Computational intelligence approach for modeling hydrogen production: a review

Figures & data

Figure 1. Hydrogen production method.

Table 1. Publications on CI techniques in field of hydrogen production between 2007 and 2017.

Row	Published Year	Author(s)	Objective
Artificial neural network
1	2017	(Jha, Kana, & Schmidt, 2017)	To optimize the hydrogen yield and Chemical Oxygen Demand removal efficiency in a UASB bioreactor
2	2016	(Hossain, Ayodele, Cheng, & Khan, 2016)	To estimate the produced hydrogen-rich syngas through methane dry reforming process over Ni/CaFe2O4 catalysts
3	2016	(Karaci, Caglar, Aydinli, & Pekol, 2016)	To model the thermochemical conversion process (i.e. hydrogen gas production from waste materials)
4	2014	(Whiteman & Kana, 2014)	To predict the bio-hydrogen production process
5	2014	(El-Shafie, 2014)	To estimate the bio-hydrogen yield
6	2010	(Rosales-Colunga, García, & Rodríguez, 2010)	To estimate the biohydrogen production during fermentative processes
ANFIS and other fuzzy methods
1	2017	(Shabanian, Edrisi, & Khoram, 2017)	To estimate the hydrogen production and to optimize the production process for reaching the maximum production yield and energy efficiency
2	2016	(Aghbashlo, Hosseinpour, Tabatabaei, Younesi, & Najafpour, 2016)	Exegetically optimization of the operational conditions of the photo-bioreactor for bio-hydrogen production during water gas shift (WGS) reaction using a multi-objective hybrid optimization technique
3	2014	(Woo, 2014)	To analyze the safety of nuclear power plants production of hydrogen
4	2011	(Chang, Hsu, & Chang, 2011)	To choose the most appropriate hydrogen production technology using an evaluating method: application in Taiwan
5	2012	(Huang et al., 2012)	To develop an online system for bio-hydrogen production through monitoring control approach.
6	2012	(Heo, Kim, & Cho, 2012)	To evaluate the hydrogen production using six alternative methods include: biomass gasification (BG), NPE, coal gasification (CG), steam methane reforming (SMR), wind electrolysis (WE), and by-product hydrogen
7	2014	(Thengane, Hoadley, Bhattacharya, Mitra, & Bandyopadhyay, 2014)	To compare different hydrogen production methods from view point of cost benefit approach
GA and related algorithms
1	2007	(Mu & Yu, 2007)	To model the steady-state performance of a GB hydrogen production through UASB reactor
2	2009	(Wang & Wan, 2009)	To increase the hydrogen production yield
3	2016	(Li & Lu, 2017)	To obtain the optimal value of the varying temperatures (i.e. room temperature (T1), hydrolysis temperature (T2) and oxygen decomposition temperature (T3)) in nuclear-based hydrogen Production process
4	2013	(Niknam, Bornapour, & Gheisari, 2013)	To consider the placement of Combined sources of Power, Heat, and Hydrogen fuel cell power plants without assuming the devices preparing cost
5	2015	(Bornapour & Hooshmand, 2015)	To plan the placement and the operation of MCFCPPs in distribution networks in case of using for CHPH production
6	2013	(Niknam, Bornapour, Ostadi, & Gheisari, 2013)	To optimize the planning of MCFCPPs for CHPH production
7	2012	(Niknam, Fard, & Baziar, 2012)	To evaluate the operation of hydrogen production, thermal load and electrical energy by FCPP
Support Vector Machine (SVM) method
1	2016	(Monroy, Guevara-López, & Buitrón, 2016)	To develop a model for evaluating and estimating BHP during photofermentation process

Table 2. Modeling characteristics.

