Abstract

In 2020, China made an official announcement of reducing CO2 emissions and achieving carbon neutrality by 2060. Beyond the energy-related emissions, the steel and cement industries are typically fossil fuel-dependent and serve as the main contributors of CO2 emissions, which renders achieving carbon neutrality of particular challenges. These industries are highly energy-demanding, while only a few viable low-carbon solutions that would play a significant role during the transition from fossil fuels to renewable energies are commercially available. Herein, this study mainly focuses on the mitigation potential of low-carbon and carbon-negative technologies for these industries. An overall systematic optimizing model containing the above four industrial departments in China has been established, taking their regional demand, cost, GHG emissions, low-carbon strategies, etc. into consideration. According to the model, the most cost-effective solution supporting carbon neutrality by forming a combination of renewable energy, low-carbon and carbon-negative technologies, and energy-intensive sectors has been proposed and reasonably identified.

Abstract

We use a bottom-up prospective model to explore how the iron and steel industry can decarbonize its activity by combining bioenergy with carbon capture and storage technologies.

Abstract

Electricity sector is a main contributor of greenhouse gas (GHG), NOx, SO2 and PM2.5 and related environmental impact in China. This research conducted a critical review of 36 studies containing 51 cases involving 10 electricity generation technologies in China to analyze the ranges of emission data for GHG, NOx, SO2 and PM2.5, summarize the current LCA methodologies and the key indicators like impact categories and temporal scale. The results showed it’s incomplete to measure the environmental performance of an electricity generation technology solely based on GHG emission. Emissions data were set and evaluated at different life cycle stages for different electricity generation technologies. Emissions from fuel combustion contributed the majority of the life cycle emissions for fossil fuel power, up to 90% for GHG, 70% for SO2, similarly, biomass fuel provision accounted the largest share of emissions for biomass technologies, up to 85% for GHG. Emission and environmental impacts from plant infrastructure contributed the largest share for renewable energies. And the emission of end-of-life treatment from renewable energies can offset considerable life cycle emissions. The results from this review provided a concrete and balanced basis for further LCA modelling in the China’s whole electricity sector.

Abstract

End-users are transiting towards more active, integrating new low-carbon (LC) technologies and bringing unpredictability to low-voltage (LV) distribution networks. Although smart meters have a great potential in increasing the observability, they are mostly being employed only for billing purposes, leaving many other possibilities unexploited, further complicating the many analyses required for effective operational planning and real-time (RT) operation. Detection of phase consumption of end-users is significantly difficult, due to the nonlinear relationships between obtained phase voltage measurements and aggregated end-user consumption. Machine learning (ML) is increasingly used for these and similar problems, and therefore, in this paper, a neural network (NN) – based model is developed to detect end-user consumption in an LV distribution network from available voltage measurements and aggregated end-user consumption. Furthermore, the influence of topology on the output values of the model is investigated and a graph neural network (GNN) – based model is created that considers both the structure and data of the distribution network elements. Both models are tested on the real-world LV distribution network with more than 150 end-users. The results showed the effectiveness of both models in determining the distribution of end-user consumption, with the GNN-based model showing significantly better results. Such a model can help the energy utilities to overcome this time-consuming problem and lay a good foundation for further analyzes required to enable operation and planning of distribution networks.

Abstract

Vigorous studies have been conducted for effective reduction of carbon dioxide (CO2) and other greenhouse gases from anthropogenic sources as primary driver of climate change. Among a portfolio of technologies for CO2 capture, storage and monitoring, microencapsulation of carbon solvents (MECS) has become promising and gained increasing attention in recent decade, as it can minimize the leakage of the solvent and enhance mass transfer with an increased surface area to volume ratio. In particular, encapsulation of carbon solvents via polymeric shells also circumvents the issues that carbon solvents normally encounter, such as high viscosity or corrosivity, by offering a protective layer between the solvents and absorption column. Although there has been recent progress towards microencapsulation of carbon solvents, the realization of which has focused mainly on use of polydimethylsiloxane (PDMS) devices which necessitate the use of clean room or glass capillary devices which are inherently difficult to align while fabrication. In this work, an off-shelf needle based microfluidic system has been established to form microcapsules with monoethanolamine (MEA) encapsulated in the silicone polymer shell made by TEGO RAD 2650 for carbon capture. The low-cost and facile method offers an exquisite control over the size, shape and inner structure of the microcapsules. MECS of MEA has higher absorption CO2 rate than its neat solvent by at least a fold. Beyond that, incorporating graphene nanoplatelet (GNP) into the core solvent yields MECS of MEA with GNP, showing improved CO2 uptake capacity by 10 and 60% when absorption was carried out at 25 and 60°C, respectively. Therefore, the off-shelf droplet-based microfluidics opens up a new avenue for ease access to versatile fabrication of microencapsulated carbon sorbents with well-tailored properties in more cost-effective way, thus benefitting their pragmatic applications for carbon capture and utilization.

Abstract

Novel large-sized nanofilm-constructed hierarchical porous SiO2 (macropore size of 0.5–1.0 μm, wall thickness of 40–50 nm) was prepared by a dual- templating approach, and used as the advanced support for g-C3N4 photocatalyst. The photocatalytic activities were evaluated by degradations of Rhodamine B (RhB) under the simulated sunlight irradiation and experiments were conducted at wide ranges of the g- C3N4 loading content, solution pH, and photocatalyst dosage. The combination of increased light absorption, improved electron–hole separation and adsorption capacity, endows this fine structure with excellent photocatalytic performance. The trapping experiments were also performed to identify the main reactive species in the catalytic reactions.

