Abstract

Low-grade waste heat driven AR technology is considered a promising strategy to realize near-zero-carbon cooling applications. The HEN plays a critical role in the chemical process industry owing to its significant impact in the energy recovery. For abundant industrial waste heat, an integrated HEN-AR superstructure combining microscopic adsorption capacity prediction, mesoscopic AR unit and waste heat recovery system is proposed to satisfy specific refrigeration requirements. A MINLP model is formulated, and the objective function is to minimize TAC. In this paper, simultaneous optimization and screening of integrated HEN-AR system for 11 zeolite/NH3 working pairs in a practical industrial case is investigated. 6 working pairs have the ability to achieve the cooling capacity of 1350 kW at 287 K. A new power-based coefficient of performance for cooling 〖COP〗_C^P is innovatively proposed with power consumption replacing free low-grade waste heat. The optimal IRN/NH3 working pair is identified, with the highest COPc, COPpc, SCP. Configuration and thermodynamic analysis for the HEN-AR system with superior IRN/NH3 working pair are comprehensively elaborated. The integrated HEN-AR model offers bright insights for low-grade waste heat adsorption refrigeration applications.

Abstract

Efficiently solving large-scale heat exchanger networks (HENs) remains a changing task due to the non-convex and nonlinearity characteristics. A novel methodology for optimal design of HEN combined structure design model based on pinch analysis and automatically converted the structure into associating topology in the superstructure is developed, subsequently as the initial point to optimize the superstructure-based model. Various InHEN structures obtained by changing ∆Tmin, and results of initial HEN (InHEN) model satisfy theoretical utility. The conversion model uses a temperature comparison-based method to locate the exchanger position, and distance-based method proposed in this study achieve updating the location. The reduced MINLP to NLP model through the strategy of replacing binary variables with absolute values in the superstructure is presented. A weakening strategy is proposed to simplify the computation of temperature differences. The TAC as the objective is then solved with various initial points, and the near globally optimal solution is obtained. The framework of optimization with initial points is named PinNLP model. The complex large-scale case is investigated for proving the method’s applicability and advantages in solving quality and time, and the results show that the optimal is close to the global optimal solution. The proposed methodology can be applied to industrial HEN synthesis and thus has a wide space of applications.

Abstract

A hybrid CO2-surfactant thermal system can efficiently and eco-friendly improve the oil recovery rate for post-steam heavy oil reservoirs by modifying heavy oil characteristics. However, the complex conditions of high-temperature and high-pressure reservoirs hinder experimental investigations into the microscopic mechanisms of this system. In this study, Molecular Dynamics (MD) simulations reveal the molecular interaction mechanism between a hybrid thermal system, heavy oil, and pore surfaces. The hybrid thermal systems comprise CO2 and a cost-effective SDS (sodium dodecyl sulfate) surfactant. The effects of surface wettability, external temperature, and CO2 concentration on the transition of heavy oil microstructure are investigated.
Results show that the hybrid CO2-surfactant systems can effectively improve the microstructure of heavy oil by promoting the thermal expansion process. Moreover, surface wettability and CO2 concentration significantly affect the microstructure of heavy oil. Specifically, the thermal expansion of heavy oil is suppressed on hydrophobic surfaces compared to hydrophilic surfaces. This is because an oil-surface interaction promotes the formation of dense clusters of asphaltenes within a heavy oil layer, making the heavy oil on hydrophobic surfaces more challenging to recover. Meanwhile, the concentration of CO2 determines the state of a hybrid thermal system and ultimately affects the distribution of heavy oil. The hybrid thermal system with moderate dynamic activity and a high effective distribution ratio of CO2 can efficiently improve the microstructure of heavy oil for recovery and demonstrate the potential of CO2 storage. Furthermore, an optimum CO2 concentration of 10 wt.% is recommended for designing the hybrid thermal system.
This study provides insights into the molecular transition mechanism of heavy oil in various hybrid thermal systems. It offers valuable theoretical guidance for designing efficient and eco-friendly heavy oil recovery operations to support the transition towards carbon neutrality.

Abstract

Electrochromic smart windows represent advanced adjustable envelope technology, where the solar radiation transmittance can be dynamically adjusted in response to changes in the applied voltage, thus demonstrating superior energy-saving potential. In this paper, a cell room office located in a multi-story office building was constructed in DesignBuilder to explore the effects of window-to-wall ratio, outdoor temperature-based control strategy, and spectral modulation characteristics on the energy performance of electrochromic smart windows. The conclusions show that for Helsinki, Beijing, and Miami, electrochromic windows with visible modulation spectral characteristics are more energy efficient compared to near-infrared modulation spectra, and this phenomenon is more pronounced with larger window-to-wall ratios. Selecting a suitable control strategy can make the electrochromic window adapt to change the transmission state with the change of weather conditions and achieve the best energy saving effect. The energy-saving effect of triggering the electrochromic window state change at 15℃ is better than that at 27℃. It is hoped that this paper will enable electrochromic windows to contribute in a small way to the field of effective solar energy utilization and solar thermal management and to promote the development of smart window technology.

Abstract

Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.

