Cameroon
Towards Global Cleaner Energy and Hydrogen Production: A Review and Application ORC Integrality with Multigeneration Systems
Apr 2022
Publication
The current evidential effect of carbon emissions has become a societal challenge and the need to transition to cleaner energy sources/technologies has attracted wide research attention. Technologies that utilize low-grade heat like the organic Rankine cycle (ORC) and Kalina cycle have been proposed as viable approaches for fossil reduction/carbon mitigation. The development of renewable energy-based multigeneration systems is another alternative solution to this global challenge. Hence it is important to monitor the development of multigeneration energy systems based on low-grade heat. In this study a review of the ORC’s application in multigeneration systems is presented to highlight the recent development in ORC integrality/application. Beyond this a new ORC-CPVT (concentrated photovoltaic/thermal) integrated multigeneration system is also modeled and analyzed using the thermodynamics approach. Since most CPVT systems integrate hot water production in the thermal stem the proposed multigeneration system is designed to utilize part of the thermal energy to generate electricity and hydrogen. Although the CPVT system can achieve high energetic and exergetic efficiencies while producing thermal energy and electricity these efficiencies are 47.9% and 37.88% respectively for the CPVT-ORC multigeneration configuration. However it is noteworthy that the electricity generation from the CPVT-ORC configuration in this study is increased by 16%. In addition the hot water cooling effect and hydrogen generated from the multigeneration system are 0.4363 L/s 161 kW and 1.515 L/s respectively. The environmental analysis of the system also shows that the carbon emissions reduction potential is enormous.
Green Electricity and Medical Electrolytic Oxygen from Solar Energy - A Sustainable Solution for Rural Hospitals.
Oct 2022
Publication
The objective of this paper is to design and simulate for rural areas isolated from the electricity grid a system based on solar energy for the optimal supply of green electricity and medical oxygen to a hospital. The system sized to produce 20 Nm3 /day is constituted of a 37.46 kW photovoltaic farm a 15.47 kW electrolyzer and a 15.47 kW fuel cell. The simulation of the Photovoltaic system is performed using the single diode model solved with the Lambert function defined in MATLAB Software. The daily production of oxygen and hydrogen during the sunniest day of the month is respectively 20.81 Nm3 /day and 41.61 Nm3 /day. The daily energy that can be stored is relevant to the hydrogen production and an electricity storage capacity of 124.89 kWh is feasible. During the least sunny day of the least sunny month the daily production of oxygen and hydrogen is respectively 7.72 Nm3 /day and 15.44 Nm3 /day. The recorded values prove that the system sized can constitute a viable solution to ensure the permanent supply a green electricity and oxygen to the hospital with good energy storage capacity.
Investigation of Hybrid Power-to-hydrogen/Nautral Gas and Hydrogen-to-X System in Cameroon
May 2024
Publication
In Sub-Saharan Africa (SSA) the capacity to generate energy faces significant hurdles. Despite efforts to integrate renewable energy sources and natural gas power plants into the energy portfolio the desired reduction in environmental impact and alleviation of energy poverty remain elusive. Hence exploring a spectrum of hybrid technologies encompassing storage and hydrogen-based solutions is imperative to optimize energy production while mitigating harmful emissions. To exemplify this necessity the 216 MW Kribi gas power plant in Cameroon is the case study. The primary aim is to investigate cutting-edge emissions and energy schemes within the SSA. This paper assessed the minimum complaint load technique and four power-to-fuel options from technical financial and environmental perspectives to assess the viability of a natural gas fuel system powered with hydrogen in a hybrid mode. The system generates hydrogen by using water electrolysis with photovoltaic electricity and gas power plant. This research also assesses process efficiency storage capacity annual costs carbon avoided costs and production prices for various fuels. Results showed that the LCOE from a photovoltaic solar plant is 0.19$/kWh with the Power-to-Hydrogen process (76.2% efficiency) being the most efficient followed by the ammonia and urea processes. The study gives a detailed examination of the hybrid hydrogen natural gas fuel system. According to the annual cost breakdown the primary costs are associated with the acquisition of electrical energy and electrolyser CAPEX and OPEX which account for 95% of total costs. Urea is the cheapest mass fuel. However it costs more in terms of energy. Hydrogen is the most cost-effective source of energy. In terms of energy storage and energy density by volume the methane resulted as the most suitable solution while the ammonia resulted as the best H2 storage medium in terms of kg of H2 per m3 of storage (108 kgH2/m3 ). By substituting the fuel system with 15% H2 the environmental effects are reduced by 1622 tons per year while carbon capture technology gathered 16664 tons of CO2 for methanation and urea operations yielding a total carbon averted cost of 21 $/ton.
Machine Learning Models for the Prediction of Turbulent Combustion Speed for Hydrogen-natural Gas Spark Ignition Engines
May 2024
Publication
The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result calculation time and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380 a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 % both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems firefighting systems simulation and prototyping tools.
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