No.	Methodology	Production Method	Modeling method/ classification method	Input(s) /objective function (s)	Output (s)/ Criteria	References
1	Using immobilized cell volume (ICV), Hydraulic Retention Time (HRT) and process temperature as input variables (independent variables) for developing the ANN and RSM	Anaerobic Sludge Blanket (UASB) bioreactor	ANN	Hydraulic retention times, immobilized cell volumes and temperatures	hydrogen yield and COD (Chemical Oxygen Demand) removal efficiency	(Jha et al., 2017)
2	Applied radial basis function (RBF) and multi layered perceptron (MLP) to predict the production process in presence of the catalyst metal loadings, feed ratio and the reaction temperature as inputs and H2 yield, CO yield, CH4 conversion and CO2 as output of process	Methane reforming	ANN	Feed ratio, Reaction Temperature and Metal loading	CH4 conversion, CO2 conversion, H2 yield and CO yield	(Hossain et al., 2016)
3	Pyrolysis process was modeled using MLP network using LM algorithm with 13 neurons on hidden layer by employing catalyst amount, type, biomass diversity, and temperature	pyrolysis of waste materials	ANN	catalyst amount, type, biomass diversity, and temperature	Hydrogen rich gas production yield	(Karaci et al., 2016)
4	concentration of molasses, pH, temperature and inoculum concentration were considered as input variables and hydrogen production yield was considered as output variable	Fermentation process	ANN	concentration of molasses, pH, temperature and inoculum concentration	Hydrogen production	(Whiteman & Kana, 2014)
5	Sixty data set were collected from batch type reactor. Reaction temperature, initial medium pH and the initial substrate were considered as input of ANN and the output was the hydrogen production yield.	Fermentation process	ANN	Reaction temperature, initial medium pH and the initial substrate	Hydrogen production yield	(El-Shafie, 2014)
6	A BPNN with 12 nodes in hidden layer with the conjugated gradient algorithm was employed to model the hydrogen production (as target parameter) using oxidation-reduction potential, pH dissolved CO2.	Fermentation process	BPNN	oxidation-reduction potential, pH dissolved CO2	Hydrogen production	(Rosales-Colunga et al., 2010)
7	In first stage, ANFIS method was employed to predict the hydrogen yield and conversion efficiency of butanol and jet fuel and ICA methodology was developed to optimize the above mentioned processes from view point of production yield and energy efficiency	noncatalytic filtration combustion to be used in fuel-reforming process	ANFIS	Inlet velocity and equivalence ratio	hydrogen yield and conversion efficiency of butanol and jet fuel	(Shabanian et al., 2017)
8	The objective function of study was developed by ANFIS, and then the NSGA was applied to find the highest energy efficiency and the lowest destructed energy	Photo bio reactor	NSGA with ANFIS	culture agitation speed and syngas flow rate	normalized energy destruction, rational energy efficiency, and process energy efficiency	(Aghbashlo et al., 2016)
9	The assessment of HTGC reactor was modeled using fuzzy algorithm for the stabilized hydrogen production using nuclear energy	Nuclear energy-based hydrogen production	Modified Fuzzy method	In five scenario for evaluation the system: Long-term cooling using conduction Shutting down the cooling system, Flow coast down and power equilibrium, Trip of pre-turbine, Re-criticality achievements,		(Woo, 2014)
10	Linguistic scores prepared by Delphi questionnaire, were used to collect the technology rating and weights. The prepared linguistic scores were fuzzificated, and the ideas of decision makers (on related weights and rates) was exported using fuzzy Delphi method (FDM). The developed model was employed to study hydrogen production based on different technologies.	NG reforming, NG reforming with CO2 storage, using net Electricity for producing Hydrogen, Using PV electricity for producing Hydrogen, using electrolysis by wind power for producing Hydrogen, using biomass gasification for producing Hydrogen, using fermentation process for producing Hydrogen	Fuzzy Delphi method	linguistic variables for Energy efficiency, CO2 emission, Fossil fuel consumption, Technological maturity, Technology development potential, Technological/industrial support, Feedstock, Capital, and Hydrogen production costs, Domestic market demand, Global market demand, Land use, Safeguard and social acceptance	Evaluating and presenting the best hydrogen production technology	(Chang et al., 2011)
