Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer(CL)is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell(PEMFC).Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL.In this study,a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC.Moreover,a research framework combining the response surface method(RSM)and artificial neural network(ANN)model is proposed to conduct a quantitative analysis,and further a rapid and accurate prediction.With the help of this research framework,the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated.The results show that the mass of platinum,the mass of carbon,and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance.At the selected points,the sensitivity of the decision variables is ranked:volume fraction of dry ionomer>mass of platinum>mass of carbon>agglomerate radius.In particular,the sensitivity of the volume fraction of dry ionomer is over 50%at these points.Besides,the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set.This in-depth study is beneficial to provide feasible guidance for high-performance CL design.

Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO(partial swarm optimization)-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method. 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation.Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand,the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

Application-oriented hydrolysis reaction system of solid-state hydrogen storage materials for high energy density target:A review

Hydrogen storage and delivery technology is still a bottleneck in the hydrogen industry chain.Among all kinds of hydrogen storage methods,light-weight solid-state hydrogen storage(LSHS)materials could become promising due to its intrinsic high hydrogen capacity.Hydrolysis reaction of LSHS materials occurs at moderate conditions,indicating the potential for portable applications.At present,most of review work focuses on the improvement of material performance,especially the catalysts design.This part is important,but the others,such as operation modes,are also vital to to make full use of material potential in the practical applications.Different operation modes of hydrolysis reaction have an impact on hydrogen capacity to various degrees.For example,hydrolysis in solution would decrease the hydrogen capacity of hydrogen generator to a low value due to the excessive water participating in the reaction.Therefore,application-oriented operation modes could become a key problem for hydrolysis reaction of LSHS materials.In this paper,the operation modes of hydrolysis reaction and their practical applications are mainly reviewed.The implements of each operation mode are discussed and compared in detail to determine the suitable one for practical applications with the requirement of high energy density.The current challenges and future directions are also discussed.

Performance of CO_2 absorption in a diameter-varying spray tower

The application of spray towers for CO_2 capture is a development trend in recent years. However, most of the previous jobs were conducted in a cylindrical tower by using a single spray nozzle, whose configuration and performance is not good enough for industrial application. To solve this problem, the present work proposed a diameter-varying spray tower and a new spray mode of dual-nozzle opposed impinging spray to enhance the heat and mass transfer of CO_2 absorption process. Experiments were performed to investigate the mass transfer performance (in terms of the CO_ 2 removal rate (η) and the overall mass transfer coefficient (K_Ga_e)) of the improved spray tower under various operating conditions. Experimental results showed that the liquid to gas ratio and mole ratio of MEA to CO_2 are major factors, which affect the absorption performance and the maximums of η and (K_Ga_e that are 94.0% and 0.574 kmol·m^(-3)·h^(-1)·kP a^(-1), respectively, under the experimental conditions. Furthermore, new correlations to predict the mass transfer coefficient of the proposed spray tower are developed in various CO_2 concentrations with a Pearson Correlation Coefficient over 90%.

Characterizing the catalyst fluidization with field synergy to improve the amine absorption for CO2 capture

There are great interests to capture the CO2 to control the greenhouse gas emission.Amine absorption of CO2 is being taken as an effective way to capture CO2 in industry.However,the amine absorption of CO2 is cost-ineffective due to great energy consumption and solution consumption.In order to reduce the capture cost,catalyst fluidization is proposed here to intensify the mass transfer and heat transfer.Catalyst fluidization with field synergy and DFT model is developed by incorporating the effects of catalyst reaction kinetics,drag force and multi-field into the mass transfer,heat transfer,fluid flow and catalyst collision.Experiments with an improved distributor are performed well to validate the model.The reaction kinetics is determined by the DFT simulation and experiment.The mass transfer coefficient in the fluidized reactor is identified as 17%higher than the conventional packed reactor.With the field synergy of catalyst fluidization,the energy consumption for CO2 desorption is reduced by 9%.Stepwise operation and inclination reactor are used to improve catalyst fluidization process.

A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model

Gas load forecasting is important for the economic and reliable operation of the city gas transmission and distribution system.In this paper,a nonlinear autoregressive model(NARX)with exogenous inputs,support vector machine(SVM),Gaussian process regression(GPR)and ensemble tree model(ETREE)were used to predict and compare the gas load based on the gas load data in a certain region for past 3 years.The results showed that the prediction errors for most of days were higher than 10%.Further,simulation data were generated by considering the gas load variation trend,which was then combined with historical data to form the augmentation data set to train the model.The test results indicated that the prediction error of daily gas load in one year reduced to below 7%with a machine learning prediction method based on augmentation data.In addition,the model based on augmentation data set still performed better than original data in predicting the monthly gas load in last year as well as daily gas load in last month and week.Therefore,the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load.

Gas leakage recognition for CO2 geological sequestration based on the time series neural network

The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies.

Resource utilization of solid waste carbide slag:a brief review of application technologies in various scenes

China is the largest producer and consumer of calcium carbide in the world.The calcium carbide industry is an indispensable industry to support the basic life of people.The huge production capacity of calcium carbide is accompanied by a large number of solid waste carbide slag.Due to the immature treatment technology of carbide slag,a large number of carbide slag are stacked on-site,resulting in land occupation,air-drying,easy take-off ash,and pollution of the environment and water resources.In China,calcium carbide is mainly used to produce acetylene and further utilized,80%of which is used to produce polyvinyl chloride(PVC).A large amount of carbide slag is not used,while only a small part is used in the traditional building materials industry,flue gas desulfurization,sewage treatment,etc.,however,the economic benefits are poor.Therefore,converting the solid waste carbide slag produced by the calcium carbide industry into high value-added CaCO3,CaCl2,CaSO4 whiskers,etc.has become a potential way to expand the development field of the calcium carbide industry and is environmentally friendly.This paper focuses on summarizing the traditional and emerging high value-added utiliza-tion technologies of carbide slag,and then introduces the application research of carbide slag in carbon emission reduction.Finally,the defects of these technologies are summarized and further research directions are prospected.This study provides basic guidance for the diversified development of efficient resource utilization of carbide slag.Graphical abstract Diversified development of calcium carbide industry,resource utilization of solid waste carbide slag and its application of carbon emission reduction have been fully reviewed.

Recovery of waste heat in cement plants for the capture of CO2

Large amounts of energy are consumed during the manufacturing of cement especially during the calcination process which also emits large amounts of CO2. A large part of the energy used in the making of cement is released as waste heat. A process to capture CO2 by integrating the recovery and utilization of waste heat has been designed. Aspen Plus software was used to calculate the amount of waste heat and the efficiency of energy utilization. The data used in this study was based on a dry process cement plant with a 5-stage preheater and a precalciner with a cement output of 1 Mt/y. According to the calculations： 1） the generating capacity of the waste heat recovery system is 4.9MW. 2） The overall CO2 removal rate was as high as 78.5%. 3） The efficiency of energy utilization increased after the cement producing process was retrofitted with this integrated design.