Oxygen Content In Flue Gas Research Articles

Carbon monoxide emission models for small-scale biomass combustion of wooden pellets

Tighter legal emission limits require means to prevent releasing harmful substances into the atmosphere during the combustion of biomass. Economic considerations suggest to meet these restrictions by improving the ability to predict and therefore prevent emissions, which can be done by improved control algorithms. This work presents different methods to obtain models for the prediction of carbon monoxide emissions in a small-scale biomass combustion furnace for wooden pellets. The presented models are intended for an application in model based control, either as part of the underlying model or for carbon monoxide soft sensing and fault detection. The main focus is on simple structures which can be handled by the already existing hardware of the furnaces. Different black-box models and a kinetic process model are introduced and compared. The black-box models are based on the measured flue gas oxygen concentration and the combustion temperature, since these measurements are typically available even for smaller plants. The obtained models are validated with measured data in order to find the most suitable structures, of which combined fuzzy black-box models show the most promising results. The presented methodology can be readily applied to the investigated furnace. However, the model parameters have to be adapted for other plants.

Emissions from in-use residential wood pellet boilers and potential emissions savings using thermal storage

Effective biomass energy utilization for residential space heating relies on having combustion systems with both high efficiency and low emissions. European technology in wood pellet boilers was introduced into the United States over the past decade. However, these systems need to be designed and operated to minimize the emissions of air pollutants, particularly airborne particulate matter. One approach to minimize emissions is to improve system efficiency and limit the number of boiler cycles that include start-up and shut down periods where emission rates are higher by utilizing thermal storage as part of the boiler system. In this study, emissions from two 25 kW European-designed, but U.S. manufactured wood pellet boilers (PB and WPB) with thermal energy storage (TES) were measured in actual home operation using the EPA CTM-039 stack sampling method. These measurements allowed the estimation of the emissions reductions due to the presence of TES. PB had much higher emissions than WPB because PB had frequent local oxygen deficit-induced non-uniform combustion, which highlights the significance of periodic onsite oxygen tuning after the boiler installation. Particulate emissions were dominated by PM2.5 and the particles mainly consisted of low melting point, alkali compounds such as K2SO4, KCl, Na2SO4, CaCl2, etc. Both PM2.5 and polycyclic aromatic hydrocarbons (PAHs) emissions increased linearly with CO because they are products of incomplete combustion. Optimum boiler operating conditions were found with 12% flue gas oxygen content for both systems to achieve minimum CO emissions, which is 2% higher than the manufacturer's set-point of 10%. The potential emissions reductions by using a system with TES instead of a non-TES system were estimated under three scenarios. The results showed both significant gaseous and particulate matter emissions reductions that demonstrate that modern, high-efficiency wood pellet boilers with TES systems can produce heat with lower total emissions compared to non-TES systems.

Loop Interaction Analysis and Control Structure Selection: Application to a Fluid Catalytic Cracking Unit

A simulation approach for the assessment of variables interaction and consequent control structure selection of a fluid catalytic cracking unit (FCCU) is presented in this paper. The simulator which was implemented in Matlab draws from an earlier mathematical model of the FCCU, was used as a virtual FCCU for studying the dynamic response of the riser temperature (T rx ), the regenerator temperature (T rg ) and the regenerator flue gas oxygen concentration (O d ) to step changes in air flow rate (F a ), regenerated catalyst flow rate (F rc ), gas oil feed rate (F gr ). The results show strong interaction in FCCU variables, with F a affecting T rg and O d ; F rc affecting T rx, T rg and O d ; F gr affecting T rx , T rg and O d . A linearised state-space model based on the first-principle model was deduced and transformed to a 3x3 input-output model. Three channel interaction measures: Relative Gain Array (RGA), Effective Relative Gain Array (ERGA) and the Normalized Relative Gain Array (RNGA) were applied to the selection of FCCU control structure. All the measures point to a diagonal scheme with the following pairings: (T rx /F gr ), (T rg /F a ) and (O d /F rc ) ,for the decentralized control of the riser temperature, the regenerator temperature and the flue gas oxygen concentration respectively. The suggested control structure offers a high promise of stability, with a Niederlinski index (NI) of 101.79. DOI : 10.7176/CTI/8-03

