Intelligent prediction on air intake flow of spark ignition engine by a chaos radial basis function neural network

To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.The data of air intake flow is proved to be multidimensionally nonlinear and chaotic.The RBF neural network is used to train the reconstructed phase space of the data.The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer.The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model.The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48,respectively.

Recent advances in application of moving bed bioreactors for wastewater treatment from recirculating aquaculture systems: A review

In recent years,government investments in implementing restrictive public policies on the treatment and discharge of effluents from the aquaculture industry have increased.Hence,efficient and cleaner methods for aquaculture production are needed.Recirculating aquaculture systems(RAS)offers water conservation by recycling the treated aquaculture water for reuse.RAS wastewater treatment using a moving bed bioreactors(MBBRs)process has been considered well suited for maintaining good water quality,thereby making fish farming more sustainable.Currently,improvements were achieved in tackling the influence of salinity,organic matter,disinfectant,and bioreactor start-up process on the MBBR performance efficiency.This review highlights an updated overview of recent development made using MBBR to treat the residual water from RAS.Precisely,nitrification and simultaneous nitrification-denitrification(SND),and other hybrid processes for nitrogen removal were elucidated.Finally,future challenges and prospects of MBBRs in RAS facilities that need to be considered were also proposed.

Numerical Study on Effects of Key Factors on Performance of Ce02-based Catalyzed Diesel Particulate Filter

Catalyzed diesel particulate filter(CDPF)combines the functions of the oxidization catalyst and the diesel particulate filter.Due to good redox capacity and oxygen storage capacity,CeO2 is used as the catalyst of CDPF.Since the effects of key factors on the performance of Ce02-based CDPF were rarely reported,it was performed in this study based on a zero-dimensional numerical model using plug flow reactor in which a reaction mechanism was established and validated by the experiment of the thermal gravimetric analyzer.The effects of exhaust gas temperature and three defined parameters including the ratios of NO2 in NO,(α),NOx to soot(β),and catalyst coated amount to carbon loading amount(y)on catalyst poisoning temperature,N20 concentration,NOx reduction rate and soot regeneration rate were investigated.The results show that the rising exhaust gas temperature causes the reduction of NOx concentration,and the NOx reduction rate comes to 66%when the catalyst poisoning temperature is reached.The soot regeneration rate and the N2O concentration first increase and then decrease as the exhaust gas temperature increases.Meanwhile,the higher exhaust gas temperature suppresses the production of N2O,but raises the possibility of catalyst poisoning.The increasing a and p result in the increase of soot regeneration rate and the decrease of NOx reduction rate.The catalyst poisoning temperature is improved at higher a and lower p.The soot regeneration rate has a fast increase with y at first and then stabilizes rapidly.The results of this study are valuable to optimize the operation of CDPF.