Online Monitoring of Exhaut Ga Emiion of a Boiler with Dieel/Biodieel Fuel Blend Andrea Valdman *1, Maurício Bezerra de Souza Jr. 1, Roana Folly 1, Belki Valdman 1 1 School of Chemitry, Chemical Engineering Department, Federal Univerity of Rio de Janeiro, Rio de Janeiro, Brazil Av.Horacio Macedo, 149 - Centro de Tecnologia - Bloco E/l. E-209 - Rio de Janeiro - 21941-909 - Brazil An online, low-cot, neural network baed oft enor wa developed and implemented, with the aim of being compatible with the preent indutrial automation and control ytem. The virtual enor wa deigned to predict exhaut ga emiion level of an originally built dieel boiler with dieel/biodieel mixture blend at different air ratio, uing temperature, flow rate and preure a input variable to the neural network model. With that purpoe, experimental data were obtained for different dieel and biodieel mixture of fuel in the boiler, in order to validate the model. The online experimental data conited of proce variable, obtained uing a SCADA ytem connected to fieldbu communication protocol intrumentation, and exhaut pollutant ga level, obtained uing a conventional ga analyzer. A detailed methodology wa etablihed for each phae of the tudy, including data collection and treatment, topology and neural training comparative tudie, implementation of the neural network algorithm propoed in the SCADA ytem and the application of an online validation and maintenance procedure. Experimental online tet confirmed the compatibility between the exhaut ga emiion level inferred by the online oft enor and thoe obtained with the analyzer. Scan acquiition interval were ix time maller and maintenance proceeding were optimized, without demanding a large time interval. Thu, the automation olution can be ued to provide pollutant monitoring, helping to achieve a more conitent operation, regarding production and environmental profit. 1. Introduction In indutrial procee the difficultie on dealing with operational diturbance are conidered the main caue for productivity loe and ignificant change on final product pecification. The worldwide globalization context implie that indutrial proce plant mut have great flexibility to adapt their production line in order to obtain product within pecification, according to profitability, ocial and environment added value target or even diverified raw material availability. Indutrial plant depend on effective meaurement device to upervie pecific proce variable, allowing automatic control adjutment and diagnoi. However, online meaurement intrument and analyzer are known to be expenive and unreliable olution (Mohler et al., 2010), with large time repone. Thi cenario reveal that 51% of all online application encountered in previou review (Huain, 1999) are baed on the artificial neural network
(ANN) known a multilayer perceptron model (MLP). They are the mot popular ANN ued in engineering application, due to their large generalization capacity, flexibility, and reduced computational complexity (Rivera et al., 2009). Thi lat characteritic mut particularly be conidered when developing an automation-integrated ytem, which hould preent low time repone. On the other hand, combution baed equipment are widely integrated in indutrial environment, where foil fuel are dominant (Drapela et al., 2009). Therefore, pollutant reduction i trongly aimed and everal tudie are being conducted focuing on environmental concern (Alejo Sanche et al., 2010) and efficiency improvement (Hippinen et al., 2010). Due to international environmental policie, allied to the competitive advantage of agribuine, the ue of biodieel a an alternative liquid fuel for indutrial boiler i gradually increaing in everal countrie. Previou contribution alo indicate MLP model a major application for modeling and control of combution procee (Kalogirou, 2003). Thi paper preent the implementation of an online virtual meauring olution for analyzing exhaut ga emiion of a boiler fueled with dieel/biodieel blend, integrating indutrial intrumentation baed on fieldbu communication equipment and oftware, and a MLP neural network tool, pecifically developed to work a a generic field device. 2. Experimental Tet 2.1 Proce and automation ytem facilitie The pilot plant ued in the current work i part of the utility facilitie intalled in the pilot plant of the School of chemitry, chemical department lab in Federal Univerity of Rio de Janeiro. Baically, the plant ha a feed water tank, a fuel mixture tank for preparing the blend and a emi-indutrial vertical boiler (Table 1). Table 1: Boiler pecification Boiler Model CV-VDM-500 Steam Production 500 kg/h Nominal heating power 0.3 MW Steam characteritic Saturated Maximum working preure 8.00 kgf/cm 2 Main fuel Dieel Fuel flow rate ~26 kg/h The automation ytem ha fieldbu protocol baed device for meauring continuou proce variable, a dedicated PLC controller and a SCADA tation to upervie the proce. The continuou proce variable available are: fuel temperature and feed tank level; feed water temperature, tank level, and flow rate; boiler water level; team production flow rate and preure; flue ga emiion temperature. Exhaut ga emiion (O 2, CO, CO 2, NO, NO 2, SO 2 ) were continuouly analyzed with a Teto 350XL flue ga analyzer. The boiler ha a fixed fuel flow rate of 26kg/hr and a regulated damper allow different air flow rate adjutment. The air flow rate wa initially et to 30% of exce air, to achieve the bet performance when uing dieel according to the