A new method for boiler combustion calculation

Fuel combustion calculation is an important basic work of boiler performance test and analysis. During the process of combustion calculation, solving excess air ratio and actual volume of dry flue gas are key objectives. Current method is to firstly calculate excess air ratio, then calculate actual volume of dry flue gas. And in order to solve excess air ratio, almost all fue gas composition need to be measured, which will not only increase the workload of measurement, but also reduce the accuracy of calculation results. To this end, a new combusion calculating method is proposed in this paper which solves actual dry flue gas ratio through oxygen content in flue gas, thus actual volume of dry flue gas is calculated. Finally, the excess air ratio is obtained through acutal volume of dry flue gas. By the new method, the flue gas composition measure items can be reduced to only oxygen content, but it will not reduce the precision of combustion calculation.

NOx reduction by CO over ASC catalysts in a simulated rotary reactor: Effect of reaction conditions

A rotary reactor, separated the NOx removal process into adsorption stage and reduction stage, was utilized to remove the NOx from the flue gas. The influences of various reaction conditions on the reactor were tested, i.e., oxygen concentration in the adsorption zone, time ratio of adsorption and reduction, gas hourly space velocity (GHSV) of the reactor, GHSV of the reduction zone and effect of H2O and SO2. The results showed that the synthesized ASC catalyst had higher deNOx efficiency with the increasing of oxygen concentration in flue gas. Meanwhile, the GHSV of the reactor had an obvious influence on NOx adsorption, and high NOx adsorption and reduction efficiencies were observed at 10,000 h−1. The optimal reaction conditions in this research were concluded as follows: oxygen concentration was 8%, time ratio of adsorption and reduction was 60 s: 60 s, GHSV of the adsorption zone was 10,000 h−1 and GHSV of the reduction zone was 12,000 h−1. While the water and SO2 tolerance of catalysts was weak, and the catalysts would be inactivated when exposed to 100 ppm SO2 in 1.5 h. Free nitrate ions were the main nitrate species on the catalyst surface. In situ DRIFT study showed that a large amount of free nitrate ions was released from the catalyst surface when the reduction time was increased from 30 s to 60 s, and much NOx was adsorbed on the catalyst surface in the form of free nitrate ions when the oxygen concentration was 8%.

Combustion adjustment test of circulating fluidized bed boiler

To achieve optimal operation, a combustion adjustment test were carried out in a 240t/h CFB boiler. Three factors, including bed pressure drop, primary air flow and secondary air flow were studied, each of which had three values of factor levels. Results show that the sensible heat loss in exhaust flue gas is the largest part of heat loss, accounting for more than 75%. To achieve higher thermal efficiency, the bed pressure drop should be maintained around 7.2kPa, the oxygen content in flue gas around 5%, and the bed temperature around 900°C. From the point of combustion efficiency, the boiler can be operated under a relatively low bed pressure drop, around 6.7kPa. The heat losses due to the unburned combustibles are relatively small. The NOx concentration in flue gas was also studied, from which it can be concluded that the emissions targets should be set up in combination with the boiler efficiency, especially CO concentration in flue gas. Through combustion adjustments, the NOx emission can be decreased by about 50% without affecting the boiler efficiency.

