Renewable Energy Global Innovations features: 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 gasifiers. Energy Conversion and Management, 171, 1651-1661.

 

Go To Energy Conversion and Management 

The post Bio-Inspired Modeling for H2 Production appeared first on Renewable Energy Global Innovations.

Read more research excellence studies on: Renewable Energy Global Innovations (https://ift.tt/21cCPA4)

Renewable Energy Global Innovations features: Spatial complementarity in time between energy resources

Significance Statement

Hybrid energy systems have been seen as a promising alternative solution for a sustainable future. Their use has increased rapidly over the past few years thus promoting their research relating to design and optimization. Despite their high initial costs as compared to one renewable energy resource, recent growth in the demand for energy conversion devices have seen the cost reduction. Today, technology managers require efficient tools to help them in making decisions on new plants and investments in renewable resources thereby leading to better use of the available energy resources. As such, complementarity concept has been identified as a promising solution.

In renewable energy resources field, complementarity can be used as a design parameter, planning, and management tool. Generally, a complete complementarity needs to consider time, energy and amplitude of variation. Time-complementarity is completed when the availability minima takes place between six-month, energy-complementarity requires equal mean values of the compared energy resources while amplitude-complementarity only takes place when the difference in the maximum and minimum energies are equal for the compared resources. Consequently, complementarity can be accessed depending on the nature of the study that is, between energy resources in one or different places. Owing to various difficulties that have been experienced in the past researches such as technical challenges and result presentation, there has been a great need for more effective approaches for determining complementarities between the energy resources.

Dr. Alfonso Risso, Professor Alexandre Beluco and Professor Rita de Cássia Marques Alves at Universidade Federal do Rio Grande do Sul (UFRGS) in Brazil proposed a new method for obtaining spatial complementarity in time and how it can be expressed through maps. They established a hexagonal network of cells and determined complementary roses for each of them. In this case, the petal lengths represented the distance between the cells while the color represented the complementarity of the cells. The authors purposed to use the method in determining the spatial complementarity in time between some wind farms and hydroelectric power plants along a certain territory and present the obtained map of the complementarity in time. Their work is currently published in the research journal, Energies.

The authors successfully applied the newly developed approach in determining the spatial complementarity in time between initially identified wind farms and power plants along the State of Rio Grande territory. They also presented it on a map hence enhancing the effectiveness of the method.

By expressing the spatial complementarity in time through a map, the researchers brought on board a better use of the complementarity method as a key tool in design, optimizing and management of renewable energy resources. Thus, they overcame some of the previously faced challenges to enhance the efficiency of the complementarity concept among the technology and energy managers. Furthermore, the information on the complementarity map is reliable since the data used to obtain the complementarity of the roses are accurate. Therefore, it can be extended to determine energy-complementarity and amplitude- complementarity. The study will, therefore, advance the renewable energy resources sector for the realization of a sustainable future.

Journal Reference

Risso, A., Beluco, A., & Marques Alves, R. (2018). Complementarity Roses Evaluating Spatial Complementarity in Time between Energy Resources. Energies, 11(7), 1918.

Go To Energies 

 

The post Spatial complementarity in time between energy resources appeared first on Renewable Energy Global Innovations.

Read more research excellence studies on: Renewable Energy Global Innovations (https://ift.tt/21cCPA4)