , Konrad Wojdan a, Konrad S´wirski a, Tomasz Janda b, Daniel Nabagło b, Jerzy Chachuła b a Warsaw University of Technology, Institute of Heat Engineering, Nowowiejska 21/25 St., 00-665 Warszawa, Poland b EDF Polska CUW Sp. z o.o., Research & Development Department, Ciepłownicza 1 St., 31-587 Kraków, Poland highlights Novel approach in combustion modelling and optimization in coal-fired boiler. R&D and implementation project that was carried on on real boiler. Artificial immune optimization system integrated with temperature profile measurement. Proper control of the fireball shape resulted in 0.27% boiler efficiency increase. article info abstract Article history: This paper presents methodology and results of a research project on software optimization of combus- Received 26 November 2016 tion process efficiency in coal-fired power plant. Accepted 14 May 2017 The general goal of this project was to increase boiler efficiency by proper control of the combustion Available online 19 May 2017 process using optimization software, integrated with Distributed Control System and in-furnace temper- ature profile measurement system. The research goal relays on new approach in combustion modelling Keywords: based on in-furnace temperature distribution and utilization of this model in on-line boiler control. it is Combustion optimization assumed that this approach allows for more precise control of the combustion process, what finally has a Combustion advanced control positive influence on boiler performance – the efficiency in specific. Power boilers Boiler efficiency The solution has been designed, installed and tested on existing, utility plant – 225 MW (650 t/h of Acoustic temperature measurement nominal steam generation). Final analysis has shown positive results – the boiler efficiency increased over 0.25%. Ó 2017 Published by Elsevier Ltd. 1. Introduction modernization of boiler equipment e.g. new measurement tech- nologies, to on-line software optimization systems. The electricity sector worldwide faces still-increasing demand During last few years contactless temperature measurement for cost efficient power generation and stricter environmental technologies have become more popular in the industry. There regulations. These two factors motivate operators, especially in are two main technologies, which are used in coal fired boilers to coal-fired generating stations, to search new solutions for process measure temperature distribution – acoustic technology [2–4] optimization. Nowadays, coal still plays an important role in and laser technology [5,6].In[7] authors present advantages of electricity production – over 40% of global electricity production acoustic system in evaluation of combustion quality in pulverized comes from coal [1]. That is why optimization of combustion pro- coal-fired boiler. On the other hand, in [8] the meaning of laser cess in terms of boiler efficiency and emission of air pollutants is technology in combustion optimization projects is presented. Tem- the key in minimizing operational and maintenance costs. There perature distribution in horizontal cross-section of a boiler is a are many methods for such optimization, which range from great indicator of quality of combustion process. This indicator could be used in boiler control to optimize the combustion process. The research key of this work is to develop new modelling ⇑ Corresponding author. approach of the combustion process which is based on in-furnace ´ E-mail addresses: lukasz.sladewski@itc.pw.edu.pl (Ł. Sladewski), konrad.wojdan@ temperature distribution. This new model will be used by combus- itc.pw.edu.pl (K. Wojdan), konrad.swirski@itc.pw.edu.pl (K. S´wirski), tomasz.janda@ edf.pl (T. Janda), daniel.nabaglo@edf.pl (D. Nabagło), jerzy.chachula@edf.pl tion optimization software for on-line control of the combustion. (J. Chachuła). http://dx.doi.org/10.1016/j.applthermaleng.2017.05.078 1359-4311/Ó 2017 Published by Elsevier Ltd. Pobrano z http://repo.pw.edu.pl / Downloaded from Repository of Warsaw University of Technology 2021-09-24 712 Ł. S´ladewski et al. / Applied Thermal Engineering 123 (2017) 711–720 Nomenclature ðpÞ ai autoregressive coefficients T gas temperature [K] A current process operating point ua current value of process inputs vector i th AC Advanced Control ukÀj process input at k-j time stamp ½ =ð Á Þ k B universal gas constant J mol K , ub average value of process inputs vector before control iðpÞ th bj process input coefficients change in k lymphocyte ½ = k C speed of sound m s ua average value of process inputs vector after control cðpÞ constants change in kth