Extreme Learning Machine And Vortex Search Algorithm And Their Application To Boiler Combustion Optimization

At present,coal-fired thermal power generation is still the main form of power generation in China.With the increasing energy crisis and the awareness of environmental protection,the realization of energy saving and environmental protection of coal-fired boilers in power plant is one of the urgent problems to be solved.In order to realize the optimal operation state of boiler combustion,one depends on accurate combustion characteristics model of a boiler,and the other depends on the efficient optimization technology.However,combustion process of a boiler involves many chemical reactions such as turbulence,heat and mass transfer.Furthermore,multiple operating parameters are highly coupled,so it is difficult to solve the problem of combustion optimization by the traditional mechanical modelling method and the classical optimization algorithm.Therefore,Extreme Learning Machine(ELM)and Vortex Search(VS)algorithm in computational intelligence technology are deeply studied.Their improvements are applied to establish combustion characteristic models and optimize adjustable operation parameters of a boiler.In this way,boiler combustion with high efficiency and low Nitrogen oxide emission can be realized.The research not only has a strong theoretical significance,but also has a very broad application prospect.The main research contents are as follows:Firstly,in order to solve the problems that the VS algorithm converges slowly and is easy to fall into local optimization,two improved VS algorithms are proposed.The first one is to use Logic self-mapping chaos to increase its local exploitation performance and Lévy flight strategy to increase its global exploration ability;the second one is to use Bloch spherical coordinates to improve the quality of initial candidate solutions and generalized opposition-based learning to accelerate the convergence speed.The test is carried out by using 13 well-known classical benchmark functions to verify the feasibility and effectiveness of the improved algorithms.Wilcoxon signed rank test is used to compare the VS algorithm and other intelligent optimization algorithms in statistical senseSecondly,in view of weak generalization ability of the ELM,according to the nonlinear degree of the input variables and output variables of the sample,a relaxation factor is reasonably added to the ELM,and an adaptive ELM(AELM)with the relaxation factor is proposed.The theoretical proof of convergence analysis is given.Then,11 standard regression data sets in UCI database are used to test and compare the generalization ability of AELM with other networks.Finally,considering that the online learning algorithm can reduce the complexity of space and time,and improve the real-time performance of the algorithm,the online learning algorithm of AELM is given by combining the idea of least squares and using MP generalized inverse of the matrix.Thirdly,since the input weights and thresholds of the nonlinear part of the ELM with parallel layer perception(PELM)are randomly generated,the generalization ability and stability of PELM are affected.A two-linear PELM(TPELM)is proposed by using the input samples and the minimum norm least square solution of the equations to generate the input weights and threshold of the lower network.Then,the validity of TPELM was verified by 11 standard regression data sets in UCI.Finally,an online TPELM learning algorithm based on the increment of input samples is presented.Fourthly,according to the historical combustion data of circulating fluidized bed boiler(CFBB),the batch offline models of Nitrogen oxide emission concentration and thermal efficiency of the boiler are established by using TPELM and AELM respectively.Through detailed comparison with PELM,ELM and other models,it is verified that the proposed models have better generalization performance and stability performance.In order to track the combustion dynamics of the boiler in realtime,and no longer repeat to learn the data that has been trained,the online models of NOx emission concentration and thermal efficiency are established using OAELM and OTPELM respectively.Finally,based on the established combustion characteristic model of CFBB,the improved VS algorithms are applied to offline and online optimizations of boiler thermal efficiency or Nitrogen oxide emission concentration to achieve the goals of increasing thermal efficiency or decreasing Nitrogen oxide emission concentration.Furthermore,by using the idea of Pareto dominance,the external repository is introduced into the first improved VS algorithm.Then,the algorithm is applied to simultaneous optimization of thermal efficiency and Nitrogen oxide emission concentration.The multi-group ratio schemes of the adjustable parameters are given,which can increase the thermal efficiency while decrease the nitrogen oxides concentration.