Combustion of Lignite Coal in a Bubbling Fluidized Bed Combustor under Oxyfuel Conditions

Oxyfuel combustion is one of the leading technologies considered for capturing CO2. The field of oxyfuel combustion in a bubbling fluidized bed is recently addressed only marginally, but it is more suitable for lower capacity loads. The paper presents some results from bubbling fluidized bed oxyfuel combustion of lignite coal from 25kW experimental device and 250kW pilot BFB boiler. The methodology of the results presentation is stated and confrontation of the experimental results with the mathematical model is presented. The experiments were focused on identification of the oxyfuel regime operation and determination of the different CO2/O2 ratio in fluidized medium. The CO2 flue gas concentration was for the majority of experiments kept in the range of 84 to 91% and the oxygen concentration in flue gas from 4 to 8% in case of smaller laboratory combustor. Pilot BFB combustor suffers more with air leakage but the results correspond to the reality of the real operation of these facilities in an industrial scale.

RIMER and SA based thermal efficiency optimization for fired heaters

Due to frequent changes in thermal load and drift of online oxygen analyzer, the heater’s thermal efficiency optimization system with limited maintenance resources seldom works in long term. To solve this problem, a novel and practical optimization method combing RIMER (i.e. belief rule-base inference methodology using the evidential reasoning) approach and SA (stochastic approximation) on-line self-optimization is proposed. The optimization scheme consists of (i) an off-line expert system that determines the optimal steady state operation for a given thermal load, and (ii) an on-line optimization system that further improves the thermal efficiency to alleviate the influence caused by sensors’ drift. In more details, at the off-line stage, a belief-rule-base (BRB) expert system is constructed to determine good initial references of operating conditions for a specified thermal load, which quickly drives the system to a near optimal operation point when confronted with the thermal load change; this is based on RIMER. During on-line operation, these off-line determined initial values are further refined by using the SA approach to optimize the thermal efficiency. The newly obtained optimal operating condition then is updated online to compensate the sensor’s drift. The optimized profile is implemented through a practical control strategy for the flue gas – air system of fired heaters, which is applied to the flue gas oxygen concentration and chamber negative pressure control on the basis of flue gas-air control system. Simulation results on the UniSim™ Design platform demonstrate the feasibility of the proposed optimization scheme. Furthermore, the field implementation results at a real process illustrate the effectiveness of this optimization system. Both simulation and field application show that the thermal efficiency can be nearly improved by c. 1%.

Optimization of the Exhaust Gas Oxygen Content for Coal-fired Power Plant Boiler

During the running of utility boiler, the flue gas oxygen content is one of the most important factors that affects boiler efficiency, and it affects each other with other thermal economic parameters such as flue gas temperature, unburned carbon in fly ash and slag and coal consumption of power supply. So it's a key parameter to optimize the combustion process of fuel in utility boiler through combustion control system. Colligating the effect of flue gas oxygen content, flue gas temperature and unburned carbon in fly ash and slag on coal consumption, and aim for reducing coal consumption, the equations for associating flue gas temperature with power and oxygen content, and unburned carbon in fly ash and slag with power and oxygen content are obtained by three dimensional data fitting. And model for calculating boiler efficiency based on boiler heat balance is established. With the formula of coal consumption, the partial differential equation for calculating the variation rate of coal consumption is obtained, and optimal oxygen content is obtained through theoretical analysis and experimental research. The analysis of a 1000MW coal-fired power unit in Guangdong Province shows that the coal consumption operated at optimal oxygen content is reduced.

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation–model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler—the KPA Unicon BioGrate boiler.

Quantitative measurement and indication of pulverized coal ignition temperatures in O2/CO2 environments

The method on quantitative determination of the ignition condition of pulverized coal particles in a laminar drop tube furnace (DTF) was proposed with the continuous measurement of the evolved oxygen concentration in flue gas at variable furnace wall temperatures. Then the method was adopted to quantitatively determine the coal ignition temperatures in different O2/CO2 and air environments for five coal ranks with the measurement of the separate axial temperature profiles in the furnace centerline with and without coal particles feeding. The result shows that the coal ignition temperature in 21%O2/79%CO2 environment measured by DTF method is higher than that in air environment for each coal rank. Moreover, the coal ignition temperature significantly decreases with the increasing oxygen partial pressure in O2/CO2 environments, and the coal ignition temperature in O2/CO2 environment can match that in air environment when the oxygen partial pressure is increased to the value ranging from 25.8% to 44.1%, which is relative to different coal ranks.

