Bio-Inspired Modeling for H2 Production

Significance Statement

The environmental problems, climate change and need to reduce greenhouse gases emission has compelled energy users to sort for alternative renewable and clean energy sources for a sustainable future. Researchers have identified hydrogen as a promising solution since it is a high-quality, clean and renewable energy source. Unfortunately, being a non-primary source, hydrogen can only be generated from other sources of energy such as fossil fuels, natural gas reforming, and coal gasification. However, such hydrogen production methods have failed to meet the requisite low carbon dioxide emission for sustainable development, therefore, researchers have been looking for alternative production techniques.

Presently, generating hydrogen from biomass has taken significant interest amongst researchers due to its renewability and zero carbon emission. To enhance hydrogen production process in large-scale experiments, the effects of a various operating condition like sorbent to biomass ratio, pressure, temperatures, and steam to biomass ratio have been investigated. Moreover, most of these methods have not been fully explored due to their expensive nature, time-consuming and less effective data mining techniques. The use of mathematical modeling techniques comprising computer-based models has henceforth been employed in the investigation of hydrogen production from biomass gasification. Despite the reported improvements, computer-based models are time-consuming, involve complicated algorithms and complex differential equations which may require assumption hence leading to inaccurate findings.

Recently, Dr. Jaroslaw Krzywanski at Jan Dlugosz University in Poland in collaboration with Chinese scientists at Zhejiang University proposed artificial intelligence (AI) methods that included artificial neural networks (ANN) and genetic algorithms (GA) as a simpler alternative method for data acquisition and analysis for hydrogen production via biomass steam gasification with CaO enhancement. They estimated hydrogen concentration in the syngas produced from biomass in circulating fluidized bed (CFB) and bubbling fluidized bed (FB). Also, they investigated the conditions and influencing parameters on hydrogen gas production. Eventually, they compared the experimental results and the simulation results. The work is published in the journal, Energy Conversion and Management.

The authors observed that desirably adjusting reaction temperature, CaO to carbon mole ratio and H2O to carbon mole ratio can result in a high hydrogen concentration in the syngas produced. Also, they noted that CFB produced high hydrogen concentration as compared to FB gasifiers. Furthermore, the similarity in the simulation and experimental results confirmed the efficiency of the proposed AI model. For instance, a maximum relative error less than ±8 was obtained between the calculated and measured data.

The developed non-iterative model enabled effective optimization of the hydrogen gas production process where the process parameters are generated from a given set of input data. In addition to the ability of the ANN to reproduce the whole process, the proposed AI approaches, therefore, overcomes the various limitation of the experimental procedures and programmed computing approaches. Consequently, owing to the simplicity of the model for handling data and experimental procedures, it can as well be used in hydrogen production for predicting its concentration in syngas from biomass via CaO sorption. This is possible for both CFB and FB gasifiers. The study will therefore advance hydrogen gas production for the realization of a sustainable development.



About the author

Abdul Rahim Shaikh is PhD candidate at the key laboratory of Clean Energy Utilization of Energy Engineering Department of Zhejiang University Hangzhou China. His work mainly focuses on Chemical Looping Gasification where he deals with the effect of natural and modified sorbents on coal, biomass and biomass/coal blends and also plant simulations on Aspen plus and Aspen Hysys. His favorite pass time is dismantling stuff in his home workshop and keeping up to date on current affairs.

About the author

Hongtao Fan is a doctoral research student in State Key Laboratory of Clean Energy Utilization at Zhejiang University in the City of Hangzhou. His research focuses on the research and development of biomass calcium based chemical looping gasification technology, including experimental researches on dual fluidized bed gasification with sorbent enhancement and regeneration, sorbent cyclic capacity maintenance, process numerical simulation on CLG process and hydrogen plant system modeling.

About the author

Yi Feng, is studying for a doctorate in the national key laboratory of clean energy utilization of the institute of sustainable energy in Zhejiang University.

My working field is chemical looping gasification of lignite and have completed the related experimental researches of cal-based chemical looping gaisification using lignite and biomass as fuel within two pressurized fluidized bed at atmospheric pressure under various operation parameters, such as temperature (650-750℃), water/carbon molar ratio (1-2), Cal/carbon molar ratio (0-2), during the master stage. At the same time, I have participated in the declaration and research of coal/biomass pressurized oxygen-enriched combustion mechanism (national natural science foundation of China). i have also participated in the fourth international chemical looping conference and the fluidization conference in china, and the reports were given at the conference.

About the author

Jaroslaw Krzywanski is an Associate Professor at the Faculty of Mathematics and Natural Science at Jan Dlugosz University in Czestochowa, Poland.
He received the M.Sc. degree from Czestochowa University of Technology, Department of Mechanical Engineering and Computer Sciences, Institute of Thermal Machinery, Poland and Ph.D. degree from Silesian University of Technology, Faculty of Energy and Environmental Engineering, Poland.

He has published more than 140 refereed works, including papers, two monographs, conference proceedings and serves as an editorial board member of several international journals.

He is interested in modeling of energy devices and processes, including solid fuels combustion, gas emissions and hydrogen production from biomass combustion and gasification. He uses both programmed and artificial intelligence (AI), bio-inspired methods to predict e.g. heat transfer and pollutants emissions from coal and biomass combustion and co-combustion in large- and pilot-scale circulating fluidized bed (CFB) boilers, chemical looping combustion (CLC) and calcium looping combustion (CaL) in fluidized bed (FB) systems, performance of adsorption chillers, as well as the hydrogen concentration in syngas during the H2 production via CaO sorption enhanced anaerobic gasification of sawdust in FB units.

Journal Reference

Krzywanski, J., Fan, H., Feng, Y., Shaikh, A., Fang, M., & Wang, Q. (2018). Genetic algorithms and neural networks in optimization of sorbent enhanced H 2 production in FB and CFB gasifiersEnergy Conversion and Management171, 1651-1661.


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