Refine
Document Type
Way of publication
- Open Access (12)
Keywords
- CO2 methanation (7)
- Biogas upgrading (3)
- Biomethane production (3)
- Waste valorization (3)
- CO2 waste valorization (2)
- CeO2 (2)
- Cu catalysts (2)
- Green energy (2)
- Mixed-integer linear programming (2)
- Ni catalysts (2)
Institute
BTU
Current industrial trends promote reduction of material and energy consumption of fossil fuel burning, and energy-intensive process equipment. It is estimated that approximately 75% of the energy consumption in hydrocarbon processing facilities is used by such equipment as fired heater, hence even small improvements in the energy conservation may lead to significant savings [1, 2]. In this work, a mathematical modelling and optimisation study is undertaken using gPROMS® ProcessBuilder® to determine the optimal operating conditions of an existing API 560 Type-E vertical-cylindrical type natural draft fired heater, in operation at the Atuabo Gas Processing Plant (GPP), in the Western Region of Ghana. It is demonstrated that the optimisation results in significant reduction of fuel gas consumption and operational costs.
Photovoltaic/thermal systems are a novel renewable energy approach to transform incident radiation into electricity and simultaneously store the excess thermal energy produced. Both sensible and latent heat storage materials have been investigated in the past for thermal storage; with desert sand having been recently considered as an efficient and inexpensive alternative. In this work, we use a transient Computational Fluid Dynamics simulation to compare the performance of desert sand to that of well-established phase-change materials used in photovoltaic/thermal systems. The simulation gives as a result the temperature profiles within the device as well as the time evolution of the charge/discharge cycles when using PCMs. The results show the suitability of desert sand as a thermal storage material to be used in photovoltaic/thermal systems.
This work investigates the use of variable pricing to control electricity imported and exported to and from both fixed and unfixed distributed energy resource network designs within the UK residential sector. It was proven that networks which utilise much of their own energy and import little from the national grid are barely affected by variable import pricing, but are encouraged to export more energy to the grid by dynamic export pricing. Dynamic import and export pricing increased CO2 emissions due to feed-in tariffs which encourages CHP generation over lower-carbon technologies such as solar panels or wind turbines.
Food and beverages industry is facing major challenges in the years to come, as the world population is expected to grow, accompanied by a growing need for energy, feed and fuel. Much of the processing in the food industry is performed in small-scale decentralized plants, with relatively few possibilities of energy recovery. Innovation in areas such as process and product modeling, process intensification and process control enable the development of new manufacturing pathways, more efficient, versatile, selective and sustainable. Furthermore, the addition of Internet of Things elements and the way they connect to the physical process will ensure continuous communication between the various actors of the food value chain. This will enable the design of new compact, scalable, flexible, modular, and automated production equipment that will offer food manufacturers the ability to respond rapidly, economically, accurately and flexibly to the consumer demands.
Purpose Live non-invasive monitoring of biomarkers is of great importance for the medical community. Moreover, some studies suggest that there is a substantial business gap in the development of mass-production commercial sweat-analysing wearables with great revenue potential. The objective of this work is to quantify the concentration of biomarkers that reaches the area of the garment where a sensor is positioned to advance the development of commercial sweat-analysing garments. Design/methodology/approach Computational analysis of the microfluidic transport of biomarkers within eccrine sweat glands provides a powerful way to explore the potential for quantitative measurements of biomarkers that can be related to the health and/or the physical activity parameters of an individual. The numerical modelling of sweat glands and the interaction of sweat with a textile layer remain however rather unexplored. This work presents a simulation of the production of sweat in the eccrine gland, reabsorption from the dermal duct into the surrounding skin and diffusion within an overlying garment. Findings The model represents satisfactorily the relationship between the biomarker concentration and the flow rate of sweat. The biomarker distribution across an overlying garment has also been calculated and subsequently compared to the minimum amount detectable by a sensor previously reported in the literature. The model can thus be utilized to check whether or not a given sensor can detect the minimum biomarker concentration threshold accumulated on a particular type of garment. Originality/value The present work presents to the best of our knowledge, the earliest numerical models of the sweat gland carried out so far. The model describes the flow of human sweat along the sweat duct and on to an overlying piece of garment. The model considers complex phenomena, such as reabsorption of sweat into the skin layers surrounding the duct, and the structure of the fibres composing the garment. Biomarker concentration maps are obtained to check whether sensors can detect the threshold concentration that triggers an electric signal. This model finds application in the development of smart textiles.