11	pH and temperature were employed as fermentation environmental factors to control the feeding pump speed and heater performance fuzzy control was employed in lab-view software to develop a real time controller on hydrogen production process	Fermentation	Fuzzy controller for controlling the production process	PH and temperature	feeding pump operating speed and heater status/ Hydrogen production	(Huang et al., 2012)
12	The fuzzy AHP method were employed to evaluate the studied system based on Twelve factors and their weights include: Ripple effect to other industries, Market size, Environmental contribution, Human resource development, Operation, Equipment, Infrastructure, and Distribution costs, Technological Reliability, dependency, acceptability and Stability.	SMR, gasification of biomass and coal, NPE and wind electrolysis, and by-product of hydrogen	Fuzzy AHP	Risks, Opportunities, Benefits, Costs, and Concepts	Evaluating and presenting the best hydrogen production technology	(Heo et al., 2012)
13	AHP and Fuzzy-AHP methods were employed to evaluate eight hydrogen production technologies including: partial biomass and coal gasification, SMR, oxidation of hydrocarbons, hydro-based, wind based and photovoltaic-based electrolysis, water splitting by chemical looping.	partial biomass and coal gasification, SMR, oxidation of hydrocarbons, hydro-based, wind based and photovoltaic-based electrolysis, water splitting by chemical looping	CBA using AHP and Fuzzy-AHP approaches	Common inputs of each method	Hydrogen production, utilities and raw material consumption greenhouse gas emissions, energy scalability and efficiency	(Thengane et al., 2014)
14	Applied ANN and GA by the input parameters of OLR, HRT, and IBA and the output parameters of H2 production rate, concentration, and yield, effluent total organic carbon and effluent aqueous products, separately	ASBR	ANN and GA	OLR, HRT, and IBA	H2 production rate and yield, H2 concentration in the biogas, and TOC, acetate, vale rate, propionate, butyrate, capo rate in the reactor effluent	(Mu & Yu, 2007)
15	Developed the neural network-based GA and RSM based on, initial PH, temperature and glucose concentration as the input variables and the hydrogen production as the target value.	Fermentation process	ANN-GA	initial PH, temperature and glucose concentration	Hydrogen yield	(Wang & Wan, 2009)
16	GA-MCS approach is employed to find the optimal temperature ranges	nuclear-based through Cu-Cl cycle	Genetic Algorithm and Monte Carlo Simulation	room temperature, hydrolysis temperature and oxygen decomposition temperature	Hydrogen production	(Li & Lu, 2017)
17	Employed the h-Self Adaptive Gravitational Search algorithm to achieve the optimal locating for FCPPs and daily optimal active powers.	Fuel Cell Power Plants (FCPPs)	Θ-SAGSA	Cost, emission and voltage deviation	heat produced by FCPP in bus I, bus during time t, equivalent electrical energy, equivalent electrical energy of saved, the maximum power of FCPP and equivalent electric power for hydrogen production during time t	(Niknam, Bornapour, & Gheisari, 2013)
18	The study scenarios were defined and the RWM-PDF was employed to generate scenarios based on input random variables. This approach helped define the problem as a cost-function. FFA and POM were employed to reduce the costfunction.	MCFCPP	FFA	Cost, emission and voltage deviation	Achieving the best response for developed scenarios	(Bornapour & Hooshmand, 2015)
19	The objective function included: electrical energy production cost, thermal energy, and hydrogen production, and voltage deviation The optimal planning for production of heat power and hydrogen with nonlinear nature was considered as study problem, accordingly a SLA-BIA was employed for solving the mentioned issue	MCFCPP	SLA-BIA	Several scenarios for management of thermal energy and hydrogen production, total emission of MCFCPPs and achieving the best optimal set		(Niknam, Bornapour, Ostadi, et al., 2013)
20	TLA to take the optimal operation management of PEM-FCPPs and the optimal configuration of the system	FCPP	TLA	scenarios of PEM-FCPPs managements		(Niknam et al., 2012)
21	Using experimental photo-fermentations to obtain Kinetic parameters experimentally determining the Optimal conditions for light intensities, pH and Temperature, as state variables, as variables in the mechanistic model - employing MPCA technique and a classification stage using SVM	Photo fermentation process	Mathematical modeling/Support Vector Machine	I, pH and Temperature	Hydrogen production	(Monroy et al., 2016)
22	Employing a method for fault diagnosis and propagation analysis using ANN and GP to uncover the interactions between hydrogen production process variables.	Cu–Cl thermochemical cycle	FSN	A/C ratio Feed D flow rate Separator liquid flow	Explosion	(Gabbar et al., 2014)