Abstract

Microwave (MW) irradiation is an attractive option for process intensification and has been proved effective in many catalytic processes. In this work, the (CNTs-Fe3O4)-Co nanocomposites were carefully design and successfully fabricated under a controlled manner. It was found that for the (CNTs-Fe3O4)(1:4)-Co (10 wt%) catalyst, in the temperature range of 40 to 60°C, MW heating led to the hydrogen generation rate (HGR) being improved by 75 to 95.4% as compared with conventional heating. The synergistic effect of cobalt and Fe3O4 sites of the (CNTs-Fe3O4)(1:4)-Co (10 wt%) catalyst was also verified by the evaluation of the catalytic performance of the (CNTs-Fe3O4) catalysts with different Co loading and DFT calculations. Moreover, the pre-exponential factor (A) of the NaBH4 hydrolysis catalyzed by the (CNTs-Fe3O4)(1:4)-Co (10 wt%) catalyst under MW heating was approximately 15 times higher than that of conventional heating, indicating that the effective collision frequency of the atoms at the reaction interface of the catalyst under MW heating was much higher than that of conventional heating because of the higher active sites on the surface of (CNTs-Fe3O4)(1:4)-Co (10 wt%) catalyst under MW irradiation. Furthermore, the designed continuous experiment with microwave intermittent off-on (5 min as a reaction cycle) was carried out at 26± 0.3°C controlled by a continuous thermostatic water bath, and the results proved that both thermal and non- thermal effects of MW heating contributed to the enhancement of HGR under MW heating.

Abstract

The concentrated parabolic trough solar power is regarded as a promising renewable alternative expected to exert a vital role in supplying green and sustainable electricity and thermal energy for the human race in the future. However, the parabolic trough collector (PTC) system still encounters a knotty problem of considerable radiation heat loss caused by its high operating temperature (generally above 400 °C), which significantly degrades the thermal performance of the PTC system and thus power generation performance of the PTC power plant. In this work, a novel PTC system integrated with solar photovoltaics (PTC-PV) is proposed. The PV panels have a narrow width which is the same as the diameter of the parabolic trough receiver (PTR), and are placed above the PTRs at a certain distance. In addition, the rear surfaces of the PV panels are covered with a high-reflective coating used to block the radiation heat loss from the PTR. The unique configuration of PV panels in the PTC-PV system is expected to reduce the radiation heat loss of PTRs and simultaneously generate electricity in an efficient manner due to the sun tracking mechanism of the PTC system. In this research, a test rig of the proposed PTC-PV system was established in the Hong Kong Polytechnic University. The comprehensive experiments were carried out. In the experiments, PV efficiency under different PTR’s operating temperatures and solar irradiances were explored. Moreover, the overall performance of the PTC-PV system was also evaluated and compared with the traditional PTC system. The experimental results showed that the PV panels in the PTC-PV system maintained superior photoelectric efficiency compared to the traditional PV system without sun tracking. The high-reflective coating covered on the rear surface of the PV panel can effectively reduce the PV temperature when the temperature of the glass tube in the PTR is above 75 °C. And the electricity capacity of the PTC-PV based concentrated solar power plant is effectively improved by 1.1 % compared to the traditional PTC based concentrated solar power plant.

Abstract

The forecasting of photovoltaic (PV) power generation and inelastic load is of great significance for the stable and efficient power supply of a microgrid power system. However, most of the PV prediction research in literature is based on known solar radiation which is difficult to obtained. In order to relieve the uncertainty in a microgrid, this work proposes full-process forecasting methods based on solar energy and load periodic characteristics analysis. For solar energy, a combination of Gaussian process regression (GPR) and physical model methods is utilized for the short-term accurate forecasting. The long-term trend forecasting is realized based on a cascade online TS fuzzy model. For inelastic load, an improved online long-short term memory (LSTM) rolling forecasting method is proposed. Simulation results show that the GPR & physical model methods can reach or even exceed the existing accuracy, while the cascade online TS fuzzy model method can achieve 5.5% higher accuracy than existing algorithms. Compared with the current offline LSTM method, the accuracy of the online method can be improved by up to 4.92%.

Abstract

Insight into the development of the next generation concentrated solar power plant (CSP), molten salts are the most promising candidate in storing the thermal energy, although it still suffers from low energy storage density and thermal conductivity. Recently, various nano-additives were verified to have the potential in promoting the thermal properties of molten salts. In this work, 50nm MgO nanoparticles with a mass fraction of 1% to 10% are dispersed into the LiNO3/NaCl eutectic, and the thermal properties consisting of thermal conductivity, specific heat capacity (Cp), phase change enthalpy, and viscosity are evaluated by experiments. Compared with many other ceramic nanoparticles, only the MgO dispersed nanomaterial obtains both enhancement in solidus thermal conductivity and Cp, which are improved up to 63.5% and 28% at 4wt.% MgO, respectively. On the contrary, the phase change enthalpy and viscosity are developed toward the negative direction, but within the acceptable limit. Combining the above results, molten salts with nanoparticles added are still desirable candidates to meet the demand of the next generation CSP.