Ceria substrate–oxide composites as catalyst for highly efficient catalytic oxidation of NO by O2

Oxidation catalysts can be used to achieve highly efficient NO oxidation by residual O2 in flue gas and decrease the power consumption of pollutant oxidation process through ozone injection. Ceria was selected as the catalyst carrier because of its excellent oxygen storage capacity. Ceria substrate composite catalysts were prepared through sol–gel method. The effects of dopant metals (Co, Mn, Fe, Cr, and Ni), calcination temperatures, and Co doping content on NO conversion efficiency were assessed. The physico-chemical characteristics of catalysts prepared were analyzed through BET, XRD, TPR, TEM, and XPS. Cobalt was used as the dopant metal because of its excellent catalytic activity. The optimal condition was 400°C calcination, and the effective mole ratio of Co to Ce was 0.51. Under the optimal condition, the Ce–Co composite catalyst yielded an NO conversion efficiency of 93% at 230°C. The catalytic activity of the Ce–Co composite catalyst on NO oxidization was related to structural features, crystalline pattern, redox ability, and interactions between cobalt and ceria. The effect of residual oxygen concentration in flue gas on NO oxidation was evaluated. When the oxygen concentration exceeded 6%, NO conversion efficiency stabilized. The sulfur- and water-resistant ability of the Ce–Co composite catalyst was very weak. Only 53% NO conversion was obtained at 190°C when 200ppm SO2 and 3% steam were added.

Application of Fuzzy Neural Network in the Intelligent Control of Biomass Gasifier

In view of biomass gasify process which has nonlinear, non-minimum- phase, big delay and strong load interference characteristics, a fuzzy neural network control algorithm for biomass gasifier is presented. Gasified process of biomass gasifier has been researched, and intelligent control of temperature and primary air flow for biomass gasifier has been designed. The control objects are gasifier temperature and flue gas oxygen content , the regulation objects are the material feed flow and primary air flow. Simulation results show that, this system has better control effect than the traditional fuzzy control system.

Based on Vari Combination Forecasting Model for Power Plant Flue Gas Oxygen Contentance-Covariance Method

Focusing on the problem which power plant flue gas oxygen content is hard to predict efficiently, an intelligent hybrid prediction method is proposed. First, the RBF neural network and principal component analysis method are utilized to reduce the dimension of the input variables. Second, the case-based reasoning method is carried on to predict the flue gas oxygen content based on the above analysis results. Then, in order to reflect the cumulative effect of the flue gas oxygen content with time, the GM (1, 1) grey prediction method is used to predict the current flue gas oxygen content value. Finally, the final flue gas oxygen content is obtained by the variance-covariance method of weight combination forecasting. Simulations and industrial results show that the proposed intelligent hybrid prediction model has high accuracy and robustness, and can solve the problem of predicting the flue gas oxygen content effectively.

Thermochemical Wear of Refractory Linings Associated With Gas Firing in Metallurgical Plants

The present paper deals with the corrosive interaction between the atmosphere and the refractory lining as well as in one case the steel shell for two industrial facilities. In one of the following cases a magnesia carbon lining is partly decarburized by the atmosphere during preheating of a steel ladle. In the other case, the flue gas from blast furnace gas heating of a hot blast stove causes a nitric attack on the cold side of the refractory lining at the steel shell. Investigation of these mechanisms rely on computational fluid dynamics (CFD) simulations which quantify the flow and temperature field as well as the species transport during heat release from the gas combustion in both plants. Furthermore, the heat exchange between hot gas and refractory is estimated and thereby associated chemical reactions with/within the refractory material. In both CFD case studies a non‐premixed approach was applied to model the combustion process and to characterize the flue gas composition. The follow‐up reactions with refractory lining and the steel casing are discussed specifically for each case. The calculated oxygen content in flue gas during ladle preheating is estimated to 15%vol. The maximum heat transfer between hot gas and refractory takes place in the bottom region of the ladle due to convection. The radiative heat exchange plays the most important role in the zone of magnesia‐carbon lining. Within the investigation of carbon burnout kinetics limiting factors like diffusion and mass transfer coefficient of oxygen were defined and (partially) determined. The NO formation during the blast stove heating period strongly depends on the dome temperature which is an important blast process control value. Calculated NO emission loads showed promising agreement with real plant data and literature trends.

