scholarly journals Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1738
Author(s):  
Zhenhao Tang ◽  
Xiaoyan Wu ◽  
Shengxian Cao
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Working Conditions ◽  
Predictive Control ◽  
Combustion Process ◽  
Combustion Efficiency ◽  
Operating Conditions ◽  
Support Vector ◽  
Nox Emissions ◽  
Proposed Model

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

scholarly journals Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1036 ◽  
Author(s):  
Xinying Xu ◽  
Qi Chen ◽  
Mifeng Ren ◽  
Lan Cheng ◽  
Jun Xie
Keyword(s):  
Power Plant ◽  
Combustion Process ◽  
Optimization Method ◽  
Combustion Efficiency ◽  
Pollutant Emissions ◽  
Combustion Model ◽  
Nox Emissions ◽  
Coupling Characteristics ◽  
Learning Machine ◽  
Combustion Optimization

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

Combustion Characteristics of Small Size Gas Turbine Combustor Fueled by Biomass Gas Employing Flameless Combustion

2007 ◽  
Author(s):  
Masato Hiramatsu ◽  
Yoshifumi Nakashima ◽  
Sadamasa Adachi ◽  
Yudai Yamasaki ◽  
Shigehiko Kaneko
Keyword(s):  
Gas Turbine ◽  
Combustion Efficiency ◽  
Combustion Characteristics ◽  
Operating Conditions ◽  
Nox Emissions ◽  
Gas Turbine Combustor ◽  
Flameless Combustion ◽  
Engine Exhaust ◽  
Micro Gas Turbine

One approach to achieving 99% combustion efficiency (C.E.) and 10 ppmV or lower NOx (at 15%O2) in a micro gas turbine (MGT) combustor fueled by biomass gas at a variety of operating conditions is with the use of flameless combustion (FLC). This paper compares experimentally obtained results and CHEMKIN analysis conducted for the developed combustor. As a result, increase the number of stage of FLC combustion enlarges the MGT operation range with low-NOx emissions and high-C.E. The composition of fuel has a small effect on the characteristics of ignition in FLC. In addition, NOx in the engine exhaust is reduced by higher levels of CO2 in the fuel.

Effects of Cycle Operating Conditions on Combustor Performance

1997 ◽  
Vol 119 (1) ◽  
pp. 45-49 ◽  
Author(s):  
N. T. Davis ◽  
V. G. McDonell ◽  
G. S. Samuelsen
Keyword(s):  
Cost Function ◽  
Duty Cycle ◽  
Load Condition ◽  
Combustion Efficiency ◽  
Operating Conditions ◽  
Nox Emissions ◽  
Variable Geometry ◽  
Oxides Of Nitrogen ◽  
Mixing Processes ◽  
The Cost

To mitigate the environmental impact of next-generation gas turbine combustors, the emission performance at each condition throughout the load duty cycle must be optimized. Achieving this with a single combustor geometry may not be possible. Rather, the mixing processes and airflow splits must likely be modified as a function of load in order to (1) abate the emission of oxides of nitrogen, (2) maintain combustion efficiency, and (3) preclude lean blow-out over the entire duty cycle. The present study employs a model combustor to evaluate combustor performance as a function of load and explore the application of variable geometry to optimize performance at each condition. A parametric variation of flow splits is conducted at each load condition by independently adjusting the primary jet area and swirler choke area. The resultant impact on combustor performance is measured and quantified in terms of a cost function. The cost function is defined to increase with improving combustor performance (e.g., improving combustion efficiency and/or declining NOx emissions). Cycle operating conditions are found to alter the response mappings of efficiency and NOx. As a result, the optimal configuration of the combustor changes as the load is varied over the duty cycle. The results provide guidance on the application of active control.

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

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