Abstract

To improve the compatibility between expanded graphite (EG) and glycine water-based phase change material (GWPCM), different concentrations of modified expanded graphite (MEG) were prepared by using surfactant of triton x-100 (TX-100) as a modifier. The adsorption capacity of EG and MEG was implemented in detail. Composite GWPCM by using MEG as a porous material was prepared. The thermophysical properties of the prepared MEG/GWPCM were evaluated. The results showed that MEG-0.15 exhibited the best compatibility with GWPCM, and the adsorption capacity increased to 82.87 %. When the addition amount of MEG was 16 wt%, the MEG/GWPCM possessed a suitable melting temperature (-5.75 °C) and a high latent heat (250.77 J/g). Notably, the thermal conductivity was 2.4523 W/(m·K), which was 4.08 folds of GWPCM. The prepared composite materials showed great potential in the low-temperature cold energy storage of photovoltaic refrigeration systems.

Abstract

This study investigates the benefits and limitations of small-scale biogas technology at the household level in rural Sub-Saharan Africa. A literature study and a case study were done to explore if small-scale biogas production is a viable source of electricity in rural Sub-Saharan Africa. The results show that using cattle manure as feedstock requires a daily substrate flow of 750 L of the diluted substrate, i.e. 250 kg of manure and 500 L of water for a 24-hour electricity supply, using a 2-kW generator. This requires a minimum herd size of 25 cattle. Most households don’t have so many cattle. However, a herd of 10 cattle provides enough biogas to power several electrical appliances, significantly improving the household’s energy situation. The study concludes that the uptake of biogas technology in Sub-Saharan Africa is slow. Common barriers include inadequate substrate supply, lack of water and variable temperatures, high initial costs, poor technical quality, intense labour operations and maintenance, and insufficient policy support. Improved uptake of biogas technology in Sub-Saharan Africa requires establishing national institutional frameworks and supporting policies, collaboration with the intended users of the technology and local support organisations, ensuring long-term local availability of spare parts and supplies, and, when household-level access to feedstock is limited, centralise biogas systems on the village level to combine feedstock into one production system.

Abstract

Low heating load areas require intermittent heating to create a comfortable indoor environment quickly and efficiently, and the indoor thermal environment depends strongly on the choice of indoor terminal units. In this paper, the thermal performances of traditional fan coil and floor heating are analyzed. It shows that the radiant heat transfer shows good thermal comfort, while convective heat transfer shows higher intermittency. Therefore, adding switchable convective heating to radiant heating is an effective improving means. On this basis, a novel terminal based on flat heat pipes is proposed, which can switch flexibly between radiation and convection modes and its thermal performance is analyzed. Furthermore, a comparative experiment between the novel terminal based on flat heat pipes and traditional terminals is conducted. The results show that the effect of the switchable mode of the novel terminal is equivalent to the combined effect of the traditional terminals at the key points of heating capacity and temperature rise. In addition, this novel terminal can initially heat up the space within 20-40 minutes and then stabilize the room temperature in a comfortable range of 18-22 °C. In areas of low heating load, this novel terminal can replace two traditional terminals, thereby reducing system complexity and initial investment.

Abstract

Distributed energy supply involving solar energy is a promising way to improve sustainable development. However, the challenges of high energy consumption for CO2 capture and low solar share in traditional solar natural gas reforming or coal gasification polygeneration still need to be overcome. Here, we proposed a novel solar carbon-neutral polygeneration system integrated with solar chemical looping dry reforming, PV-SOEC and distributed energy supply, characterized by enhanced solar share, low carbon capture energy consumption and high efficiency. The used solar chemical looping dry reforming not only obtains automatic separation products of CO and H2, but upgrade solar energy into chemical energy. The obtained CO combined with the by-product of PV-SOEC (O2) is then used for generation on the way of pure oxygen combustion. The exhausted CO2 is cascaded utilized through an absorption refrigeration and heat exchange, and then captured with no extra energy consumption. Through a simulation model, we find that the overall system efficiency can reach 63.7% with a solar share of 44.2%. The solar-to-chemical efficiency of the solar chemical looping dry reforming unit is 63.9%. This work offers a promising solution for the integration and construction of a high-efficiency, high-solar-share energy system.

Abstract

This comprehensive data science analysis of the IEA’s hydrogen projects database from 2000-2022 reveals accelerating green hydrogen project growth. The descriptive analysis exposes increasing project counts, total capacity, and grid integration over time and by geography. Key countries leading in projects and capacities are Germany, Australia, the USA, France, Spain, and China. Transportation, chemicals, and refining dominate end-use applications. Solar, wind, and hydro lead renewable feedstocks, with over 50% of future capacity from novel electrolysis methods, like SOEC and other electrolysis technologies. Germany’s current and future projects are electrolysis-based, unlike the USA which has a more diversified approach. Clustering uncovers project typologies centered on technology, status, location, and end-use. Sophisticated deep learning forecasting with transformer and RNN models predicts massive future growth expansion (MAE 10.19, MSE 457.74, R2 0.558). The rigorous methodological approach provides unprecedented insights into the swift expansion and budding contours of the global hydrogen sector. These data-driven models unpack project growth dynamics, offering intelligence for policy, research, and industry to strategically harness the bourgeoning hydrogen economy. Overall, this study thoroughly probes the mechanics of green hydrogen project development through ampliative modeling of the most extensive database available.