Development of an air fuel control system for a domestic wood pellet boiler

Wood pellets are a kind of solid biomass energy and a renewable energy source. Made by compressing sawdust, wood pellets have a higher energy density than split firewood and wood chips. In 2007, the new and renewable energy (NRE) portion was 2.4% with respect to total primary energy in Korea. The Korean government wants to increase the new and renewable energy (NRE) portion up to 6.1% by 2020 [1]. To achieve this target, the government has been establishing some policies, such as incentive policy, NRE mandatory use for public building and renewable portfolio standard (RPS) and so on. To supply wood pellets as fuel for the combustion chamber of a wood pellet boiler, most domestic wood pellet boilers put a constant volume by using an auger type fuel feed system. In an auger system as fuel feeding, there is the possibility of changing energy input due to the different density of wood pellets even in a constant volume flow rate of wood pellets. If fuel input rate is changed without any correction of air flow rate for combustion, the condition of combustion in a wood pellet boiler can be deteriorated. We have developed an air-fuel control system for a domestic wood pellet boiler by using flue gas oxygen concentration measurement and a PID controller. To measure O2 concentration of flue gas, a wide band O2 sensor was adopted. We changed fuel input from 100% to 50% by artificial manipulation to confirm the control system. The O2 concentration in flue gas can be controlled to be 8.5% ± 1% without significant change of CO and NOx concentration.

A novel approach for modelling of NOx emission reduction in a tangentially fired coal boiler

In this research paper, predictive modelling of NOx emission of a 210 MW capacity pulverised coal-fired boiler and combustion parameter optimisation to reduce NOx emission in flue gas is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt were studied. The data collected from parametric field experiments were used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load condition and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in sequential quadratic programming (SQP) and genetic algorithm (GA) to find the optimum level of input operating conditions for low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.

Monitoring of Efficiency and NOx Emissions at a Coal-Fired Utility Boiler

In order to improve boiler efficiency and to reduce the NOx emission of a coal-fired utility boiler using combustion optimization, a hybrid model was proposed to monitor boiler efficiency and NOx emissions. In this model, operational parameters were inputs, and its features were selected by kernel principal component analysis (KPCA). The relationships between the selected features and combustion products such as NOx emissions, unburned carbon and oxygen content in flue gas were mapped by -support vector regression ( -SVR), and then boiler efficiency was calculated by analytical model. The parameters of hybrid model were determined by grid search and 5-fold cross validation. The predicted results indicate that the presented hybrid model can monitor both efficiency and NOx emissions of coal-fired utility boiler, and the predicted performance of KPCA- -SVR model is more superior, comparing the other two models.

ANN - SQP Approach For NOx Emission Reduction In Coal Fired Boilers

Abstract In this research paper, predictive modelling of NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt were studied. The data collected from parametric field experiments were used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load condition and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in sequential quadratic programming (SQP) to find the optimum level of input operating conditions for low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.

ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler

An artificial neural network (ANN) and genetic algorithm (GA) approach to predict NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas, is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature, and nozzle tilt were studied. The data collected from parametric field experiments was used to build a feed-forward back-propagation neural net. The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load conditions and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in GA. The genetic search was used to find the optimum level of input operating conditions corresponding to low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.