Elsevier

Chemical Engineering Journal

Volume 357, 1 February 2019, Pages 641-654
Chemical Engineering Journal

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

https://doi.org/10.1016/j.cej.2018.09.129Get rights and content

Highlights

  • Error backpropagation algorithm is used to built general mathematical model.

  • Computational model is established based on general mathematical model.

  • Computational model shows good reliability and predictive ability.

  • Relations between reaction parameters and performance are studied.

  • Optimum reaction parameters are obtained by using genetic algorithm.

Abstract

To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take platemicroreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The Emax of 5% for plate microreactor and Emax of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a feasible method for methanol steam reforming performance optimisation for hydrogen production.

Introduction

Compared with conventional reactor, owing to the characteristics of microchannel structure and small channel size, microreactor has the advantages, such as high surface-to-volume ratio, intensified heat and mass transfer, rapid and direct amplification, and high safety. Therefore, it has received considerable attention from researchers [1]. On the one hand, microreactor for hydrogen production has been received more attention because of its ability to provide reliable online hydrogen source for fuel cells [2]. On the other hand, methanol as fuel has the advantages, such as liquid, sulphur-free, low reforming temperature, high hydrogen content, cheap, easy storage and transportation, as well as renewable [3]. Hence, development of methanol microreactor for hydrogen production is an important direction in the research of mobile hydrogen source in vehicle [4], [5].

In the previous work, structure design, manufacturing method of microreactor for hydrogen production, and reaction support of microreactor for hydrogen production are mainly studied in the research of methanol steam reforming technology [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. In the structure design of the microreactor, microreactors such as a plate–fin microreactor, cube–post microreactor, annular microreactor, and cylindrical microreactor have been developed [6], [7], [8], [9], [10]. In the manufacturing method of a methanol microreactor, some technologies such as milling, special process, and microelectromechanical systems (MEMSs) have been used to manufacture straight channels, serpentine channels, spiral channels, etc. [11], [12], [13], [14]. In research on reaction support, porous metal materials used as the reaction support in microreactors have also been examined. Foam technology, solid-phase sintering technology, and liquid-phase sintering technology have been developed to fabricate the porous reaction support and have been successfully applied as catalyst support in ammonia decomposition and hydrogen production by methanol [15], [16], [17], [18].

Neural networks have strong feature extraction and abstraction capabilities, and they can integrate multisource information, process heterogeneous data, and capture change dynamics, thus playing an important role in parameter optimisation [19], [20]. To date, some scholars have used neural networks to optimise the reaction performance of microreactors. For example, Aghajani used an artificial neural network to research the size of synthesised nano-iodine in microreactors; it was found that the relationships between flow rate of solvent, flow rate of antisolvent, and size of the synthesised nano-iodine are in inverse relation [21]. Na researched the optimisation of catalyst loading in Fischer–Tropsch microchannel reactors, using the distribution of catalyst loading in microchannel reactors as a variable and considering C5+ productivity and temperature rise in microchannels as optimisation objects by using computational fluid dynamics, it was found that C5+ productivity was increased to 22% and ΔTmax was decreased to 63.2% by using a genetic algorithm (GA) [22]. Recently, Jung researched the structure optimisation of Fischer–Tropsch microchannel reactors, considering such structure parameters as the length, width, and height of microchannels in microreactor as variables, using reactor core volume and reaction temperature rise were used as optimisation objects by utilising the coupling method and artificial neural networks [23].

Although some research involving the design, manufacturing method, as well as the methanol steam reforming performance optimisation of the microreactor for hydrogen production, has been conducted, the study of the reaction parameters optimisation of methanol steam reforming for hydrogen production has not been reported. Here, in order to obtain the optimum reaction parameters of cylindrical microreactor for hydrogen production, a mathematical model for the methanol steam reforming performance of different microreactors for hydrogen production was created using the error backpropagation algorithm. The validity and universal applicability of the mathematical model for different microreactors were verified by experimental data from the plate and cylindrical microreactor. The predictive ability and reliability of the computational model for cylindrical microreactor were verified by experimental data. The relationships between reaction parameters and reaction results of methanol steam reforming were studied relying on the computational model for cylindrical microreactor. Subsequently, the optimum reaction parameters of cylindrical microreactor for hydrogen production were obtained using a GA, thereby optimising the reaction parameters.

Section snippets

Establishment of mathematical model for different microreactors

A mathematical model for methanol steam reforming performance for hydrogen production is established utilising an error backpropagation algorithm. Subsequently, the computational model for reaction performance of the specific testing system for hydrogen production is gained by training the mathematical model for different microreactors with several sets of experimental data from the specific testing system, combining with MATLAB neural network toolbox and the designed matrix laboratory (MATLAB)

Cylindrical microreactor and its testing system

Fig. 5 shows a cylindrical microreactor for methanol steam reforming for hydrogen production. The cylindrical microreactor mainly consisted of an evaporation chamber, a reforming chamber, heating cartridges, thermocouples, and reaction support consisting of three round foam metals. Methanol and water were evaporated into gas in an evaporation chamber. The foam metal was coated with a Cu-based catalyst [12]. Then, methanol and steam were reacted in three round foam metals to produce H2, CO and CO

Solution of the computational model for cylindrical microreactor

Table 3 shows input data, including PPI of foam metal, reaction temperature of methanol steam reforming for hydrogen production, injection velocity of methanol and water mixture, and catalyst loading of foam metal; output data include methanol conversion from 76 sets of experimental data of cylindrical microreactor for methanol steam reforming for hydrogen production. Appendix B shows the main MATLAB program designed to establish the computational model for methanol steam reforming performance

Effects of reaction parameters on reaction performance for cylindrical microreactor

The effects of the reaction parameters of methanol steam reforming for hydrogen production on the methanol conversion are studied relying on the computational model for cylindrical microreactor for methanol steam reforming for hydrogen production. The reaction parameters consist of the PPI of foam metal, reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and the catalyst loading of foam metal. Then, the mechanisms of

Optimisation of reaction parameters for cylindrical microreactor

The optimum reaction parameters for cylindrical microreactor are obtained by invoking the MATLAB neural network toolbox, combined with the GA and the designed MATLAB program, and relying on the computational model for cylindrical microreactor. The reliability of the optimum reaction parameters is validated by experiments.

Conclusions

Methanol steam reforming performance optimisation for hydrogen production was studied using an error backpropagation and a genetic algorithm (GA). The main conclusions can be drawn as follows:

  • (1)

    The established mathematical model for different microreactors had reliability and universal applicability, which could be applied in different methanol steam reforming microreactors for hydrogen production. When the mathematical model for different microreactors is applied in the specific testing system

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Project No. 51475397) and the Natural Science Foundation of Fujian Province of China (No. 2017J06015). In addition, the supports from the Fundamental Research Funds for Central Universities (Xiamen University, China) (Nos. 20720160079 and 2072062009) are also acknowledged.

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