manufacturer of the boiler. Previou tudie ugget that tet uing biodieel or biodieel blend in ubtitution to dieel a fuel can be conducted without any modification on the tet burner (Canacki et al., 2009). 2.2 Fuel blend and tet procedure All the blend were prepared before each tet with metropolitan dieel and dende palm biodieel, and followed an experimental chedule, including mixture blend of 20%, 30%, 40%, 50%, 60% and 100% of palm biodieel. After heating the boiler up to the working preure level, the team flow rate wa fixed on 450 kg/hr and the air flow rate were maintained at different level of exce air (10%, 20% and 30%) during an hour each. The experimental procedure wa conducted continuouly along all the proceeding tep and, after each tet, the proce data wa gathered from the SCADA ytem and the ga analyzer. In equence, the experimental data were treated and tudied to achieve the bet MLP neural network model for predicting ga emiion. 3. Reult and Dicuion 3.1 Integrating the oft enor to the available automation ytem Before developing the oft enor, all intance of the available automation ytem were analyzed in order to determine the bet location to implement the algorithm itelf, including the fieldbu intrument and the SCADA ytem. Since the main objective of the developed tool wa to allow the implementation in a many a poible indutrial facilitie, the SCADA ytem wa elected a the development platform (Figure 1). Ga Analyer Analyzer Data non frequent data Boiler plant Fieldbu Intrument SCADA Sytem Proce Data Soft Senor Soft Senor Data Boiler Monitoring Online Sytem Figure 1. Integration of the automation equipment and the oft enor 3.2 Soft enor development Initially, the experimental data were treated to elect the meaured proce variable that mot influenced the ga emiion. Thi tep of development wa baed on the phenomenological analyi of the boiler operation, elimination of poible outlier and conideration of teady tate condition. In thi tudy both input and output data were normalized to the range [0, 1]. A total of 502 input-output pattern were available, being 80 % of them ued for training and 20 % for validation of the ANN. The number of neuron on the hidden layer varied from 4 to 13, where hyperbolic tangent, in, exponential and identity function were teted a
activation function. Different network were trained, validated and analyzed according to the prediction performance limit of tandard deviation ratio and mean quare error. Conidering that the oft enor wa to be implemented a an inherent part of the automation ytem and that the computational effort of the model i a determinant part of it performance, enitivity tudie were carried out, in order to reduce the number of input variable. Only input variable whoe withdraw at leat doubled the prediction error of the MLP were kept (Valdman, 2010). The bet model wa compoed of 6 linear input neuron, 1 hidden layer with 10 hyperbolic tangent neuron and 6 linear output neuron (Figure 2). Thi MLP preented a R 2 coefficient of 0.95 and a mean quare error of 0.01, both for training and validation data, indicating that overfitting wa not preent Feed Water TIT1023 temperature ( C) Flue ga TIT1042 temperature ( C) Fuel TIT1011 temperature ( C) Steam FIT1031 flow rate (m³/h) Steam PIT1032 preure (pi) Exce air SI1046 ratio ( %) O2 (%) CO (ppm) CO2 (%) NO (ppm) NO2 (ppm) Oxigen Carbon monoxide Carbon dioxide Nitrogen monoxide Nitrogen dioxide SO2 Sulphur dioxide (ppm) Figure 2. Soft enor neural network topology 3.3 Online automation ytem development Controlling optimal ratio of reduced pollutant ga emiion and energy gain require efficient monitoring tak with reliable enor to control the etting of the indutrial proce (Rivera et al., 2009). In order to provide indutrial application, a generic MLP neural network tool wa developed in the preent tudy, uing VBA (Viual Baic Application) language and integrated in an indutrial SCADA ytem uch a ifix Proficy. After informing the input variable, the weight, biae and activation function of the model, and the output variable of the neural network model, a real-time online oft enor for the boiler ga emiion prediction wa achieved. On automatic cycle bai, the oft enor receive the 6 pecified meaured proce variable (input variable to the oft enor), procee the neural network model and predict the 6 ga emiion releaed through the chimney (output variable of the oft enor). 3.4 Online validation After validating the automated application itelf, a new experimental tet wa conducted for online confirmation (validation) of the developed oft enor. The blend conited of 31% of biodieel and 69% of dieel a the boiler fuel and the reult for O 2, CO 2, SO 2 and NO 2 are preented on Figure 3.
0 6 20 18 5 16 4 14 12 O 2 (%) 3 CO 2 (%) 10 8 2 6 1 4 2 0 50 50 45 45 40 40 35 35 SO 2 (ppm) 30 25 20 NO 2 (ppm) 30 25 20 15 15 10 10 5 5 0 0 Figure 3. Online experimental validation data comparing oft enor ( ) and ga analyzer ( ) reult The reult obtained for oxygen indicate that the trend are conitent with the proce behavior, revealing the three different tep of air exce. The reult predicted by the oft enor were within the error meaurement preented by the ga analyzer, except for the ulphur dioxide during the firt hour of the run. Conidering that the experimental blend in thi new tet ued a different biodieel, not contemplated on the experimental data et ued during training and validation, the online oft enor preent good interpolation reult. Another important apect of the reult i the lower acquiition time interval achieved by the online oft enor in comparion with the online ga analyzer, including the maintenance of ga prediction during the cleaning purge period of the equipment. In comparion with the online equipment, maintenance procedure are much eaier to be performed and an adjuted (actualized) model can be achieved only a few minute after any experimental tet. 4. Concluion The application developed in thi tudy introduce a new approach of the uefulne of online model-baed enor. The integration of thee concept within an automation ytem increae the opportunitie of ucceful indutrial olution, epecially when critical proce variable are not available online. The availability of dynamic proce information enlarge the poibilitie of improvement on control and optimization trategie and operational diagnoi. Baed on thee fact, the main concluion are: i.a oft enor, baed on MLP, wa developed to predict ga emiion of the reidual gae expelled in reult of fuel combution in a boiler;
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