Table 3. Model evaluation criteria

Accuracy and Performance Index	Description
MSE=	P, the number of data set patterns N, the number of output units Tij and Lij are target and output values
RMSE=	P, the number of data set patterns N, the number of output units Tij and Lij are target and output values
MAE=	P, the number of data set patterns N, the number of output units Tij and Lij are target and output values
MAPE=	P, the number of data set patterns N, the number of output units Tij and Lij are target and output values
r=	PMi is the target power Pp; is the output power PM is the average power n is the number of the samples;

Figure 2. Distribution of CI methods in hydrogen production.

Table 4. Total results of the presented studies.

Paper. No.	Results and evaluation	Paper. No.	Results and evaluation
1	Based on the results, ANN presented the highest accuracy and reliability for modeling and optimization the studied parameters compared to RSM method. Accordingly, a higher molar bio hydrogen yield (0.90 mol-H2/mol glucose) and COD removal efficiency (84.81%) in the UASB system was obtained, optimized by the ANN model.	12	Based on the results, the most important indexes were able to achieve economic feasibility and lowering risks. SMR was selected the appropriate hydrogen production method.
2	MLP method by presenting the highest R^2 value for the simulated output of the selected variable as the best predictor compared to the RBF method	13	Based on results (from view point of cost-benefit analysis), the splitting of water by CLT had the most acceptable results compared to the renewable approaches.
3	Based on the results the developed network was able to estimate the hydrogen rich gas ratio with little error.	14	Based on the results, the developed model was able to estimate the daily variation of the performance of UASB reactor, and could predict the reactor performance at various HRTs
4	RSM and ANN models resulted the R^2 values of 0.75 and 0.91, respectively. Based on the results in the validation process, the prediction errors of 15.12 and 119.08% prediction were calculated for the ANN and the RSM based on the volume of hydrogen.	15	Based on the results, the performance of the ANN-based GA was much better than that for RSM, and this concluded that the ANN model had a much higher predicting accuracy compared to that of the RSM model
5	Based on the results, the developed ANN model provided significantly high predicting performance	16	Based on the results GA-based MCS method could be applied to find the optimal temperature ranges such that this method found temperature ranges of [25.705, 25.809], [433.595, 434.245] and [521.645, 528.415] for T1, T2 and T3, respectively.
6	Based on the results, the BPNN with correlation coefficient of 0.955 between the target and the output values successfully estimated the hydrogen production with a potential to be used in other hydrogen production systems	17	Based on the simulation results, a satisfactory performance of the developed model was found, being an effective algorithm with a shorter convergence time.
7	Based on the prediction results, the R^2 value of ANFIS model was calculated to be about 0.998, 0.998, 0.999 and 0.999, respectively for estimating the hydrogen yield and conversion efficiency of jet fuel and butanol.	18	The developed method led to a reduced voltage deviation, cost and emission. On the other hand, based on the simulation results, the algorithm had a significant advantage compared to the other comparative methods.
8	The culture agitation speed of 383.33 rpm and the syngas flow rate of 13.34 ml/min provided the optimal condition by rational exergy efficiency of 85.64%, yielding process exergy efficiency of 21.66% and exergy destruction of 1.55	19	Based on the results, the developed algorithm decreased the convergence time for optimally planning the location and operation. The simulation results validated the performance of the proposed approach.
9	Based on results, a frequency of 8% was obtained for successful long-term cooling by conduction.	20	The simulation results demonstrated satisfactory performance of the developed method, with a potential for greater efficiency in the thermal energy and hydrogen production process.
10	Based on the results, we aver that the use of wind power and photovoltaic electricity were the two appropriate approaches used for hydrogen production in Taiwan	21	Based on the results, both data-driven models had strong outputs for further usage.
11	Based on the results, the use of the fuzzy controller acted to reduce the energy consumption in hydrogen production process and provided an appropriate condition for growth of microorganisms by providing the best environmental condition maximum production in the case of using fuzzy controller was 13.44 L/Day

Table 5. The values of the model evaluating factors.