lymphocyte CFD Computational Fluid Dynamics wðpÞ weights of disturbance partition ~ CV process outputs (Controlled Variables) yk Current value for k-th monitored process output D number of disturbance signals y ^k Demand value for k-th o monitored process output DCS Distributed Control System ya current value of process outputs vector r th DV process disturbances (Disturbance Variables) yk r process output in NAMAX method r th th FD forced draft fan ykÀi r process output at k-i time stamp k g condition function for lymphocyte fitting process yb average value of process outputs vector before control ID forced draft fan change in kth lymphocyte k K number of inputs ya average value of process outputs vector after control th Kk matrix of the automatically identified input-output change in k lymphocyte gains of the process z process disturbances l distance between particular transmitter and particular za current value of process disturbances vector receiver [m] ak Linear penalty coefficient for k-th manipulated variable b LOI Loss on Ignition, k Square penalty coefficient for k-th manipulated variable c M molecular weight [mol] k Linear penalty coefficient for k-th monitored process M time horizon of process inputs output ~ c d mk Current value for k-th manipulated variable k Square penalty coefficient for k-th monitored process ^c m k Demand for k-th manipulated variable output d MIMO Multi-Input-Multi-Output D!m optimal change vector of manipulated variables MPC Model Predictive Control DT vector of optimal changes for twelve current AGAM MV process controlled inputs (Manipulated Variables) temperatures N autoregressive time horizon Duk control change in process inputs vector in kth lympho- p disturbance partition cyte P number of disturbance partitions Dyk static process response on outputs vector in kth lympho- _ Q loss sum of heat losses cyte _ ð ; Þ Q out sum of useful heat outputs l Lk A lymphocyte selection function _ Q tot sum of heat losses and useful heat outputs gB boiler efficiency R specific gas constant [J=ðkg Á KÞ] j adiabatic coefficient [–] R ! number of process outputs s flight time [s] ~ ð Þ slm sd T AGAM Dispersion of current shape, defined by vector of mea- k Insensibility zone for linear penalty for k-th manipu- sured AGAM temperatures lated variable ^ sm sd ! Dispersion in reference shape s Insensibility zone for square penalty for k-th manipu- ~ k siðT AGAMÞ Intensity of current shape, defined by vector of mea- lated variable sured AGAM temperatures sly Insensibility zone for linear penalty for k-th monitored ^ k si ! Intensity in reference shape process output ~ ð Þ ssy sp T AGAM Left-right position of temperatures hotspot of current k Insensibility zone for square penalty for k-th monitored shape, defined by vector of measured AGAM tempera- process output ðÞ ð Þ ¼ 1 ð þj jÞ tures þ ‘‘positive” operator x þ 2 x x ^ sp Left-right position of temperatures hotspot in reference shape Regarding software optimization systems for advanced com- network and genetic algorithm for combustion process control bustion control there is number of different solutions. The main are described in [16–19]. Effective combustion optimization could difference between those solutions relay on difference in approach be also performed by solutions, which combine artificial neural in process modelling and difference in algorithms used to search network models and quadratic programming [20], artificial bee for optimal solution. MPC is the main group of advanced process colony [21] or particle swarm [22] algorithms. Moreover, in [22] control algorithms for MIMO processes e.g. combustion process. authors provide a comparative analysis of artificial neural network Detailed description of different MPC solutions are provided in and support vector machine models of the combustion process. [9,10] and their advantages in combustion optimization were The support vector machine algorithm [23] have found also its described in [11–14]. MPC as well as steady-state optimization application in combustion process optimization with genetic algo- solutions utilize often algorithms, which are inspired by processes rithm [24,25], ant colony [26,27], particle swarm, strength pareto that could be observed in the nature. One of most popular solutions evolutionary or archive-based hybrid scatter search [28] algo- combines artificial neural network algorithms for process mod- rithms. Computational fluid dynamics is another method for com- elling and genetic algorithm as the method for searching for the bustion process modelling, which has been used for on-line optimal solution. General idea