Solar ponds are a promising technology to capture and store solar energy. Accurate, reliable and versatile models are thus needed to assess the thermal performance of salinity gradient solar ponds. A CFD simulation set-up has been developed in this work to obtain a fully versatile model applicable to any practical scenario. Also, a comparison between the results obtained with an existing one-dimensional MATLAB model and the two- and three-dimensional CFD models developed in this work has been carried out to quantify the gain in accuracy and the increase in computational resources needed. The two and three-dimensional models achieve considerably higher accuracy than the 1-D model. They are subsequently found to accurately evaluate the heat loss to the surroundings, the irradiance absorbed by the solar pond and the thermal performance of the pond throughout the year. Two geographic locations: Bafgh (Iran) and Kuwait City, have been evaluated.
Distributed Energy Systems (DES) are set to play a vital role in achieving emission targets and meeting higher global energy demand by 2050. However, implementing these systems has been challenging, particularly due to uncertainties in local energy demand and renewable energy generation, which imply uncertain operational costs. In this work we are implementing a Mixed-Integer Linear Programming (MILP) model for the operation of a DES, and analysing impacts of uncertainties in electricity demand, heating demand and solar irradiance on the main model output, the total daily operational cost, using Global Sensitivity Analysis (GSA). Representative data from a case study involving nine residential areas at the University of Surrey are used to test the model for the winter season. Distribution models for uncertain variables, obtained through statistical analysis of raw data, are presented. Design results show reduced costs and emissions, whilst GSA results show that heating demand has the largest influence on the variance of total daily operational cost. Challenges and design limitations are also discussed. Overall, the methodology can be easily applied to improve DES design and operation.
A comprehensive guide that offers a review of the current technologies that tackle CO2 emissionsThe race to reduce CO2 emissions continues to be an urgent global challenge. "Engineering Solutions for CO2 Conversion" offers a thorough guide to the most current technologies designed to mitigate CO2 emissions ranging from CO2 capture to CO2 utilization approaches....
This study investigates hydrogen production via a directly coupled solar‐PEM electrolysis system using aqueous methanol instead of water. The effect of load matching and methanol concentration on hydrogen production rates, electrolysis efficiency, and solar‐hydrogen efficiency was investigated. The electrolysis efficiencies were subsequently used in simulation studies to estimate production costs in scaled up systems. The results show that the added hydrogen production associated with the methanol solutions leads to favourable hydrogen production costs at smaller scales.
Synthesis and characterisation of n‐octacosane@silica nanocapsules for thermal storage applications
(2019)
This work reports the synthesis and characterisation of a core‐shell n‐octacosane@silica nanoencapsulated phase‐change material obtained via interfacial hydrolysis and polycondensation of tetraethyl orthosilicate in miniemulsion. Silica has been used as the encapsulating material because of its thermal advantages relative to synthesised polymers. The material presents excellent heat storage potential, with a measured melting latent heat varying between 57.1 and 89.0 kJ kg−1 (melting point between 58.2°C and 59.9°C) and a small particle size (between 565 and 227 nm). Degradation of the n‐octacosane core starts between 150°C and 180°C. Also, the use of silica as shell material gives way to a heat conductivity of 0.796 W m−1 K−1 (greater than that of nanoencapsulated materials with polymeric shell). Charge/discharge cycles have been successfully simulated at low pressure to prove the suitability of the nanopowder as phase‐change material. Further research will be carried out in the future regarding the use of the synthesised material in thermal applications involving nanofluids.