Paper No.	method	Efficiency factor	Value
1	ANN-MLP	R^2	0.99 for hydrogen production yield
	RSM	R^2	0.90 for hydrogen production yield
2	ANN-MLP	R^2	0.9726, 0.8597, 0.9638 and 0.9394 for H2 yield, CO yield, CH4 conversion
	ANN-RBF	R^2	0.9218, 0.7759, 0.8307 and 0.7425 for H2 yield, CO yield, CH4 conversion
3	ANN-MLP	r	0.955 for hydrogen production yield
		MSE	6.97 for hydrogen production yield
4	ANN-MLP	R^2	0.91 for hydrogen production yield
	RSM	R^2	0.75 for hydrogen production yield
5	ANN-MLP	R^2	0.98 for hydrogen production yield
6	BP ANN	r	0.955 for hydrogen production yield
7	ANFIS	R^2	0.998, 0.998, 0.999 and 0.999, respectively for hydrogen yield and conversion efficiency of jet fuel and butanol.
8	ANFIS	R^2	0.9, 0.97 and 0.92 for normalized exergy destruction, rational exergy, and process exergy efficiencies
		MAPE	0.019, 0.0029, 0.0228 for normalized exergy destruction, rational exergy, and process exergy efficiencies
		MSE	1.15, 0.245 and 1.39 for normalized exergy destruction, rational exergy, and process exergy efficiencies
9	Modified fuzzy algorithm	frequency	8% of successful long-term conduction cooling
12	Fuzzy AHP	Overall weights of each production method based on risk, cost and opportunities	0.529, 0.46, 0.449, 0.331, 0.395 and 0.366 for SMR, CGP, BG, WE, NPE, and hydrogen yield, respectively.
13	Fuzzy AHP	Final score of each production method based on GHG emission, raw material, energy efficiency, scalability, wastes and emission	0.1945, 0.1648, 0.1474, 0.1385, 0.1151, 0.0908, 0.0863 and 0.0627 for WS-CL, H-EL, SMR, W-EL, POX, PV-EL, CG and BG, respectively.
14	GA-NN	R^2 for Training	0.966, 0.81, 0.882 and 0.92 for H2 concentration in the biogas, production rate, yield, and effluent TOC.
		R^2 for validation	0.719, 0.806, 0.843 and 0.854 for H2 concentration in the biogas, production rate, yield, and effluent TOC.
15	ANN-MLP	RMSE, and SEP	16.6% and 38.4%, respectively for RMSE and SEP for hydrogen production
	RSM	RMSE, and SEP	7.7% and 17.8% respectively for RMSE and SEP for hydrogen production
	GA-ANN	Maximum hydrogen production	360.5 ml/g of glucose
	GA-RSM	Maximum hydrogen production	289.8 ml/g of glucose

Figure 3. Comparing of prediction models in terms of R².

Jha, P., Kana, E., & Schmidt, S. (2017). Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor? International Journal of Hydrogen Energy, 42(30), 18875–18883. doi: 10.1016/j.ijhydene.2017.06.063

 Web of Science ®Google Scholar

Hossain, M. A., Ayodele, B. V., Cheng, C. K., & Khan, M. R. (2016). Artificial neural network modeling of hydrogen-rich syngas production from methane dry reforming over novel Ni/CaFe 2 O 4 catalysts. International Journal of Hydrogen Energy, 41(26), 11119–11130. doi: 10.1016/j.ijhydene.2016.04.034

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Karaci, A., Caglar, A., Aydinli, B., & Pekol, S. (2016). The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN). International Journal of Hydrogen Energy, 41(8), 4570–4578. doi: 10.1016/j.ijhydene.2016.01.094

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Whiteman, J., & Kana, E. G. (2014). Comparative assessment of the artificial neural network and response surface modelling efficiencies for biohydrogen production on sugar cane molasses. BioEnergy Research, 7(1), 295–305. doi: 10.1007/s12155-013-9375-7

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El-Shafie, A. (2014). Neural network nonlinear modeling for hydrogen production using anaerobic fermentation. Neural Computing and Applications, 24(3-4), 539–547. doi: 10.1007/s00521-012-1268-8

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Rosales-Colunga, L. M., García, R. G., & Rodríguez, A. D. L. (2010). Estimation of hydrogen production in genetically modified E. Coli fermentations using an artificial neural network. International Journal of Hydrogen Energy, 35(24), 13186–13192. doi: 10.1016/j.ijhydene.2010.08.137

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Shabanian, S. R., Edrisi, S., & Khoram, F. V. (2017). Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels. Korean Journal of Chemical Engineering, 34(8), 2188–2197. doi: 10.1007/s11814-017-0134-x

 Web of Science ®Google Scholar

Aghbashlo, M., Hosseinpour, S., Tabatabaei, M., Younesi, H., & Najafpour, G. (2016). On the exergetic optimization of continuous photobiological hydrogen production using hybrid ANFIS–NSGA-II (adaptive neuro-fuzzy inference system–non-dominated sorting genetic algorithm-II). Energy, 96, 507–520. doi: 10.1016/j.energy.2015.12.084

 Web of Science ®Google Scholar

Woo, T. H. (2014). Modified fuzzy algorithm based safety analysis of nuclear energy for sustainable hydrogen production in climate change prevention. International Journal of Electrical Power & Energy Systems, 61, 192–196. doi: 10.1016/j.ijepes.2014.03.035

 Web of Science ®Google Scholar

Chang, P.-L., Hsu, C.-W., & Chang, P.-C. (2011). Fuzzy Delphi method for evaluating hydrogen production technologies. International Journal of Hydrogen Energy, 36(21), 14172–14179. doi: 10.1016/j.ijhydene.2011.05.045

 Web of Science ®Google Scholar

Huang, S.-R., Chen, H.-T., Chung, C.-H., Wu, C.-C., Tsai, T.-Y., Chu, C.-Y., & Lin, C.-Y. (2012). Fermentative hydrogen production using a real-time fuzzy controller. International Journal of Hydrogen Energy, 37(20), 15575–15581. doi: 10.1016/j.ijhydene.2012.02.078

 Web of Science ®Google Scholar

Heo, E., Kim, J., & Cho, S. (2012). Selecting hydrogen production methods using fuzzy analytic hierarchy process with opportunities, costs, and risks. International Journal of Hydrogen Energy, 37(23), 17655–17662. doi: 10.1016/j.ijhydene.2012.09.055

 Web of Science ®Google Scholar

Thengane, S. K., Hoadley, A., Bhattacharya, S., Mitra, S., & Bandyopadhyay, S. (2014). Cost-benefit analysis of different hydrogen production technologies using AHP and fuzzy AHP. International Journal of Hydrogen Energy, 39(28), 15293–15306. doi: 10.1016/j.ijhydene.2014.07.107

 Web of Science ®Google Scholar

Mu, Y., & Yu, H.-Q. (2007). Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm. International Journal of Hydrogen Energy, 32(15), 3308–3314. doi: 10.1016/j.ijhydene.2007.05.021

 Web of Science ®Google Scholar

Wang, J., & Wan, W. (2009). Optimization of fermentative hydrogen production process using genetic algorithm based on neural network and response surface methodology. International Journal of Hydrogen Energy, 34(1), 255–261. doi: 10.1016/j.ijhydene.2008.10.010

 Web of Science ®Google Scholar

Li, B., & Lu, L. (2017). Genetic-algorithm-based fault detection of the heat transfer process in nuclear-based hydrogen production based ON Cu–Cl cycle. International Journal of Hydrogen Energy, 42(6), 3863–3875. doi: 10.1016/j.ijhydene.2016.06.176

 Web of Science ®Google Scholar

Niknam, T., Bornapour, M., & Gheisari, A. (2013). Combined heat, power and hydrogen production optimal planning of fuel cell power plants in distribution networks. Energy Conversion and Management, 66, 11–25. doi: 10.1016/j.enconman.2012.08.016

 Web of Science ®Google Scholar

Bornapour, M., & Hooshmand, R.-A. (2015). An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants. Energy, 83, 734–748. doi: 10.1016/j.energy.2015.02.082

 Web of Science ®Google Scholar

Niknam, T., Bornapour, M., Ostadi, A., & Gheisari, A. (2013). Optimal planning of molten carbonate fuel cell power plants at distribution networks considering combined heat, power and hydrogen production. Journal of Power Sources, 239, 513–526. doi: 10.1016/j.jpowsour.2013.03.117

 Web of Science ®Google Scholar

Niknam, T., Fard, A. K., & Baziar, A. (2012). Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants. Energy, 42(1), 563–573. doi: 10.1016/j.energy.2012.02.023

 Web of Science ®Google Scholar

Monroy, I., Guevara-López, E., & Buitrón, G. (2016). A mechanistic model supported by data-based classification models for batch hydrogen production with an immobilized photo-bacteria consortium. International Journal of Hydrogen Energy, 41(48), 22802–22811. doi: 10.1016/j.ijhydene.2016.10.100

 Web of Science ®Google Scholar

Gabbar, H. A., Hussain, S., & Hosseini, A. H. (2014). Simulation-based fault propagation analysis—application on hydrogen production plant. Process Safety and Environmental Protection, 92(6), 723–731. doi: 10.1016/j.psep.2013.12.006

 Web of Science ®Google Scholar